AiATrack: Attention in Attention for Transformer Visual Tracking

Transformer trackers have achieved impressive advancements recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement. To address this issue, we propose an attention in attention (AiA) module, which enhances appropriate correlations and suppresses erroneous ones by seeking consensus among all correlation vectors. Our AiA module can be readily applied to both self-attention blocks and cross-attention blocks to facilitate feature aggregation and information propagation for visual tracking. Moreover, we propose a streamlined Transformer tracking framework, dubbed AiATrack, by introducing efficient feature reuse and target-background embeddings to make full use of temporal references. Experiments show that our tracker achieves state-of-the-art performance on six tracking benchmarks while running at a real-time speed.Comment: Accepted by ECCV 2022. Code and models are publicly available at https://github.com/Little-Podi/AiATrac

Single and combined strategies for mesenchymal stem cell exosomes alleviate liver fibrosis: a systematic review and meta-analysis of preclinical animal models

Background: The efficacy of mesenchymal stem cells (MSCs) in treating liver fibrosis has been supported by various clinical studies. However, stem cell transplantation is limited in clinical application due to its low survival rate, low liver implantation rate, and possible carcinogenicity. Recently, there has been increasing interest in the use of MSC-exos due to their widespread availability, low immunogenicity, and non-carcinogenic properties. Numerous studies have demonstrated the potential of MSC-exos in treating liver fibrosis and preventing progression to end-stage liver disease.Objective: This study aimed to systematically investigate the efficacy of MSC-exos single administration in the treatment of hepatic fibrosis and the combined advantages of MSC-exos in combination with drug therapy (MSC-exos-drugs).Methods: Data sources included PubMed, Web of Science, Embase, and the Cochrane Library, which were built up to January 2024. The population, intervention, comparison, outcomes, and study design (PICOS) principle was used to screen the literature, and the quality of the literature was evaluated to assess the risk of bias. Finally, the data from each study’s outcome indicators were extracted for a combined analysis.Results: After screening, a total of 18 papers (19 studies) were included, of which 12 involved MSC-exos single administration for the treatment of liver fibrosis and 6 involved MSC-exos-drugs for the treatment of liver fibrosis. Pooled analysis revealed that MSC-exos significantly improved liver function, promoted the repair of damaged liver tissue, and slowed the progression of hepatic fibrosis and that MSC-exos-drugs were more efficacious than MSC-exos single administration. Subgroup analyses revealed that the use of AD-MSC-exos resulted in more consistent and significant efficacy when MSC-exos was used to treat hepatic fibrosis. For MSC-exos-drugs, a more stable end result is obtained by kit extraction. Similarly, infusion through the abdominal cavity is more effective.Conclusion: The results suggest that MSC-exos can effectively treat liver fibrosis and that MSC-exos-drugs are more effective than MSC-exos single administration. Although the results of the subgroup analyses provide recommendations for clinical treatment, a large number of high-quality experimental validations are still needed.Systematic Review Registration: CRD42024516199

NOVEL NANOSTRUCTURED HIGH-PERFORMANCE ANION EXCHANGE IONOMERS FOR ANION EXCHANGE MEMBRANE FUEL CELLS

poster abstractA novel block copolymer, styrene-ethylene/butylene-styrene (SEBS), was chosen as the starting material to prepare pendant quaternary ammonium-based ionomers with an ion-exchange-capacity (IEC) of 0.66, 1.30, and 1.54 meq g-1, denoted by QSEBS-L, QSEBS-M, and QSEBS-H, respectively. These QSEBS ionomers were demonstrated to have excellent dimensional stability against hydration without significantly sacrificing the ionic conductivity as compared to the widely studied polysulfone (PSf) based ionomers. The water uptake of the QSEBS-based ionomers depended on their functionality; a higher IEC in the ionomer resulted in more water uptake and a higher ionic conductivity. The MEAs fabricated with the QSEBS-M and QSEBS-H ionomers showed the best H2/O2 fuel cell performance with peak power densities reaching 210 mW cm-2 at 50 °C, which was significantly higher than that of the PSf-based ionomers (~30 mW cm-2). Electrochemical impedance spec-troscopy (EIS) analysis indicated that the superior fuel cell performance ob-served with the QSEBS-based ionomers can be attributed to: (1) the low in-ternal cell resistance due to good comparability of the QSEBS-based ionomers with the membranes and (2) the low mass transport and charge transport in both the anode and the cathode due to the excellent dimension-al stability and balanced conductivity-hydrophobicity originated by the unique morphology of the QSEBS-based ionomers. AFM phase imaging measurements of the QSEBS-based ionomers revealed unique nanostruc-tures containing isolated hydrophobic and continuous anion conducting hy-drophilic domains. By further optimizing the chemistry and morphology of the ionomers and the membranes, the resistance of the anode and cathode of the AEMFCs will be further reduced

Numerical calculation of oil film for ship stern bearing based on matrix method

Radial sliding bearings are widely used in ship shafting, its characteristics of lubricating oil film have important influence on the normal operation of the whole shaft system. In this work, the difference equations which is used to calculate the radial sliding bearing oil film features is transformed into matrix equations, the solving process be converted into solving matrix equation, combined with the powerful matrix calculation function of MATLAB, the solution process is simplified. It is not necessary to set the error precision and relaxation factor, so as to avoid the problem that the calculation result is not stable or even not convergent in the process of Successive Over Relaxation(SOR) method, and the calculation precision and stability are improved. The numerical results of matrix calculation method is compared with the result of SOR method, verified the correctness and feasibility of the matrix calculation method. Because the calculation is relatively stable, the matrix calculation method is more suitable for the calculation core of the relative computing software

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.Comment: AAAI 202