3,439 research outputs found

    Statistical computation of Boltzmann entropy and estimation of the optimal probability density function from statistical sample

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    In this work, we investigate the statistical computation of the Boltzmann entropy of statistical samples. For this purpose, we use both histogram and kernel function to estimate the probability density function of statistical samples. We find that, due to coarse-graining, the entropy is a monotonic increasing function of the bin width for histogram or bandwidth for kernel estimation, which seems to be difficult to select an optimal bin width/bandwidth for computing the entropy. Fortunately, we notice that there exists a minimum of the first derivative of entropy for both histogram and kernel estimation, and this minimum point of the first derivative asymptotically points to the optimal bin width or bandwidth. We have verified these findings by large amounts of numerical experiments. Hence, we suggest that the minimum of the first derivative of entropy be used as a selector for the optimal bin width or bandwidth of density estimation. Moreover, the optimal bandwidth selected by the minimum of the first derivative of entropy is purely data-based, independent of the unknown underlying probability density distribution, which is obviously superior to the existing estimators. Our results are not restricted to one-dimensional, but can also be extended to multivariate cases. It should be emphasized, however, that we do not provide a robust mathematical proof of these findings, and we leave these issues with those who are interested in them.Comment: 8 pages, 6 figures, MNRAS, in the pres

    Individualism-collectivism and interpersonal memory guidance of attention

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    Recently it has been shown that the allocation of attention by a participant in a visual search task can be affected by memory items that have to be maintained by a co-actor, when similar tasks are jointly engaged by dyads (He, Lever, & Humphreys, 2011). In the present study we examined the contribution of individualism-collectivism to this ā€˜interpersonal memory guidanceā€™ effect. Actors performed visual search while a preview image was either held by the critical participant, held by a co-actor or was irrelevant to either participant. Attention during search was attracted to stimuli that matched the contents of the co-actorā€™s memory. This interpersonal effect correlated with the collectivism scores, and was enhanced by priming with a collectivistic scenario. The dimensions of individualism, however, did not contribute to performance. These data suggest that collectivism, but not individualism, modulates interpersonal influences on memory and attention in joint action

    Effects of SCH23390 and spiperone administered into medial striatum and intermediate medial mesopallium on rewarding effects of morphine in day-old chicks

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    .In the avian forebrain, the medial striatum and the intermediate medial mesopallium are thought to be important structures for associative learning in chicks, where the role of dopaminergic systems in learning processes has been verified in various behavioral paradigms, such as one-trial passive avoidance learning. However, it is not yet clear whether the dopaminergic system of these regions is responsible for associative learning underlying cue-elicited drug reward. In this study, a 6-day conditioning schedule in day-old chicks with i.p. morphine (2 mg/kg) was used to compare the effects of intracerebrally injected dopamine D(1) receptor antagonist, SCH23390, and D(2) antagonist, spiperone. The antagonists were injected bilaterally (3 mu g/site) into either the medial striatum or the intermediate medial mesopallium, and tests were conducted on morphine-induced conditioned place preference or locomotor activity. The acquisition of place preference was significantly inhibited by SCH23390 in either the medial striatum or the intermediate medial mesopallium, but not by spiperone. However, in the medial striatum, but not in the intermediate medial mesopallium, the locomotor activity was blocked by both SCH23390 and spiperone. These data suggest that the medial striatum and the intermediate medial mesopallium in birds are differentially involved in the rewarding effects of morphine, and similarly to mammals, the dopamine D(1) system may play an important role in the development of opiate reward. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved

    Image Restoration Algorithm Based on Artificial Fish Swarm Micro Decomposition of Unknown Priori Pixel

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    In this paper, we put forward a new method to holographic reconstruct image that prior information, module matching and edge structure information is unknown. The proposed image holographic restoration algorithm combines artificial fish swarm micro decomposition and brightness compensation. The traditional method uses subspace feature information of multidimensional search method, it is failed to achieve the fine structure information of image texture template matching and the effect is not well. Therefore, it is difficult to holographic reconstruct the unknown pixels. This weakness obstructs the application of image restoration to many fields. Therefore, we builds a structure texture conduction model for the priority determination of the block that to be repaired, then we use subspace feature information multidimensional search method to the confidence updates of unknown pixel. In order to maintain the continuity of damaged region in image, the artificial fish swarm algorithm decomposition model is combined with the image brightness compensation strategy of edge feature. The simulation result shows that it has a good visual effect in image restoration of a priori unknown pixel, recovery time and computation costs are less, the stability and convergence performance is improved

    Implicit Regularization in Over-Parameterized Support Vector Machine

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    In this paper, we design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method, and provide theoretical guarantees for the induced implicit regularization phenomenon. In particular, we construct an over-parameterized hinge loss function and estimate the true parameters by leveraging regularization-free gradient descent on this loss function. The utilization of Nesterov's method enhances the computational efficiency of our algorithm, especially in terms of determining the stopping criterion and reducing computational complexity. With appropriate choices of initialization, step size, and smoothness parameter, we demonstrate that unregularized gradient descent achieves a near-oracle statistical convergence rate. Additionally, we verify our theoretical findings through a variety of numerical experiments and compare the proposed method with explicit regularization. Our results illustrate the advantages of employing implicit regularization via gradient descent in conjunction with over-parameterization in sparse SVMs

    Translating Cognitive and Linguistic Metaphors in Popular Science: A Case Study of Scientific Discoveries

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    Since the cognitive turn in metaphor studies in the late 1970s, metaphor has been seen as a cognitive phenomenon reflecting how we think, alongside its classic role as a powerful literary device. This ā€˜cognitive turnā€™ in metaphor studies makes it possible to investigate metaphor in two facets: the cognitive one and the linguistic one. In this tenet, the notion of metaphor features two intertwined parts: conceptual metaphors which resemble mental connections between different knowledge packets (e.g., LIFE IS A BOOK), and their linguistic manifestations known as metaphorical expressions or linguistic metaphors (e.g., They are starting a new chapter of their life). This opens a window for metaphor translation research, for it allows researchers to examine metaphor translation from the two complementary facets. Building on conceptual metaphor theory (Lakoff and Johnson 1980, 2003) and conceptual blending theory (Fauconnier and Turner 2002), our case study discusses the translation of cognitive and linguistic metaphors identified in source and target texts. Metaphorical expressions were handpicked from seven popular cosmological articles published in Scientific American between 2017 and 2018, and their official Chinese translations published in Huanqiukexue (ā€˜global science,ā€™ Beijing) and Kexueren (ā€˜science person,ā€™ Taipei). The findings lend support to the joint application of two metaphor theories to descriptive translation studies, for it not only facilitates the analysis of translation examples but also enhances the feasibility of comparing metaphor translation research across languages pinned by metaphor parameters waiting to be explored

    Dynamically orienting your own face facilitates the automatic attraction of attention

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    We report two experiments showing that dynamically orienting our own face facilitates the automatic attraction of attention. We had participants complete a cueing task where they had to judge the orientation of a lateralized target cued by a central face that dynamically changed its orientation. Experiment 1 showed a reliable cueing effect from both self- and friend-faces at a long stimulus onset asynchrony (SOA), however, the self-faces exclusively generated a spatial cueing effect at a short SOA. In Experiment 2, event-related potential (ERP) data to the face cues showed larger amplitudes in the N1 component for self-faces relative to friend- and unfamiliar-faces. In contrast, the amplitude of the P3 component was reduced for self compared with friend- and unfamiliar-other cues. The size of the self-bias effect in N1 correlated with the strength of self-biases in P3. The results indicate that dynamic changes in the orientation of oneā€™s own face can provide a strong ecological cue for attention, enhancing sensory responses (N1) and reducing any subsequent uncertainty (P3) in decision-making

    Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation

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    Modeling and control of the human musculoskeletal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current human musculoskeletal models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model (MS-Human-700) with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. The musculoskeletal model, along with its control algorithm, will be made available to the research community to promote a deeper understanding of human motion control and better design of interactive robots. Project page: https://lnsgroup.cc/research/MS-Human-700Comment: ICRA 202
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