Less is More: Focus Attention for Efficient DETR

DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discrimination brings a redundant computational burden in the traditional encoder structure. The recent sparsification strategies exploit a subset of informative tokens to reduce attention complexity maintaining performance through the sparse encoder. But these methods tend to rely on unreliable model statistics. Moreover, simply reducing the token population hinders the detection performance to a large extent, limiting the application of these sparse models. We propose Focus-DETR, which focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Specifically, we reconstruct the encoder with dual attention, which includes a token scoring mechanism that considers both localization and category semantic information of the objects from multi-scale feature maps. We efficiently abandon the background queries and enhance the semantic interaction of the fine-grained object queries based on the scores. Compared with the state-of-the-art sparse DETR-like detectors under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO. The code is available at https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR and https://gitee.com/mindspore/models/tree/master/research/cv/Focus-DETR.Comment: 8 pages, 6 figures, accepted to ICCV202

Optimization of microstructure design for enhanced sensing performance in flexible piezoresistive sensors

Flexible piezoresistive strain sensors have received significant attention due to their diverse applications in monitoring human activities and health, as well as in robotics, prosthetics, and human–computer interaction interfaces. Among the various flexible sensor types, those with microstructure designs are considered promising for strain sensing due to their simple structure, high sensitivity, extensive operational range, rapid response time, and robust stability. This review provides a concise overview of recent advancements in flexible piezoresistive sensors based on microstructure design for enhanced strain sensing performance, including the impact of microstructure on sensing mechanisms, classification of microstructure designs, fabrication methods, and practical applications. Initially, this review delves into the analysis of piezoresistive sensor sensing mechanisms and performance parameters, exploring the relationship between microstructure design and performance enhancement. Subsequently, an in-depth discussion is presented, focusing on the primary themes of microstructure design classification, process selection, performance characteristics, and specific applications. This review employs mathematical modeling and hierarchical analysis to emphasize the directionality of different microstructures on performance enhancement and to highlight the performance advantages and applicable features of various microstructure types. In conclusion, this review examines the multifunctionality of flexible piezoresistive sensors based on microstructure design and addresses the challenges that still need to be overcome and improved, such as achieving a wide range of stretchability, high sensitivity, and robust stability. This review summarizes the research directions for enhancing sensing performance through microstructure design, aiming to assist in the advancement of flexible piezoresistive sensors

Development and validation of a sensitive enzyme-linked immunosorbent assay for clonidine hydrochloride in pig urine and pork samples

Clonidine hydrochloride (CLO) is a new substitute for a traditionally used adrenergic agonist. The illegal use of CLO in the livestock industry possess potential harm to human health. Hence, it is an urgent need for the rapid detection of CLO residues. Here, we prepared a highly sensitive and specific monoclonal antibody (mAb) and it used to develop an indirect competitive ELISA (ic-ELISA) for the rapid screening of CLO residues. The limit of detection and limit of quantification values of ic-ELISA were as follows: 0.033 and 0.054 ng/mL for pig urine and 0.061 and 0.096 ng/mL for pork, respectively. Recovery experiment indicated that the ic-ELISA posed outstanding accuracy and precision. Furthermore, the results of ic-ELISA were strongly correlated to the results of HPLC. Thus, the ic-ELISA provided a sensitive and rapid on-site detection of CLO residues in pig urine and pork samples

Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic Lake Donghu (Wuhan, China).

Uncovering which environmental factors govern community diversity patterns and how ecological processes drive community turnover are key questions related to understand the community assembly. However, the ecological mechanisms regulating long-term variations of bacterioplankton communities in lake ecosystems remain poorly understood. Here we present nearly a decade-long study of bacterioplankton communities from the eutrophic Lake Donghu (Wuhan, China) using 16S rRNA gene amplicon sequencing with MiSeq platform. We found strong repeatable seasonal diversity patterns in terms of both common (detected in more than 50% samples) and dominant (relative abundance >1%) bacterial taxa turnover. Moreover, community composition tracked the seasonal temperature gradient, indicating that temperature is a key environmental factor controlling observed diversity patterns. Total phosphorus also contributed significantly to the seasonal shifts in bacterioplankton composition. However, any spatial pattern of bacterioplankton communities across the main lake areas within season was overwhelmed by their temporal variabilities. Phylogenetic analysis further indicated that 75%-82% of community turnover was governed by homogeneous selection due to consistent environmental conditions within seasons, suggesting that the microbial communities in Lake Donghu are mainly controlled by niche-based processes. Therefore, dominant niches available within seasons might be occupied by similar combinations of bacterial taxa with modest dispersal rates throughout different lake areas

SMYD5 Is a Ribosomal Methyltransferase That Catalyzes RPL40 Lysine Methylation To Enhance Translation Output and Promote Hepatocellular Carcinoma

While lysine methylation is well-known for regulating gene expression transcriptionally, its implications in translation have been largely uncharted. Trimethylation at lysine 22 (K22me3) on RPL40, a core ribosomal protein located in the GTPase activation center, was first reported 27 years ago. Yet, its methyltransferase and role in translation remain unexplored. Here, we report that SMYD5 has robust in vitro activity toward RPL40 K22 and primarily catalyzes RPL40 K22me3 in cells. The loss of SMYD5 and RPL40 K22me3 leads to reduced translation output and disturbed elongation as evidenced by increased ribosome collisions. SMYD5 and RPL40 K22me3 are upregulated in hepatocellular carcinoma (HCC) and negatively correlated with patient prognosis. Depleting SMYD5 renders HCC cells hypersensitive to mTOR inhibition in both 2D and 3D cultures. Additionally, the loss of SMYD5 markedly inhibits HCC development and growth in both genetically engineered mouse and patient-derived xenograft (PDX) models, with the inhibitory effect in the PDX model further enhanced by concurrent mTOR suppression. Our findings reveal a novel role of the SMYD5 and RPL40 K22me3 axis in translation elongation and highlight the therapeutic potential of targeting SMYD5 in HCC, particularly with concurrent mTOR inhibition. This work also conceptually broadens the understanding of lysine methylation, extending its significance from transcriptional regulation to translational control

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Distributed Fire Detection and Localization Model Using Federated Learning

Fire detection and monitoring systems based on machine vision have been gradually developed in recent years. Traditional centralized deep learning model training methods transfer large amounts of video image data to the cloud, making image data privacy and confidentiality difficult. In order to protect the data privacy in the fire detection system with heterogeneous data and to enhance its efficiency, this paper proposes an improved federated learning algorithm incorporating computer vision: FedVIS, which uses a federated dropout and gradient selection algorithm to reduce communication overhead, and uses a transformer to replace a traditional neural network to improve the robustness of federated learning in the context of heterogeneous data. FedVIS can reduce the communication overhead in addition to reducing the catastrophic forgetting of previous devices, improving convergence, and producing superior global models. In this paper’s experimental results, FedVIS outperforms the common federated learning methods FedSGD, FedAVG, FedAWS, and CMFL, and improves the detection effect by reducing communication costs. As the amount of clients increases, the accuracy of other algorithmic models decreases by 2–5%, and the number of communication rounds required increases significantly; meanwhile, our method maintains a superior detection performance while requiring roughly the same number of communication rounds

Electrochemical synthesis of co-rich nanowires for barcodes

We synthesized three types of magnetic nanowires (Co-Ni-P, Co-Pt-P, and Co-Fe-P) by electrochemical deposition in polycarbonate membranes for use in magnetic barcodes. The nanowires were about 50 nm in diameter and 6 μm in length. The Co-Pt-P nanowires had the highest coercivity and remanence. We used finite elements to calculate the spatial distribution of the stray magnetic fields produced by the barcodes. The Co-Pt-P had the greatest spatial variation which makes it the best composition for the hard magnetic segment of barcode nanowires. These barcode nanowires may be used for magnetic multiplexing detection. © 2010-2012 IEEE.1