Heavy Bino and Slepton for Muon g-2 Anomaly

In light of very recent E989 experimental result, we investigate the possibility that heavy sparticles explain the muon g-2 anomaly. We focus on the bino-smuon loop in an effective SUSY scenario, where a light gravitino plays the role of dark matter and other sparticles are heavy. Due to the enhancement of left-right mixing of smuons by heavy higgsinos, the contribution of bino-smuon loop can sizably increase the prediction of muon g-2 to the experimental value. Under collider and vacuum stability constraints, we find that TeV scale bino and smuon can still account for the new muon g-2 anomaly. The implications for LHC phenomenology are also discussed.Comment: 10 pages,1 figure;Published in:Nucl.Phys.B 969(2021)115481,add some discussions and references, matches published versio

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectivel

Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models

Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs). However, the utilization of multiple concept LoRAs to jointly support multiple customized concepts presents a challenge. We refer to this scenario as decentralized multi-concept customization, which involves single-client concept tuning and center-node concept fusion. In this paper, we propose a new framework called Mix-of-Show that addresses the challenges of decentralized multi-concept customization, including concept conflicts resulting from existing single-client LoRA tuning and identity loss during model fusion. Mix-of-Show adopts an embedding-decomposed LoRA (ED-LoRA) for single-client tuning and gradient fusion for the center node to preserve the in-domain essence of single concepts and support theoretically limitless concept fusion. Additionally, we introduce regionally controllable sampling, which extends spatially controllable sampling (e.g., ControlNet and T2I-Adaptor) to address attribute binding and missing object problems in multi-concept sampling. Extensive experiments demonstrate that Mix-of-Show is capable of composing multiple customized concepts with high fidelity, including characters, objects, and scenes

Fast Metabolic Response to Drug Intervention Through Analysis on a Miniaturized, Highly Integrated Molecular Imaging System

We report on a radiopharmaceutical imaging platform designed to capture the kinetics of cellular responses to drugs. Methods: A portable in vitro molecular imaging system comprising a microchip and a β-particle imaging camera permitted routine cell-based radioassays of small numbers of either suspended or adherent cells. We investigated the kinetics of responses of model lymphoma and glioblastoma cancer cell lines to ^(18)F-FDG uptake after drug exposure. Those responses were correlated with kinetic changes in the cell cycle or with changes in receptor tyrosine kinase signaling. Results: The platform enabled direct radioassays of multiple cell types and yielded results comparable to those from conventional approaches; however, the platform used smaller sample sizes, permitted a higher level of quantitation, and did not require cell lysis. Conclusion: The kinetic analysis enabled by the platform provided a rapid (∼1 h) drug screening assay

Evaluation of the in vitro antioxidant and antitumor activity of hydroalcoholic extract from Jatropha mollissima leaves in Wistar rats

Introduction: Despite modern sciences and advancements in new drugs or chemicals, the new era now rushes natural remedies for various illnesses and diseases that lead to end organ damage. In this study, we investigated Jatropha mollissima ethanolic extract’s effect against doxorubicin-induced cardiotoxicity and renal toxicity.Methods: To determine phytochemicals, a phytochemical screening was conducted. Various assays were used to measure the antioxidant activity, including the DPPH (2,2-diphenylpicrylhydrazyl), SOD (superoxide dismutase), NO (nitric oxide), and others. The antiproliferative effect of Jm was assessed by MTT assay; morphological analysis was performed using an inverted and phase contrast microscope, ultra morphological analysis of apoptosis with acridine orange (AO)/propidium iodide (PI) staining.Results: It was seen that doxorubicin caused elevated serum markers and abnormal changes in histological patterns. The significant reduction in cardiac and renal marker levels seen in groups given either 400 or 600 mg/kg of crude extract demonstrates that Jm has a protective effect against doxorubicin-induced cardiotoxicity due to the presence of active phytoconstituents having antioxidant potential. There is a dose-dependent decrease in cell viability when using J. mollissima. Apoptosis was observed in the treated cells.Conclusion: In conclusion, our research lends credence to the idea that J. mollissima could be used for cancer management and have cardioprotective and nephroprotective effects