The fight-or-flight response (FFR) is an instantaneous organismic response driven by emotions to external stimuli. However, most current intelligent systems requiring real-time environmental response overlooked this fundamental mechanism. Meanwhile, endowing systems with pre-response adaptive decision-making ability based on environment complexity (EC) considerations also necessitates in-depth research. This work proposes a decision-making model with a bionic memristive circuit comprising n FFR circuits and one environment enrichment (EE) module. The FFR circuit simulates FFR and implements long-term emotion memory (LTEM), memory forgetting, emotion generalization and fast emotion arousal. The EE module allows the circuit to dynamically adjust response targets in multi-stimulus scenarios by considering EC. Consequently, the circuit achieves adaptive and minimal delay responses to changeable stimuli via pre-response decision-making, validated by PSPICE simulations. The use of memristors facilitates online in-situ operating and in-memory computing, which makes the circuit expected to be deployed on multi-nozzle fire-fighting robots, enabling them to adaptively prioritize target processing in real time based on the urgency of various targets in multi-fire points scenarios.

Deep denoising models require extensive real-world training data, which is challenging to acquire. Current noise synthesis techniques struggle to accurately model complex noise distributions. We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges. By encoding camera settings into a time-aware camera-conditioned affine modulation (TCCAM), RNSD generates more realistic noise distributions under various camera conditions. Additionally, RNSD integrates a multi-scale content-aware module (MCAM), enabling the generation of structured noise with spatial correlations across multiple frequencies. We also introduce Deep Image Prior Sampling (DIPS), a learnable sampling sequence based on depth image prior, which significantly accelerates the sampling process while maintaining the high quality of synthesized noise. Extensive experiments demonstrate that our RNSD method significantly outperforms existing techniques in synthesizing realistic noise under multiple metrics and improving image denoising performance.

Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Despite recent progress, current document parsing methods have not been fairly and comprehensively evaluated due to the narrow coverage of document types and the simplified, unrealistic evaluation procedures in existing benchmarks. To address these gaps, we introduce OmniDocBench, a novel benchmark featuring high-quality annotations across nine document sources, including academic papers, textbooks, and more challenging cases such as handwritten notes and densely typeset newspapers. OmniDocBench supports flexible, multi-level evaluations—ranging from an end-to-end assessment to the task-specific and attribute-based analysis—using 19 layout categories and 15 attribute labels. We conduct a thorough evaluation of both pipeline-based methods and end-to-end vision-language models, revealing their strengths and weaknesses across different document types. OmniDocBench sets a new standard for the fair, diverse, and fine-grained evaluation in document parsing. Dataset and code are available at https://github.com/opendatalab/OmniDocBench.

This paper investigates the cooperative control of heterogeneous networked multiagent systems (MASs) in the presence of unknown nonlinear disturbances through one novel event-triggered asynchronous quasizero-sum game (ET-AQZSG) scheme. The individuals in the MASs are divided into principals and agents on the basis of their respective state values. A sufficient condition is established to prevent Zeno behavior in the ET-AQZSG scheme, ensuring its practical applicability. Additionally, a separable nonlinear functions (SNLFs) is proposed for assessing global stability. By leveraging norm inequalities and ordinary differential equations, a set of slackness conditions is formulated to ensure both feasibility and solution accuracy and promote convergence to the optimal solution. The proposed approach enhances communication flexibility without compromising computational efficiency. Finally, two numerical examples based on the topology of an electromagnetic actuator system and the topology of IEEE 39-Bus system are presented to demonstrate the efficacy of the ET-AQZSG scheme.

In recent years, with the rise of brain computer interface, automatic emotion recognition based on Electroencephalography (EEG) has attracted more and more attention. However, the emotional stimuli used in the existing studies are limited to music, pictures and videos, which cannot induce emotion well. Virtual reality (VR) can provide highly immersive 3D scenes, which can accurately and effectively induce emotion. Therefore, our work induced the subjects’ emotions through VR scenes, and collected the frontal EEG data based on wearable technology, innovatively proposed a VR-induced wearable frontal EEG emotion recognition dataset, which contains two sub datasets for cross-subject and cross-device research. Based on the dataset, we proposed a multi-spatial domain adaptation network (MSDAN) to eliminate the differences caused by individuals and EEG acquisition devices, and improve the generalization performance of the model in complex situations. MSDAN aimed to align the feature distributions of the source and target domains in multiple spaces and obtain the common features related to emotion. In the two sub datasets, our method achieved adequate results, which can obtain 72.08% and 75.14% accuracy in across-subject experiment,67.71% and 61.42% accuracy in across-device experiment, showing the significance and potential of the wearable EEG monitoring application based on VR in real life situation.

Remote sensing technique constructs a regression model to describe the relations between Aerosol optical depth (AOD) and the ground PM2.5concentration. However, potential effects from meteorological and geographical factors on the regression models have never been carefully investigated. The manuscript selects three main meteorological variables and two major geographical variables, and investigates their impacts in the performance of geographical weighted regression (GWR) and ordinary least square (OLS) models for estimating ground PM2.5concentration. Preliminary results on the case of Yangtze River Delta show that meteorological factors have more significant influence on the estimation of PM2.5concentration than geographical factors. Moreover, the observations tell that the GWR is more preferably to estimate PM2.5concentration than the OLS.

This manuscript proposes a symmetric sparse representation (SSR) method to extract pure endmembers from Hyperspectral imagery (HSI). The SSR assumes that the desired endmembers and all the HSI pixels can be sparsely represented by each other and it formulates the endmember extraction problem into finding archetypes in the minimal convex hull of the HSI data. The optimization program of SSR is solved by a simple projected gradient algorithm and the endmembers are initialized with the vector quantization scheme. Preliminary results on the popular Urban HSI data infer that the SSR performs better than several state-of-the-art methods (VCA, NFINDER, AVMAX, SVMAX, XRAY, OSP and H2NMF).

In this paper, we propose an adaptive and accurate online sub-scene modeling framework for moving cast shadow detection in applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. Additionally, in the correction process, object inner-edges analysis and shadow region expanding are adopted to reject shadow camouflages and recycle the misclassified shadow pixels respectively. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages in both outdoor and indoor scenes without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

In this paper, we propose a novel salient region detection method via color spatial distribution determined global contrasts. First, original image is preprocessed by a texture suppression approach, and segmented into superpixels. After that, the color spatial distribution of all superpixels is computed. Then, based on values of the distribution in whole image and boundaries of image, some superpixels are determined as foreground and background queries. Next, two global contrasts based on these queries are computed respectively to produce two different saliency maps. Ultimately, color spatial distribution and the two saliency maps are accumulated to generate final saliency map. Our approach is evaluated on M-SRA 1000 dataset, and the experimental results demonstrate superior performance of our method to eight state-of-the-art approaches.