Multimodal-Transport Collaborative Evacuation Strategies for Urban Serious Emergency Incidents Based on Multi-Sources Spatiotemporal Data (Short Paper)

When serious emergency events happen in metropolitan cities where pedestrians and vehicles are in high-density, single modal-transport cannot meet the requirements of quick evacuations. Existing mixed modes of transportation lacks spatiotemporal collaborative ability, which cannot work together to accomplish evacuation tasks in a safe and efficient way. It is of great scientific significance and application value for emergency response to adopt multimodal-transport evacuations and improve their spatial-temporal collaboration ability. However, multimodal-transport evacuation strategies for urban serious emergency event are great challenge to be solved. The reasons lie in that: (1) large-scale urban emergency environment are extremely complicated involving many geographical elements (e.g., road, buildings, over-pass, square, hydrographic net, etc.); (2) Evacuated objects are dynamic and hard to be predicted. (3) the distributions of pedestrians and vehicles are unknown. To such issues, this paper reveals both collaborative and competitive mechanisms of multimodal-transport, and further makes global optimal evacuation strategies from the macro-optimization perspective. Considering detailed geographical environment, pedestrian, vehicle and urban rail transit, a multi-objective multi-dynamic-constraints optimization model for multimodal-transport collaborative emergency evacuation is constructed. Take crowd incidents in Shenzhen as example, empirical experiments with real-world data are conducted to evaluate the evacuation strategies and path planning. It is expected to obtain innovative research achievements on theory and method of urban emergency evacuation in serious emergency events. Moreover, this research results provide spatial-temporal decision support for urban emergency response, which is benefit to constructing smart and safe cities

Emergent quantum probability from full quantum dynamics and the role of energy conservation

We propose and study a toy model for the quantum measurements that yield the Born's rule of quantum probability. In this model, the electrons interact with local photon modes and the photon modes are dissipatively coupled with local photon reservoirs. We treat the interactions of the electrons and photons with full quantum mechanical description, while the dissipative dynamics of the photon modes are treated via the Lindblad master equation. By assigning double quantum dot setup for the electrons coupling with local photons and photonic reservoirs, we show that the Born's rule of quantum probability can emerge directly from microscopic quantum dynamics. We further discuss how the microscopic quantities such as the electron-photon couplings, detuning, and photon dissipation rate determine the quantum dynamics. Surprisingly, in the infinite long time measurement limit, the energy conservation already dictates the emergence of the Born's rule of quantum probability. For finite-time measurement, the local photon dissipation rate determines the characteristic time-scale for the completion of the measurement, while other microscopic quantities affect the measurement dynamics. Therefore, in genuine measurements, the measured probability is determined by both the local devices and the quantum mechanical wavefunction.Comment: 8 pages, 4 figure

Linear profile decompositions for a family of fourth order Schr\"odinger equations

We establish linear profile decompositions for the fourth order Schr\"odinger equation and for certain fourth order perturbations of the Schr\"odinger equation, in dimensions greater than or equal to two. We apply these results to prove dichotomy results on the existence of extremizers for the associated Stein--Tomas/Strichartz inequalities; along the way, we also obtain lower bounds for the norms of these operators.Comment: 25 pages. The proof of the old Theorem 7 is clarified and references update

Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem

In this paper, we study a class of stochastic bilevel optimization problems, also known as stochastic simple bilevel optimization, where we minimize a smooth stochastic objective function over the optimal solution set of another stochastic convex optimization problem. We introduce novel stochastic bilevel optimization methods that locally approximate the solution set of the lower-level problem via a stochastic cutting plane, and then run a conditional gradient update with variance reduction techniques to control the error induced by using stochastic gradients. For the case that the upper-level function is convex, our method requires $\tilde{\mathcal{O}}(\max\{1/\epsilon_f^{2},1/\epsilon_g^{2}\})$ stochastic oracle queries to obtain a solution that is $\epsilon_f$-optimal for the upper-level and $\epsilon_g$-optimal for the lower-level. This guarantee improves the previous best-known complexity of $\mathcal{O}(\max\{1/\epsilon_f^{4},1/\epsilon_g^{4}\})$. Moreover, for the case that the upper-level function is non-convex, our method requires at most $\tilde{\mathcal{O}}(\max\{1/\epsilon_f^{3},1/\epsilon_g^{3}\})$ stochastic oracle queries to find an $(\epsilon_f, \epsilon_g)$-stationary point. In the finite-sum setting, we show that the number of stochastic oracle calls required by our method are $\tilde{\mathcal{O}}(\sqrt{n}/\epsilon)$ and $\tilde{\mathcal{O}}(\sqrt{n}/\epsilon^{2})$ for the convex and non-convex settings, respectively, where $\epsilon=\min \{\epsilon_f,\epsilon_g\}$

Multitask quantum thermal machines and cooperative effects

Including phonon-assisted inelastic process in thermoelectric devices is able to enhance the performance of nonequilibrium work extraction. In this work, we demonstrate that inelastic phonon-thermoelectric devices have a fertile functionality diagram, where particle current and phononic heat currents are coupled and fueled by chemical potential difference. Such devices can simultaneously perform multiple tasks, e.g., heat engines, refrigerators, and heat pumps. Guided by the entropy production, we mainly study the efficiencies and coefficients of performance of multitask quantum thermal machines, where the roles of the inelastic scattering process and multiple biases in multiterminal setups are emphasized. Specifically, in a three-terminal double-quantum-dot setup with a tunable gate, we show that it efficiently performs two useful tasks due to the phonon-assisted inelastic process. Moreover, the cooperation between the longitudinal and transverse thermoelectric effects in the three-terminal thermoelectric systems leads to markedly improved performance of the thermal machines. While for the four-terminal four-quantum-dot thermoelectric setup, we find that additional thermodynamic affinity furnishes the system with both enriched functionality and enhanced efficiency. Our work provides insights into optimizing phonon-thermoelectric devices.Comment: 14 pages, 7 figure