Search

Identifying structures in common forms the basis for networked systems design and optimization. However, real structures represented by graphs are often of varying sizes, leading to the low accuracy of traditional graph classification methods. These graphs are called cross-scale graphs. To overcome this limitation, in this study, we propose GSpect, an advanced spectral graph filtering model for cross-scale graph classification tasks. Compared with other methods, we use graph wavelet neural networks for the convolution layer of the model, which aggregates multi-scale messages to generate graph representations. We design a spectral-pooling layer which aggregates nodes to one node to reduce the cross-scale graphs to the same size. We collect and construct the cross-scale benchmark data set, MSG (Multi Scale Graphs). Experiments reveal that, on open data sets, GSpect improves the performance of classification accuracy by 1.62% on average, and for a maximum of 3.33% on PROTEINS. On MSG, GSpect improves the performance of classification accuracy by 15.55% on average. GSpect fills the gap in cross-scale graph classification studies and has potential to provide assistance in application research like diagnosis of brain disease by predicting the brain network's label and developing new drugs with molecular structures learned from their counterparts in other systems.

Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotional responses. This task is inherently complex due to its twofold objective: significantly evoking the intended emotion, while preserving the original image composition. Existing AIM methods primarily adjust color and style, often failing to elicit precise and profound emotional shifts. Drawing on psychological insights, we extend AIM by incorporating content modifications to enhance emotional impact. We introduce EmoEdit, a novel two-stage framework comprising emotion attribution and image editing. In the emotion attribution stage, we leverage a Vision-Language Model (VLM) to create hierarchies of semantic factors that represent abstract emotions. In the image editing stage, the VLM identifies the most relevant factors for the provided image, and guides a generative editing model to perform affective modifications. A ranking technique that we developed selects the best edit, balancing between emotion fidelity and structure integrity. To validate EmoEdit, we assembled a dataset of 416 images, categorized into positive, negative, and neutral classes. Our method is evaluated both qualitatively and quantitatively, demonstrating superior performance compared to existing state-of-the-art techniques. Additionally, we showcase EmoEdit's potential in various manipulation tasks, including emotion-oriented and semantics-oriented editing.

In the rapidly evolving automotive industry, Systems-on-Chips (SoCs) are playing an increasingly crucial role in enhancing vehicle intelligence, connectivity, and safety features. For enterprises whose business encompasses automotive SoCs, the sustained and stable provision and receipt of SoC relevant goods or services are essential. Considering the imperative for a resilient and adaptable supply network, enterprises are concentrating their efforts on formulating strategies to address risks stemming from supply chain disruptions caused by technological obsolescence, natural disasters, and geopolitical tensions. This study presents an open supply knowledge extraction and complement approach and build a supply chain network of automotive SoC enterprises in China, which incorporates cross-domain named entity recognition under limited information, fuzzy matching of firm entities, and supply relation inferring based on knowledge graph. Subsequently, we exhibit the degree and registered capital distribution across firms, and analyze the correlations between centrality metrics in the supply chain network. Finally, based on recovery capacity and risk transfer, two interaction disruption models (IDMs) are developed to elucidate the adaptive behaviors and effect of network disruptions under various business and attack strategies. This research not only aids in exploring the complexities of Chinese automotive SoC supply chain but also enriches our understanding of the dynamics of firm behavior in this crucial industry sector., Comment: arXiv admin note: text overlap with arXiv:2304.10428 by other authors

How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a comprehensive computational framework based on simplified neuronal activation mechanisms to simulate the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neural network structures can generate activation patterns strikingly similar to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models. This consistency, achieved with the same network structure, underscores the pivotal role of network topology in neural information processing. These results challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns. Additionally, our experiments revealed that a reconnect rate of at least 0.1% was sufficient to disrupt the previously observed activation patterns. We also observed synergistic effects between the brain hemispheres: Even with unilateral input stimuli, visual-related neurons in both hemispheres were activated, highlighting the importance of interhemispheric communication. These findings emphasize the crucial role of network structure in neural activation and offer novel insights into the fundamental principles governing brain functionality.

Recent years have witnessed remarkable progress in image generation task, where users can create visually astonishing images with high-quality. However, existing text-to-image diffusion models are proficient in generating concrete concepts (dogs) but encounter challenges with more abstract ones (emotions). Several efforts have been made to modify image emotions with color and style adjustments, facing limitations in effectively conveying emotions with fixed image contents. In this work, we introduce Emotional Image Content Generation (EICG), a new task to generate semantic-clear and emotion-faithful images given emotion categories. Specifically, we propose an emotion space and construct a mapping network to align it with the powerful Contrastive Language-Image Pre-training (CLIP) space, providing a concrete interpretation of abstract emotions. Attribute loss and emotion confidence are further proposed to ensure the semantic diversity and emotion fidelity of the generated images. Our method outperforms the state-of-the-art text-to-image approaches both quantitatively and qualitatively, where we derive three custom metrics, i.e., emotion accuracy, semantic clarity and semantic diversity. In addition to generation, our method can help emotion understanding and inspire emotional art design.

Due to the modality gap between visible and infrared images with high visual ambiguity, learning \textbf{diverse} modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect that erasing body-shape-related semantic concepts in the learned features can force the ReID model to extract more and other modality-shared features for identification. To this end, we propose shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. Jointly learning shape-related feature in one subspace and shape-erased features in the orthogonal complement achieves a conditional mutual information maximization between shape-erased feature and identity discarding body shape information, thus enhancing the diversity of the learned representation explicitly. Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method., Comment: CVPR 2023

Understanding wildlife-livestock interactions is crucial for the design and management of protected areas that aim to conserve large mammal communities undergoing conflicts with humans worldwide. An example of the need to quantify the strength and direction of species interactions is the conservation of big cats in newly established protected areas in China. Currently, free-ranging livestock degrade the food and habitat of the endangered Amur tiger and Amur leopard in the forest landscapes of Northeast China, but quantitative assessments of how livestock affect the use of habitat by the major ungulate prey of these predators are very limited. Here, we examined livestock-ungulate interactions using large-scale camera-trap data in the newly established Tiger and Leopard National Park in Northeast China, which borders Russia. We used N-mixture models, two-species occupancy models and activity pattern overlap to understand the effects of cattle grazing on three ungulate species (wild boar, roe deer and sika deer) at a fine spatiotemporal scale. Our results showed that incorporating the biotic interactions with cattle had significant negative effects on encounters with three ungulates; sika deer were particularly displaced as more cattle encroached on forest habitat, as they exhibited low levels of co-occurrence with cattle in terms of habitat use. These results, combined with spatiotemporal overlap, suggested fine-scale avoidance behaviours, and they can help to refine strategies for the conservation of tigers, leopards and their prey in human-dominated transboundary landscapes. Progressively controlling cattle and the impact of cattle on biodiversity while simultaneously addressing the economic needs of local communities should be key priority actions for the Chinese government., Comment: 43 pages, 4 figures and 3 tables