Emergent Correspondence from Image Diffusion

Finding correspondences between images is a fundamental problem in computer vision. In this paper, we show that correspondence emerges in image diffusion models without any explicit supervision. We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT), and use them to establish correspondences between real images. Without any additional fine-tuning or supervision on the task-specific data or annotations, DIFT is able to outperform both weakly-supervised methods and competitive off-the-shelf features in identifying semantic, geometric, and temporal correspondences. Particularly for semantic correspondence, DIFT from Stable Diffusion is able to outperform DINO and OpenCLIP by 19 and 14 accuracy points respectively on the challenging SPair-71k benchmark. It even outperforms the state-of-the-art supervised methods on 9 out of 18 categories while remaining on par for the overall performance. Project page: https://diffusionfeatures.github.ioComment: Project page: https://diffusionfeatures.github.i

Protecting Entanglement of Two V-type Atoms in Dissipative Cavity by Dipole-Dipole Interaction

In this work, we study a coupled system of two V-type atoms interacting with a dissipative single-mode cavity, which couples with an external environment. Firstly, in order to diagonalize Hamiltonian of dissipative cavity, we introduce a set of new creation and annihilation operators according to theorem Fano. Then, we obtain the analytical solution of this model by solving the time dependent Schrodinger equation. We also discuss in detail the influences of the cavity-environment coupling, the SGI parameter, the initial state and the dipole-dipole interaction between the two atoms on entanglement dynamics. The results show that, with the SGI parameter increasing, the entanglement will decay quicker for the initially maximal entangled state but it will decay slower for the initially partial entangled state. For the initially product state, the larger the SGI parameter, the more entanglement will be generated. The strong coupling can protect entanglement to some extent, but the dipole-dipole interaction can significantly protect entanglement. Moreover, the dipole-dipole interaction can not only generate entanglement very effectively, but also enhance the regulation effect of {\theta} on entanglement for the initially partial entangled and product states.Comment: 10 pages, 9 figure

The Arabidopsis thaliana elongator complex subunit 2 epigenetically affects root development

The elongator complex subunit 2 (ELP2) protein, one subunit of an evolutionarily conserved histone acetyltransferase complex, has been shown to participate in leaf patterning, plant immune and abiotic stress responses in Arabidopsis thaliana. Here, its role in root development was explored. Compared to the wild type, the elp2 mutant exhibited an accelerated differentiation of its root stem cells and cell division was more active in its quiescent centre (QC). The key transcription factors responsible for maintaining root stem cell and QC identity, such as AP2 transcription factors PLT1 (PLETHORA1) and PLT2 (PLETHORA2), GRAS transcription factors such as SCR (SCARECROW) and SHR (SHORT ROOT) and WUSCHEL-RELATED HOMEOBOX5 transcription factor WOX5, were all strongly down-regulated in the mutant. On the other hand, expression of the G2/M transition activator CYCB1 was substantially induced in elp2. The auxin efflux transporters PIN1 and PIN2 showed decreased protein levels and PIN1 also displayed mild polarity alterations in elp2, which resulted in a reduced auxin content in the root tip. Either the acetylation or methylation level of each of these genes differed between the mutant and the wild type, suggesting that the ELP2 regulation of root development involves the epigenetic modification of a range of transcription factors and other developmental regulators

Study on the Influence of Land Use Change on Carbon Emissions Using System Modeling under the Framework of Dual Carbon Goals

At the crucial period of addressing climate change, especially to the carbonization of land use change, it is vital that relevant actions are taken to enable two ambitious dual-carbon goals, namely, ensuring that carbon emissions peak before 2030 and achieving carbon neutrality before 2060. This research investigates the impacts of land use changes on carbon emissions using a novel approach that integrates Light Detection and Ranging (LiDAR) with Geographic Information System (GIS). This approach is innovative due to its high quality three-dimensional representation to quantified exact carbon stock and forest emissions occurring due to specific land-use change. Therefore, through actual LiDAR, this research helps demarcate the pattern emitting different land-use measures, including deforestation, urban programs, agricultural differences, and forest and land changes, over historical change records and verified carbonization formulas. Similar qualitative levels between LiDAR and GIS analysis help determine the varying degrees of carbonization occurring due to enhanced deforestation, urban additions, and agricultural contributions while reporting the possible procedural carbons acquired during reforestation and other measurements. The results helped clarify that the most distinct level of land utilization shows the least level of carbon sent into the air. Therefore, the implication is that strategic land use measures and better working conditions can curb carbon indications. These signals support land-use policy and preparedness goals in a low carbon level. This study creates valuable records for the land utilization and cartograph, created through the power of LiDAR and GIS analysis

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models

Integration of residents' experiences into economic planning process of coastal villages: Evidence from the Greater Hangzhou Bay Rim Area

Public value is gaining prominence from both academics and politicians with regards to China's rural development. However, rural planning authorities and practitioners showed limited confidence on public, which manifests as few public perceptions were integrated into the planning documents. This study explores the potential role of residents' experiences in illustrating local economic development within the context of coastal villages in which economic and industries are rapidly transforming. Two case studies from within the locale of the Greater Hangzhou Bay Rim Area are used in this article to examine the gap between residents' experiences and the actual economic development that has occurred. The main findings suggest that rural residents can directly reflect upon both current and historic trends of local economic development. Moreover, household income satisfaction (HIS) is a comprehensive notion of residents' experiences, and indicates social and economic sustainability of industrial transformation, or "thriving business", that have been highlighted in coastal villages. Public experiences could therefore act as a valid and accessible evidence for planners in rural economic planning in China and other developing countries

Astragalin Suppresses Inflammatory Responses and Bone Destruction in Mice With Collagen-Induced Arthritis and in Human Fibroblast-Like Synoviocytes

Astragalin, as a bioactive flavonoid with anti-inflammatory, antioxidant, and protective properties, provides a potential agent for rheumatoid arthritis (RA). In this study, its therapeutic efficacy and the underlying mechanisms were explored using DBA/1J mice with collagen-induced arthritis (CIA). It was demonstrated that astragalin could significantly attenuate inflammation of CIA mice. The effects were associated with decreased severity of arthritis (based on the arthritis index), joint swelling and reduced bone erosion and destruction. Furthermore, astragalin treatment suppressed the production of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-8), and inhibited the expression of matrix metalloproteinases (MMP-1, MMP-3, and MMP-13) in chondrocytes and synovial cells of CIA mice. Fibroblast-like synoviocytes derived from RA patients (MH7A cells) were applied to verify these effects. In vitro, astragalin inhibited the expression of matrix metalloproteinases (MMP-1, MMP-3, and MMP-13) dose-dependently in TNF-α-induced MH7A cells, with no apparent cytotoxicity. Furthermore, astragalin suppressed the phosphorylation of p38, JNK, and the activation of c-Jun/AP-1 in TNF-α-induced MH7A cells. In conclusion, it has proven that astragalin could attenuate synovial inflammation and joint destruction in RA at least partially by restraining the phosphorylation of MAPKs and the activating of c-Jun/AP-1. Therefore, astragalin can be a potential therapeutic agent for RA