4,796 research outputs found

    The Beginning of Wisdom: Imagining fear in French Romanesque portal sculpture, c.1080-1140

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    This thesis examines the role of fear in the design and function of Romanesque portal sculptures c.1090-1140 to understand how representations of the majestas Domini, Last Judgment and Hell were intended to guide their audiences towards an emotional state of terror and wonder that would lead to wisdom. It focuses on a small collection of important sites in Burgundy and the regions of Aveyron and Tarn-et-Garonne in south-western France to examine the development of monumental sculpture as a means of conveying and formulating theological concepts to lay and monastic audiences. Through an analysis of their iconography and composition in relation to exegetical and literary interpretations of divine majesty, judgment and eternal damnation, it offers new perspectives on pre-Scholastic art and thought, and contributes to current scholarship on affective devotion and emotional response in the context of Romanesque and Gothic sculpture. The new imagery created for the medium of portal sculpture is contextualised within the iconographic traditions which developed from Late Antiquity and continued to evolve over the early Middle Ages. The role of emotions, particularly fear, in the devotional cultures of the early twelfth century also presents new insights into the nature of visuality and spiritual sight in the Middle Ages. Portal sculptures were designed to prompt their audiences to develop the fearful attitude shared by the prophets, and which would remain even after the Last Judgment. Representations of response and the replication of divinely-created images encouraged those viewing the sculpture to imagine them as if they were real to participate in the visionary experience of the prophets or terror of the resurrected dead at the Last Judgment

    I'm a believer : Evans' transparency remark and self-knowledge

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    The central goal of this thesis is to understand self-knowledge through understanding a particularly difficult and promising remark of Gareth Evans’, from his The Varieties of Reference (Evans, 1982), a remark which has formed the basis of so called ‘Transparency’ accounts of self-knowledge. Evans’ Transparency Remark is sometimes read as deflationary of self-knowledge in some respect, and I hope to show that although Evans’ account is indeed deflationary of our ordinary idea of self-knowledge, it retains what we might consider central features of an account of self-knowledge. I do this by giving an overview of the literature surrounding Evans’ remark and making a distinction between Rationalist and Inferentialist accounts of Transparency. I also suggest that the goal of an account of self-knowledge is to explain, or explain away, the phenomenon of Privileged Access. Having done this we return to Evans’ development of his remark, and from that I develop a novel Rationalist account of self-knowledge of belief which hews closely to Evans’ own development but differs in one significant way, which leads to an answer to one of the central objections to Transparency accounts of self-knowledge, the Puzzle of Transparency. Having developed this Simple Account of Transparency and defended it against what I take to be the major objections to Transparency accounts, I turn to the best developed Inferentialist account, Byrne’s Transparency and Self-Knowledge (Byrne, 2018), and suggest why we might find his account wanting. Finally, I suggest ways in which the Simple Account of Transparency might be extended into a general account of self-knowledge, and suggest there is one important unanswered question remaining."This work was supported by the Templeton Foundation Knowledge Beyond Natural Sciences project [grant number 58450]" -- Fundin

    Defending Black-box Classifiers by Bayesian Boundary Correction

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    Classifiers based on deep neural networks have been recently challenged by Adversarial Attack, where the widely existing vulnerability has invoked the research in defending them from potential threats. Given a vulnerable classifier, existing defense methods are mostly white-box and often require re-training the victim under modified loss functions/training regimes. While the model/data/training specifics of the victim are usually unavailable to the user, re-training is unappealing, if not impossible for reasons such as limited computational resources. To this end, we propose a new black-box defense framework. It can turn any pre-trained classifier into a resilient one with little knowledge of the model specifics. This is achieved by new joint Bayesian treatments on the clean data, the adversarial examples and the classifier, for maximizing their joint probability. It is further equipped with a new post-train strategy which keeps the victim intact. We name our framework Bayesian Boundary Correction (BBC). BBC is a general and flexible framework that can easily adapt to different data types. We instantiate BBC for image classification and skeleton-based human activity recognition, for both static and dynamic data. Exhaustive evaluation shows that BBC has superior robustness and can enhance robustness without severely hurting the clean accuracy, compared with existing defense methods.Comment: arXiv admin note: text overlap with arXiv:2203.0471

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning

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    Multi-agent systems require effective coordination between groups and individuals to achieve common goals. However, current multi-agent reinforcement learning (MARL) methods primarily focus on improving individual policies and do not adequately address group-level policies, which leads to weak cooperation. To address this issue, we propose a novel Consensus-oriented Strategy (CoS) that emphasizes group and individual policies simultaneously. Specifically, CoS comprises two main components: (a) the vector quantized group consensus module, which extracts discrete latent embeddings that represent the stable and discriminative group consensus, and (b) the group consensus-oriented strategy, which integrates the group policy using a hypernet and the individual policies using the group consensus, thereby promoting coordination at both the group and individual levels. Through empirical experiments on cooperative navigation tasks with both discrete and continuous spaces, as well as Google research football, we demonstrate that CoS outperforms state-of-the-art MARL algorithms and achieves better collaboration, thus providing a promising solution for achieving effective coordination in multi-agent systems

    A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

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    Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

    Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

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    Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their ability to accurately model long-range dependencies in spatio-temporal data by learning global convolutions in a computationally efficient manner. To this end, FNOs rely on the discrete Fourier transform (DFT), however, DFTs cause visual and spectral artifacts as well as pronounced dissipation when learning operators in spherical coordinates since they incorrectly assume a flat geometry. To overcome this limitation, we generalize FNOs on the sphere, introducing Spherical FNOs (SFNOs) for learning operators on spherical geometries. We apply SFNOs to forecasting atmospheric dynamics, and demonstrate stable auto\-regressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change

    The Expedition PS129 of the Research Vessel POLARSTERN to the Weddell Sea in 2022

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