Weakly supervised coupled networks for visual sentiment analysis

Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions on-line. In this paper, we solve the problem of visual sentiment analysis using the high-level abstraction in the recognition process. Existing methods based on convolutional neural networks learn sentiment representations from the holistic image appearance. However, different image regions can have a different influence on the intended expression. This paper presents a weakly supervised coupled convolutional network with two branches to leverage the localized information. The first branch detects a sentiment specific soft map by training a fully convolutional network with the cross spatial pooling strategy, which only requires image-level labels, thereby significantly reducing the annotation burden. The second branch utilizes both the holistic and localized information by coupling the sentiment map with deep features for robust classification. We integrate the sentiment detection and classification branches into a unified deep framework and optimize the network in an end-to-end manner. Extensive experiments on six benchmark datasets demonstrate that the proposed method performs favorably against the state-ofthe- art methods for visual sentiment analysis

Historical Evolution and Frontier Trends of Research in the Field of Ecological Security Patterns in China

To further explore the scientific research level, research hotspots, and cutting-edge trends in the field of China\u27s ecological security pattern, this paper, based on the China Academic Journal Network Publishing Repository, uses CiteSpace and VOSviewer and the literature from 2002 - 2023 to carry out a visual analysis and knowledge mapping of China\u27s ecological security pattern research. The results show that from 2002 - 2023, publications in the field of China\u27s ecological security pattern have increased; there is close cooperation among authors, and the authors with the first publication and the first citation frequency are Peng Jian and Yu Kongjian from Peking University, respectively. The distribution of the research institutes is concentrated, with the dominant institution being the University of the Chinese Academy of Sciences. This field primarily takes "ecological security pattern," "ecological corridor," "minimum cumulative resistance model (mcr)," and "ecological source" as the hot research content, based on which the research hotspots are classified into five aspects, and "ecological source" and "circuit theory" are the key research frontiers. The robust scholarly interest in China\u27s ecological security pattern underscores its significance, and the comprehensive summary and analysis of research status and application dynamics are poised to foster collaborative advancements in this critical field

Application of the Meijer theorem in calculation of three-loop massive vacuum Feynman integrals and beyond

We present an analytical method to calculate the three-loop massive Feynman integral in arbitrary dimensions. The method is based on the Mellin-Barnes representation of the Feynman integral. The Meijer theorem and its corollary are used to perform the integration over the Gamma functions, exponential functions, and hypergeometric functions. We also discuss the application of the method in other multi-loop Feynman integrals.Comment: 24 pages, 4 figure

DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing

Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address these issues and achieve cross-modal fusion detection. However, the quadratic computational complexity of transformers limits their performance. Inspired by the efficiency and lower complexity of Mamba in long sequence tasks, we propose Disparity-guided Multispectral Mamba (DMM), a multispectral oriented object detection framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task. The DCFM module leverages disparity information between modalities to adaptively merge features from RGB and IR images, mitigating inter-modal conflicts. The MTA module aims to enhance feature representation by focusing on relevant target regions within the RGB modality, addressing intra-modal variations. The TPA auxiliary task utilizes single-modal labels to guide the optimization of the MTA module, ensuring it focuses on targets and their local context. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our method, which outperforms state-of-the-art methods while maintaining computational efficiency. Code will be available at https://github.com/Another-0/DMM.Comment: 12 pages, 9 figure

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First Principle and Deep Learning Molecular Dynamics Simulations

Complex strain status can exist in 2D materials during their synthesis process, resulting in significant impacts on the physical and chemical properties. Despite their prevalence in experiments, their influence on the material properties and the corresponding mechanism are often understudied due to the lack of effective simulation methods. In this work, we investigated the effects of bending, rippling, and bubbling on the ferroelectric domains in In2Se3 monolayer by density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations. The analysis of the tube model shows that bending deformation imparts asymmetry into the system, and the polarization direction tends to orient towards the tensile side, which has a lower energy state than the opposite polarization direction. The energy barrier for polarization switching can be reduced by compressive strain according DFT results. The dynamics of the polarization switching is investigated by the DLMD simulations. The influence of curvature and temperature on the switching time follows the Arrhenius-style function. For the complex strain status in the rippling and bubbling model, the lifetime of the local transient polarization is analyzed by the autocorrelation function, and the size of the stable polarization domain is identified. Local curvature and temperature can influence the local polarization dynamics following the proposed Arrhenius-style equation. Through cross-scale simulations, this study demonstrates the capability of deep-learning potentials in simulating polarization for ferroelectric materials. It further reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering

Metal-like behavior of a 2D molecular catalyst enables redox-decoupled electrocatalysis

Molecular catalysts facilitate electrochemical conversion by changing their oxidation states to transfer electrons. However, this redox-mediated mechanism features stepwise electron transfer and substrate activation in separate elementary steps, thereby resulting in an inherent loss in efficiency. Here, we synthesize a two-dimensional (2D) iron phthalocyanine (FePc) material and uncover its non-mediated electron transfer behavior in electrocatalysis, which overcomes the conventional redox-mediated limitation in the oxygen reduction reaction (ORR) pathway that molecular catalysts face. The 2D geometry enables the FePc molecules to be positioned within the electrochemical double layer, enabling electrons to directly transfer to oxygen reactants, prior to the Fe(II/III) redox. This functions in a manner akin to a metal catalyst thereby opening a redox-decoupled ORR mechanism. As a result, the reported 2D FePc molecular catalyst exhibits unprecedented ORR half-wave potential at 0.945 V vs. the reversible hydrogen electrode, achieving efficient application in zinc-air batteries and H2/O2 fuel cells. These findings open new possibilities in voltage efficient, redox-decoupled molecular catalysis that integrates strengths of molecules and materials in one synergistic system.</p

Retrieving and classifying affective Images via deep metric learning

Affective image understanding has been extensively studied in the last decade since more and more users express emotion via visual contents. While current algorithms based on convolutional neural networks aim to distinguish emotional categories in a discrete label space, the task is inherently ambiguous. This is mainly because emotional labels with the same polarity (i.e., positive or negative) are highly related, which is different from concrete object concepts such as cat, dog and bird. To the best of our knowledge, few methods focus on leveraging such characteristic of emotions for affective image understanding. In this work, we address the problem of understanding affective images via deep metric learning and propose a multi-task deep framework to optimize both retrieval and classification goals. We propose the sentiment constraints adapted from the triplet constraints, which are able to explore the hierarchical relation of emotion labels. We further exploit the sentiment vector as an effective representation to distinguish affective images utilizing the texture representation derived from convolutional layers. Extensive evaluations on four widely-used affective datasets, i.e., Flickr and Instagram, IAPSa, Art Photo, and Abstract Paintings, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both affective image retrieval and classification task

Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks

Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.Comment: 11 pages, 8 figure