Multistage iterative methods for symmetric positive definite matrices

In this paper a multistage iterative method for solving the symmetric positive definite linear systems is established and the convergence of the method is proved. A numerical example is given to illustrate the effectiveness of our method. The method is especially suitable for parallel computation, and can be viewed as a extension of the classical iterative method or as a preconditioner for the conjugate gradient method.Fundação para a Ciência e a Tecnologia (FCT) and National Natural Science Foundation of China under Grant No. 1077102

Deeply Coupled Cross-Modal Prompt Learning

Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models to downstream tasks has gained significant attention recently. Existing prompt-tuning methods in cross-modal learning, however, either solely focus on language branch, or learn vision-language interaction in a shallow mechanism. In this context, we propose a Deeply coupled Cross-modal Prompt learning (DCP) method based on CLIP. DCP flexibly accommodates the interplay between vision and language with a Cross-Modal Prompt Attention (CMPA) mechanism, which enables the mutual exchange of respective representation through a well-connected multi-head attention module progressively and strongly. We then conduct comprehensive few-shot learning experiments on 11 image classification datasets and analyze the robustness to domain shift as well. Thorough experimental analysis evidently demonstrates the superb few-shot generalization and compelling domain adaption capacity of a well-executed DCP. The code can be found at https://github.com/GingL/CMPA.Comment: Accepted by ACL 2023 finding

Phase Characterization of Cucumber Growth: A Chemical Gel Model

Cucumber grows with complex phenomena by changing its volume and shape, which is not fully investigated and challenges agriculture and food safety industry. In order to understand the mechanism and to characterize the growth process, the cucumber is modeled as a hydrogel in swelling and its development is studied in both preharvest and postharvest stages. Based on thermodynamics, constitutive equations, incorporating biological quantities, are established. The growth behavior of cucumber follows the classic theory of continuous or discontinuous phase transition. The mechanism of bulged tail in cucumber is interpreted by phase coexistence and characterized by critical conditions. Conclusions are given for advances in food engineering and novel fabrication techniques in mechanical biology

Anisotropic shear stress σxy\sigma_{xy} effects in the basal plane of Sr2_2RuO4_4

In this short note, we repeat the calculations the jumps for the specific heat C$_{\sigma_{xy}}$, the elastic compliance S$_{xyxy}^{\sigma_{xy}}$ and the thermal expansion $\alpha_{\sigma_{xy}}$ due to a shear stress $\sigma_{xy}$ in the basal plane of $Sr_2RuO_4$. Henceforth we clarify some issues regarding the elastic theoretical framework suitable to explain the sound speed experiments of Lupien et al. (2001,2002), and partially the strain experiments of Hicks et al. (2014), and Steppke et al. (2016) in strontium ruthenate. We continue to propose that the discontinuity in the elastic constant C$_{xyxy}$ of this tetragonal crystal gives unambiguous experimental evidence that the superconducting order parameter $\Psi$ has two components with a broken time-reversal symmetry state, and that the $\gamma$ band couples the anisotropic electron-phonon interaction to the $[xy]$ in-plane shear stress according to Walker and collaborators [4] and [3]. Some importants words about the roll of the spin equal to one for the transversal phonons are added in the conclusion following Levine [34].Comment: 11 pages, for section 5: added figure 2 and figure 3 replaced. One reference and typos added. figure 4 added. arXiv admin note: text overlap with arXiv:1812.0649

Extracellular Vesicles: “Stealth Transport Aircrafts” for Drugs

Extracellular vesicles (EVs) can deliver many types of drugs with their natural source material transport properties, inherent long-term blood circulation capabilities and excellent biocompatibility, and have great potential in the field of drug carrier. Modification of the content and surface of EVs according to the purpose of treatment has become a research focus to improve the drug load and the targeting of EVs. EVs can maximize the stability of the drugs, prevent immune clearance and achieve accurate delivery. Therefore, EVs can be described as “stealth transport aircrafts” for drugs. This chapter will respectively introduce the application of natural EVs as cell substitutes in cell therapy and engineered EVs as carriers of nucleic acids, proteins, small molecule drugs and therapeutic viral particles in disease treatment. It will also explain the drug loading and modification strategies of EVs, the source and characteristics of EVs. In addition, the commercialization progress of EVs drugs will be mentioned here, and the problems in their applications will be discussed in conjunction with the application of EVs in the treatment of COVID-19

What Large Language Models Bring to Text-rich VQA?

Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this work, we focus on investigating the advantages and bottlenecks of LLM-based approaches in addressing this problem. To address the above concern, we separate the vision and language modules, where we leverage external OCR models to recognize texts in the image and Large Language Models (LLMs) to answer the question given texts. The whole framework is training-free benefiting from the in-context ability of LLMs. This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets. Besides, based on the ablation study, we find that LLM brings stronger comprehension ability and may introduce helpful knowledge for the VQA problem. The bottleneck for LLM to address text-rich VQA problems may primarily lie in visual part. We also combine the OCR module with MLLMs and pleasantly find that the combination of OCR module with MLLM also works. It's worth noting that not all MLLMs can comprehend the OCR information, which provides insights into how to train an MLLM that preserves the abilities of LLM

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis

Indoor location information is an indispensable parameter for modern intelligent warehouse management and robot navigation. Indoor wireless positioning exhibits large errors due to factors such as indoor non-line-of-sight (NLOS) obstructions. In the present study, the error value under the time of arrival (TOA) algorithm was evaluated, and the trilateral positioning method was optimized to minimize the errors. An optimization algorithm for indoor ultra-wideband (UWB) positioning was designed, which was referred as annealing evolution and clustering fusion optimization algorithm. The algorithm exploited the good local search capability of the simulated annealing algorithm and the good global search capability of the genetic algorithm to optimize cluster analysis. The optimal result from sampled data was quickly determined to achieve effective and accurate positioning. These features reduced the non-direct aiming error in the indoor UWB environment. The final experimental results showed that the optimized algorithm significantly reduced noise interference as well as improved positioning accuracy in an NLOS indoor environment with less than 10 cm positioning error