Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time

Optimizing the Long-Term Operating Plan of Railway Marshalling Station for Capacity Utilization Analysis

Not only is the operating plan the basis of organizing marshalling station’s operation, but it is also used to analyze in detail the capacity utilization of each facility in marshalling station. In this paper, a long-term operating plan is optimized mainly for capacity utilization analysis. Firstly, a model is developed to minimize railcars’ average staying time with the constraints of minimum time intervals, marshalling track capacity, and so forth. Secondly, an algorithm is designed to solve this model based on genetic algorithm (GA) and simulation method. It divides the plan of whole planning horizon into many subplans, and optimizes them with GA one by one in order to obtain a satisfactory plan with less computing time. Finally, some numeric examples are constructed to analyze (1) the convergence of the algorithm, (2) the effect of some algorithm parameters, and (3) the influence of arrival train flow on the algorithm

Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks

In this paper, we present a new approach for model acceleration by exploiting spatial sparsity in visual data. We observe that the final prediction in vision Transformers is only based on a subset of the most informative tokens, which is sufficient for accurate image recognition. Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input to accelerate vision Transformers. Specifically, we devise a lightweight prediction module to estimate the importance score of each token given the current features. The module is added to different layers to prune redundant tokens hierarchically. While the framework is inspired by our observation of the sparse attention in vision Transformers, we find the idea of adaptive and asymmetric computation can be a general solution for accelerating various architectures. We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more generic dynamic spatial sparsification framework with progressive sparsification and asymmetric computation for different spatial locations. By applying lightweight fast paths to less informative features and using more expressive slow paths to more important locations, we can maintain the structure of feature maps while significantly reducing the overall computations. Extensive experiments demonstrate the effectiveness of our framework on various modern architectures and different visual recognition tasks. Our results clearly demonstrate that dynamic spatial sparsification offers a new and more effective dimension for model acceleration. Code is available at https://github.com/raoyongming/DynamicViTComment: Accepted to T-PAMI. Journal version of our NeurIPS 2021 work: arXiv:2106.02034. Code is available at https://github.com/raoyongming/DynamicVi

UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models

Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., $<$10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256$\times$256 (conditional) with only 10 function evaluations. Code is available at https://github.com/wl-zhao/UniPC.Comment: Accepted by NeurIPS 2023. Project page: https://unipc.ivg-research.xy

DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to \url{https://sstzal.github.io/DiffTalk/}.Comment: Project page https://sstzal.github.io/DiffTalk

Integrated Optimization of Service-Oriented Train Plan and Schedule on Intercity Rail Network with Varying Demand

For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results

Reliability analysis of all components in structural systems based on adaptive point estimate method and the principle of maximum entropy

Date: May 14 (Mon), 2018Place: ROHM Plaza Meeting Room, Kyoto University Katsura Campus, Kyoto, JAPANSupported by JSPS-NSFC Japan-China Scientific Cooperation ProjectOrganized by Structural Engineering of Buildings Laboratory, Department of Architecture and Architectural Engineering, Kyoto Universit

FACTORS FOR CHOOSING OF INVESTMENT MODELS BY ASIAN COMPANIES IN THE IMPLEMENTATION AREA OF GLOBAL BUSINESS INITIATIVES

The development of the community of the common destiny of mankind has great investment potential, the full disclosure of which depends on the effective cooperation not only of states but also of the corporate sector, especially in a cross-border format. The article argues for the need to develop the internal potential of Asian companies against the background of strengthening international economic ties and improving the investment climate of countries participating in investment processes. The example of China's investment activity in Asian countries shows that, despite global crisis phenomena and upheavals, a grandiose potential has been formed for the implementation of important global initiatives, the most powerful of which scientists, practitioners and even politicians rightly consider the "One Belt One Road" project. Large-scale foreign investments and the choice of appropriate methods of cross-border entry into markets of potential investment interest have a decisive influence on the success of large Chinese enterprises in the context of the development and implementation of this project. At the same time, the authors emphasize the choice of such methods of investment interaction as mergers and acquisitions (M&A) or greenfield investments. Based on the use of economic and mathematical modelling, the authors demonstrated the influence of internal and external factors on the choice of investment method by large Chinese companies in countries that are promising partners in the implementation of China's global initiative "One Belt One Road". The analysis of the potential for investment begins with the division of factors into two conditional groups, on the one hand, it is about the intra-corporate potential in the composition of the factors of strategic assets, technical capabilities, international experience, capabilities of enterprise management and the scale of the enterprise. On the other hand, the external environment is taken into account, the analytical assessment of which is based on the index of infrastructure development of the host country, control over capital and the value of cultural distance. Considering the research interest, as part of the analysis of the investment potential for cross-border interaction, such countries as India, Indonesia, Pakistan, Kazakhstan and Vietnam along the route were selected. Through the quantitative analysis of 108 investment projects in 6 countries, a benchmark for the entry of foreign investment by Chinese large enterprises through mergers and acquisitions (M&A) or greenfield investment has been obtained

Dirac quantum spin liquid emerging in a kagome-lattice antiferromagnet

Emerging quasi-particles with Dirac dispersion in condensed matter physics are analogous to their cousins in high-energy physics in that both of them can be described by the Dirac equation for relativistic electrons. Recently, these Dirac fermions have been widely found in electronic systems, such as graphene and topological insulators. At the conceptual level, since the charge is not a prerequisite for Dirac fermions, the emergence of Dirac fermions without charge degree of freedom has been theoretically predicted to be realized in Dirac quantum spin liquids. In such case, the Dirac quasiparticles are charge-neutral and carry a spin of 1/2, known as spinons. Despite of theoretical aspirations, spectra evidence of Dirac spinons remains elusive. Here we show that the spin excitations of a kagome antiferromagnet, YCu$_3$(OD)$_6$Br$_2$[Br$_{x}$(OD)$_{1-x}$], are conical with a spin continuum inside, which are consistent with the convolution of two Dirac spinons. The spinon velocity obtained from the spin excitations also quantitatively reproduces the low-temperature specific heat of the sample. Interestingly, the locations of the conical spin excitations differ from those calculated by the nearest neighbor Heisenberg model, suggesting an unexpected origin of the Dirac spinons. Our results thus provide strong spectra evidence for the Dirac quantum-spin-liquid state emerging in this kagome-lattice antiferromagnet.Comment: 7 pages, 4 figure