Deep attentive video summarization with distribution consistency learning

This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework, in which the input and output are sequences of original video frames and their predicted importance scores, respectively. Two critical issues are addressed in this article: short-term contextual attention insufficiency and distribution inconsistency. The former lies in the insufficiency of capturing the short-term contextual attention information within the video sequence itself since the existing approaches focus a lot on the long-term encoder-decoder attention. The latter refers to the distributions of predicted importance score sequence and the ground-truth sequence is inconsistent, which may lead to a suboptimal solution. To better mitigate the first issue, we incorporate a self-attention mechanism in the encoder to highlight the important keyframes in a short-term context. The proposed approach alongside the encoder-decoder attention constitutes our deep attentive models for video summarization. For the second one, we propose a distribution consistency learning method by employing a simple yet effective regularization loss term, which seeks a consistent distribution for the two sequences. Our final approach is dubbed as Attentive and Distribution consistent video Summarization (ADSum). Extensive experiments on benchmark data sets demonstrate the superiority of the proposed ADSum approach against state-of-the-art approaches

Long-term in situ observations on typhoon-triggered turbidity currents in the deep sea

This work is supported by the National Science Foundation of China (grants 91528304, 41576005, and 41530964). We thank J. Li, X. Lyu, P. Li, K. Duan, J. Ronan, Y. Wang, P. Ma, and Y. Li for cruise assistance; G. de Lange and J. Hinojosa for editing an early version of manuscript; and E. Pope and two anonymous reviewers for their reviews.Peer reviewedPublisher PD

Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS) involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA) for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS

Study Of Conceptual Design Of The Extension Method For Mechanical Products,

ABSTRACT On the foundation summing up existing intelligent conceptual design method, this paper puts forward the research content, characters, path, and method of the conceptual design of extension for mechanical products. This paper rounds the core technology of intelligent conceptual design to research the modeling method of extension design in function-principlelayout-configuration. It includes the function expression, function decomposition and synthesis, function illation and decision. The computers are utilized to simulate the human dialectic thought when resolve problems in this method. The given example shows that the extension method has been applied in the field of conceptual design for mechanical products. This method has important significance to resolve the bottleneck problem of theory studying and engineering realizing of intelligent CAD

Conflict resolution for product performance requirements based on propagation analysis in the extension theory

Traditional product data mining methods are mainly focused on the static data. Performance requirements are generally met as possible by finding some cases and changing their structures. However, when one is satisfied with the structures changed, the other effects are not taken into account by analyzing the correlations; that is, design conflicts are not identified and resolved. An approach to resolving the conflict problems is proposed based on propagation analysis in Extension Theory. Firstly, the extension distance is improved to better fit evaluating the similarity among cases, then, a case retrieval method is developed. Secondly, the transformations that can be made on selected cases are formulated by understanding the conflict natures in the different performance requirements, which leads to the extension transformation strategy development for coordinating conflicts using propagation analysis. Thirdly, the effects and levels of propagation are determined by analyzing the performance values before and after the transformations, thus the co-existing conflict coordination strategy of multiple performances is developed. The method has been implemented in a working prototype system for supporting decision-making. And it has been demonstrated the feasible and effective through resolving the conflicts of noise, exhaust, weight and intake pressure for the screw air compressor performance design

Measurement method of torsional vibration signal to extract gear meshing characteristics

A technique in measuring torsional vibration signal based on an optical encoder and a discrete wavelet transform is proposed for the extraction of gear meshing characteristics. The method measures the rotation angles of the input and output shafts of a gear pair by using two optical encoders and obtains the time interval sequences of the two shafts. By spline interpolation, the time interval sequences based on uniform angle sampling can be converted into angle interval sequences on the basis of uniform time sampling. The curve of the relative displacement of the gear pair on the meshing line (initial torsional vibration signal) can then be obtained by comparing the rotation angles of the input and output shafts at the interpolated time series. The initial torsional vibration signal is often disturbed by noise. Therefore, a discrete wavelet transform is used to decompose the signal at certain scales; the torsional vibration signal of the gear can then be obtained after filtering. The proposed method was verified by simulation and experimentation, and the results showed that the method could successfully obtain the torsional vibration signal of the gear at a high frequency. The waveforms of the torsional vibration could reflect the meshing characteristics of the teeth. These findings could provide a basis for fault diagnosis of gears

Pushing the Limits of 3D Shape Generation at Scale

We present a significant breakthrough in 3D shape generation by scaling it to unprecedented dimensions. Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3.6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D. Our approach addresses the limitations of existing methods by enhancing the quality and diversity of generated 3D shapes. To tackle the challenges of high-resolution 3D shape generation, our model incorporates tri-plane features as latent representations, effectively reducing computational complexity. Additionally, we introduce a discrete codebook for efficient quantization of these representations. Leveraging the power of transformers, we enable multi-modal conditional generation, facilitating the production of diverse and visually impressive 3D shapes. To train our expansive model, we leverage an ensemble of publicly-available 3D datasets, consisting of a comprehensive collection of approximately 900,000 objects from renowned repositories such as ModelNet40, ShapeNet, Pix3D, 3D-Future, and Objaverse. This diverse dataset empowers our model to learn from a wide range of object variations, bolstering its ability to generate high-quality and diverse 3D shapes. Extensive experimentation demonstrate the remarkable efficacy of our approach in significantly improving the visual quality of generated 3D shapes. By pushing the boundaries of 3D generation, introducing novel methods for latent representation learning, and harnessing the power of transformers for multi-modal conditional generation, our contributions pave the way for substantial advancements in the field. Our work unlocks new possibilities for applications in gaming, virtual reality, product design, and other domains that demand high-quality and diverse 3D objects.Comment: Project page: https://argus-3d.github.io