CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decisionmaking task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multihead attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of citywide hybrid order dispatching and fleet management over strong baselines

Multistage Random Growing Small-World Networks with Power-law degree Distribution

In this paper, a simply rule that generates scale-free networks with very large clustering coefficient and very small average distance is presented. These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the adding process of a new node to the network is composed of two stages. The analytic results of power-law exponent $\gamma=3$ and clustering coefficient $C=0.81$ are obtained, which agree with the simulation results approximately. In addition, the average distance of the networks increases logarithmical with the number of the network vertices is proved analytically. Since many real-life networks are both scale-free and small-world networks, MRGN may perform well in mimicking reality.Comment: 3 figures, 4 page

Neurological complications during the Omicron COVID-19 wave in China: A cohort study

Background and purpose: The aim was to investigate the neurological complications associated with coronavirus disease 19 (COVID-19) during the 2022 Omicron wave. Methods and analysis: The medical records of a cohort of people admitted to neurological wards of three participating tertiary centres in Sichuan from 12 December 2022 to 12 January 2023 were reviewed. Demographics and clinical data were obtained and analysed with an interest in COVID-19-related new-onset or worse neurological symptoms. The current data were also compared in two centres with similar data from the same period 12 months earlier. Results: In all, 790 people were enrolled, of whom 436 were positive for COVID-19. Ninety-nine had new onset COVID-related neurological problems, or their known neurological condition deteriorated during the wave. There was a significant difference in demographics from the findings amongst admissions 12 months earlier as there was an increase in the average age, the incidence of encephalitis and encephalopathy, and mortality rates. One hundred and one received COVID-specific antivirals, intravenous glucocorticoids and intravenous immunoglobulin therapy. No differences were seen between these and those who did not use them. Conclusion: New-onset neurological conditions, particularly encephalitis and encephalopathy, increased significantly during this period. Deterioration of existing neurological conditions, such as seizure exacerbation, was also observed. A large-scale treatment trial of people with COVID-19 infection presenting with neurological disorders is still needed

A comparison of nitrogen utilization and urea metabolism between Tibetan and fine-wool sheep

Citation: Zhou, J. W., Mi, J. D., Titgemeyer, E. C., Guo, X. S., Ding, L. M., Wang, H. C., . . . Long, R. J. (2015). A comparison of nitrogen utilization and urea metabolism between Tibetan and fine-wool sheep. Journal of Animal Science, 93(6), 3006-3017. doi:10.2527/jas2014-8865To study metabolic adaptation to harsh foraging conditions, an experiment was conducted to characterize and quantify N utilization efficiency and urea metabolism in Tibetan and fine-wool sheep fed 4 levels of dietary N (11.0, 16.7, 23.1, and 29.2 g N/kg DM) in 2 concurrent 4 x 4 Latin square designs. Urea kinetics were determined using continuous intrajugular infusions of (NN)-N-15-N-15-urea. Urinary excretions of total N and urea N increased linearly (P = 0.37). Fecal N excretion increased with dietary N for Tibetan sheep but not for fine-wool sheep (linear dietary N x breed; P < 0.05). Nitrogen retention (both amount per day and percentage of N intake) increased with increasing dietary N concentration (P < 0.001), and the rates of increase were greater in fine-wool than in Tibetan sheep (linear dietary N x breed and cubic dietary N x breed; P < 0.05). In Tibetan sheep, N retention as a percentage of intake was greatest for diets containing 16.7 g N/kg DM, whereas it was maximal for fine-wool sheep when the diet contained 23.1 g N/kg DM. Urea N entry rate, urea N recycled to the gastrointestinal tract (GIT), and urea N returned to the ornithine cycle all increased with dietary N (P < 0.05), and all were greater in Tibetan than fine-wool sheep for the 11.0 g N/kg DM diet but were greater in fine-wool than Tibetan sheep for the diet with 29.2 g N/kg DM (linear dietary N x breed; P < 0.05). Urea N excreted in feces, both amount and fraction of GIT entry rate, was less in Tibetan than finewool sheep for the 11.0 and 16.7 g N/kg DM diets but similar for diets with 23.1 or 29.2 g N/kg DM (linear dietary N x breed; P < 0.01). For the lowest-protein diet, the fraction of urea N production recycled to the GIT was greater in the Tibetan than fine-wool sheep (88% vs. 82%), but for the diet with 29.2 g N/kg DM it was greater for fine-wool than Tibetan sheep (46% vs. 39%; linear dietary N x breed; P < 0.05). Plasma urea N increased more rapidly in response to increasing dietary N concentration for fine-wool sheep than for Tibetan sheep (linear dietary N x breed; P < 0.05). Urea tubular load and the amount and percentage of urea reabsorbed by the kidney were greater in Tibetan than fine-wool sheep (P < 0.05). These results suggest that Tibetan sheep have mechanisms that allow them to utilize N more efficiently than the fine-wool sheep when dietary N is inadequate

A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

EVM and Achievable Data Rate Analysis of Clipped OFDM Signals in Visible Light Communication

Orthogonal frequency division multiplexing (OFDM) has been considered for visible light communication (VLC) thanks to its ability to boost data rates as well as its robustness against frequency-selective fading channels. A major disadvantage of OFDM is the large dynamic range of its time-domain waveforms, making OFDM vulnerable to nonlinearity of light emitting diodes (LEDs). DC biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are two popular OFDM techniques developed for the VLC. In this paper, we will analyze the performance of the DCO-OFDM and ACO-OFDM signals in terms of error vector magnitude (EVM), signal-to-distortion ratio (SDR), and achievable data rates under both average optical power and dynamic optical power constraints. EVM is a commonly used metric to characterize distortions. We will describe an approach to numerically calculate the EVM for DCO-OFDM and ACO-OFDM. We will derive the optimum biasing ratio in the sense of minimizing EVM for DCO-OFDM. Additionally, we will formulate the EVM minimization problem as a convex linear optimization problem and obtain an EVM lower bound against which to compare the DCO-OFDM and ACO-OFDM techniques. We will prove that the ACO-OFDM can achieve the lower bound. Average optical power and dynamic optical power are two main constraints in VLC. We will derive the achievable data rates under these two constraints for both additive white Gaussian noise (AWGN) channel and frequency-selective channel. We will compare the performance of DCO-OFDM and ACO-OFDM under different power constraint scenarios

Constraints on Spin-Independent Nucleus Scattering with sub-GeV Weakly Interacting Massive Particle Dark Matter from the CDEX-1B Experiment at the China Jin-Ping Laboratory

We report results on the searches of weakly interacting massive particles (WIMPs) with sub-GeV masses ($m_{\chi}$) via WIMP-nucleus spin-independent scattering with Migdal effect incorporated. Analysis on time-integrated (TI) and annual modulation (AM) effects on CDEX-1B data are performed, with 737.1 kg$\cdot$day exposure and 160 eVee threshold for TI analysis, and 1107.5 kg$\cdot$day exposure and 250 eVee threshold for AM analysis. The sensitive windows in $m_{\chi}$ are expanded by an order of magnitude to lower DM masses with Migdal effect incorporated. New limits on $\sigma_{\chi N}^{\rm SI}$ at 90\% confidence level are derived as $2\times$10$^{-32}\sim7\times$10$^{-35}$ $\rm cm^2$ for TI analysis at $m_{\chi}\sim$ 50$-$180 MeV/$c^2$, and $3\times$10$^{-32}\sim9\times$10$^{-38}$ $\rm cm^2$ for AM analysis at $m_{\chi}\sim$75 MeV/$c^2-$3.0 GeV/$c^2$.Comment: 5 pages, 4 figure

Tantalum disulfide quantum dots: preparation, structure, and properties

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