Compositional Zero-shot Learning via Progressive Language-based Observations

Compositional zero-shot learning aims to recognize unseen state-object compositions by leveraging known primitives (state and object) during training. However, effectively modeling interactions between primitives and generalizing knowledge to novel compositions remains a perennial challenge. There are two key factors: object-conditioned and state-conditioned variance, i.e., the appearance of states (or objects) can vary significantly when combined with different objects (or states). For instance, the state "old" can signify a vintage design for a "car" or an advanced age for a "cat". In this paper, we argue that these variances can be mitigated by predicting composition categories based on pre-observed primitive. To this end, we propose Progressive Language-based Observations (PLO), which can dynamically determine a better observation order of primitives. These observations comprise a series of concepts or languages that allow the model to understand image content in a step-by-step manner. Specifically, PLO adopts pre-trained vision-language models (VLMs) to empower the model with observation capabilities. We further devise two variants: 1) PLO-VLM: a two-step method, where a pre-observing classifier dynamically determines the observation order of two primitives. 2) PLO-LLM: a multi-step scheme, which utilizes large language models (LLMs) to craft composition-specific prompts for step-by-step observing. Extensive ablations on three challenging datasets demonstrate the superiority of PLO compared with state-of-the-art methods, affirming its abilities in compositional recognition

Electrical coupling between ventricular myocytes and myofibroblasts in the infarcted mouse heart

Aims: Recent studies have demonstrated electrotonic coupling between scar tissue and the surrounding myocardium in cryoinjured hearts. However, the electrical dynamics occurring at the myocyte-nonmyocyte interface in the fibrotic heart remain undefined. Here, we sought to develop an assay to interrogate the nonmyocyte cell type contributing to heterocellular coupling and to characterize, on a cellular scale, its voltage response in the infarct border zone of living hearts. Methods and results: We used two-photon laser scanning microscopy in conjunction with a voltage-sensitive dye to record transmembrane voltage changes simultaneously from cardiomyocytes and adjoined nonmyocytes in Langendorff-perfused mouse hearts with healing myocardial infarction. Transgenic mice with cardiomyocyte-restricted expression of a green fluorescent reporter protein underwent permanent coronary artery ligation and their hearts were subjected to voltage imaging 7-10 days later. Reporter-negative cells, i.e. nonmyocytes, in the infarct border zone exhibited depolarizing transients at a 1:1 coupling ratio with action potentials recorded simultaneously from adjacent, reporter-positive ventricular myocytes. The electrotonic responses in the nonmyocytes exhibited slower rates of de- and repolarization compared to the action potential waveform of juxtaposed myocytes. Voltage imaging in infarcted hearts expressing a fluorescent reporter specifically in myofibroblasts revealed that the latter were electrically coupled to border zone myocytes. Their voltage transient properties were indistinguishable from those of nonmyocytes in hearts with cardiomyocyte-restricted reporter expression. The density of connexin43 expression at myofibroblast-cardiomyocyte junctions was ∼5% of that in the intercalated disc regions of paired ventricular myocytes in the remote, uninjured myocardium, whereas the ratio of connexin45 to connexin43 expression levels at heterocellular contacts was ∼1%. Conclusion: Myofibroblasts contribute to the population of electrically coupled nonmyocytes in the infarct border zone. The slower kinetics of myofibroblast voltage responses may reflect low electrical conductivity across heterocellular junctions, in accordance with the paucity of connexin expression at myofibroblast-cardiomyocyte contacts

Efficient Content Location Using Semantic Small World in Peer-to-Peer Networks

        Locating content in unstructured peer-to-peer networks is a challenging problem. This paper presents a novel semantic small world resource search mechanism to address the problem. By using vector space model to compute the semantic relevance and applying small world properties such as low average hop distance and high clustering coefficient to construct a cluster overlay. In semantic small world system, the search mechanism is divided into two parts, searching at cluster and outside cluster through inner link and short link, so that it can achieve the incremental research. It significantly reduces the average path length and query cost. Meanwhile, the simulation results show that semantic small world scheme outperforms K-random walks and flooding scheme than higher query hit rate and lower query latency

The Improvement of Chord Protocol about Structured P2P System

Chord protocol is one of the most classical protocols in structured P2P system, with the good effect, reliability, high query efficiency, and many other advantages. However, Chord agreement remains two main shortcomings the one that the searching speed of resources is slower, and the other that the detention of routing is higher. This paper in view of these shortcomings, combination with modification, then put forward the new Chord structure that super nodes and common nodes coexist, super nodes management general nodes. The new structure using Zipf-law determines the proportion of super nodes and ordinary nodes. The last, the new structure is simulated by simulation software, and the improved Chord protocol make better the previous shortcoming through the new structure compares with the Chord protocol

Magnetosome Gene Duplication as an Important Driver in the Evolution of Magnetotaxis in the Alphaproteobacteria

The evolution of microbial magnetoreception (or magnetotaxis) is of great interest in the fields of microbiology, evolutionary biology, biophysics, geomicrobiology, and geochemistry. Current genomic data from magnetotactic bacteria (MTB), the only prokaryotes known to be capable of sensing the Earth’s geomagnetic field, suggests an ancient origin of magnetotaxis in the domain Bacteria. Vertical inheritance, followed by multiple independent magnetosome gene cluster loss, is considered to be one of the major forces that drove the evolution of magnetotaxis at or above the class or phylum level, although the evolutionary trajectories at lower taxonomic ranks (e.g., within the class level) remain largely unstudied. Here we report the isolation, cultivation, and sequencing of a novel magnetotactic spirillum belonging to the genus Terasakiella (Terasakiella sp. strain SH-1) within the class Alphaproteobacteria. The complete genome sequence of Terasakiella sp. strain SH-1 revealed an unexpected duplication event of magnetosome genes within the mamAB operon, a group of genes essential for magnetosome biomineralization and magnetotaxis. Intriguingly, further comparative genomic analysis suggests that the duplication of mamAB genes is a common feature in the genomes of alphaproteobacterial MTB. Taken together, with the additional finding that gene duplication appears to have also occurred in some magnetotactic members of the Deltaproteobacteria, our results indicate that gene duplication plays an important role in the evolution of magnetotaxis in the Alphaproteobacteria and perhaps the domain Bacteria

Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual relation detection (VRD) is a typical task that identifies relationship (or interaction) types between object pairs within an image. However, naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish between different fine-grained relation types and it neglects essential spatial information of two objects. To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. Specifically, RECODE first decomposes each predicate category into subject, object, and spatial components. Then, it leverages large language models (LLMs) to generate description-based prompts (or visual cues) for each component. Different visual cues enhance the discriminability of similar relation categories from different perspectives, which significantly boosts performance in VRD. To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues. Extensive experiments on four VRD benchmarks have demonstrated the effectiveness and interpretability of RECODE