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

How to optimally dispatch orders to vehicles and how to tradeoff 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 decision-making 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 multi-head 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 city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Hierarchical Catalogue Generation for Literature Review: A Benchmark

Multi-document scientific summarization can extract and organize important information from an abundant collection of papers, arousing widespread attention recently. However, existing efforts focus on producing lengthy overviews lacking a clear and logical hierarchy. To alleviate this problem, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review (HiCatGLR), which aims to generate a hierarchical catalogue for a review paper given various references. We carefully construct a novel English Hierarchical Catalogues of Literature Reviews Dataset (HiCaD) with 13.8k literature review catalogues and 120k reference papers, where we benchmark diverse experiments via the end-to-end and pipeline methods. To accurately assess the model performance, we design evaluation metrics for similarity to ground truth from semantics and structure. Besides, our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. Furthermore, we discuss potential directions for this task to motivate future research

On-line detection of spherical sensor for inrush current detection

With the exploration and demand for the field of marine, underwater vehicles are used in deep water widely, however, there is a lack of research about underwater vehicles which are applied in shallow wary water. When underwater vehicles working in shallow wary water, they will be affected by the inrush current effect of shallow waters, so the research of underwater vehicles about anti current control becomes a meaningful item. But due to the high cost of underwater work, simulation and analysis to the inrush current online detection mechanism first, to determine the effects of surge phenomenon of underwater vehicles. Electromotor drives propeller to generate the flow, the detection mechanism is subjected to the impact of all directions. For some mechanical analysis of the inrush current online detection mechanism and analyzing the influence on the water inrush current when it moved in this paper, so we can reduce the effects of water inrush current on underwater vehicles in the subsequent experiments

Breast cancer survival in the US and Europe: a CONCORD high-resolution study.

Breast cancer survival is reportedly higher in the US than in Europe. The first worldwide study (CONCORD) found wide international differences in age-standardized survival. The aim of this study is to explain these survival differences. Population-based data on stage at diagnosis, diagnostic procedures, treatment and follow-up were collected for about 20,000 women diagnosed with breast cancer aged 15-99 years during 1996-98 in 7 US states and 12 European countries. Age-standardized net survival and the excess hazard of death up to 5 years after diagnosis were estimated by jurisdiction (registry, country, European region), age and stage with flexible parametric models. Breast cancers were generally less advanced in the US than in Europe. Stage also varied less between US states than between European jurisdictions. Early, node-negative tumors were more frequent in the US (39%) than in Europe (32%), while locally advanced tumors were twice as frequent in Europe (8%), and metastatic tumors of similar frequency (5-6%). Net survival in Northern, Western and Southern Europe (81-84%) was similar to that in the US (84%), but lower in Eastern Europe (69%). For the first 3 years after diagnosis the mean excess hazard was higher in Eastern Europe than elsewhere: the difference was most marked for women aged 70-99 years, and mainly confined to women with locally advanced or metastatic tumors. Differences in breast cancer survival between Europe and the US in the late 1990s were mainly explained by lower survival in Eastern Europe, where low healthcare expenditure may have constrained the quality of treatment

SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills

Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, which enables a single model to tackle many different tasks from different languages. To this end, we define several language-specific skills and task-specific skills, each of which corresponds to a skill module. SkillNet-X sparsely activates parts of the skill modules which are relevant either to the target task or the target language. Acting as knowledge transit hubs, skill modules are capable of absorbing task-related knowledge and language-related knowledge consecutively. Based on Transformer, we modify the multi-head attention layer and the feed forward network layer to accommodate skill modules. We evaluate SkillNet-X on eleven natural language understanding datasets in four languages. Results show that SkillNet-X performs better than task-specific baselines and two multitask learning baselines (i.e., dense joint model and Mixture-of-Experts model). Furthermore, skill pre-training further improves the performance of SkillNet-X on almost all datasets. To investigate the generalization of our model, we conduct experiments on two new tasks and find that SkillNet-X significantly outperforms baselines

Design and implementation of an automatic nursing assessment system based on CDSS technology

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones.METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation.RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s.CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.</p

WebLab: a data-centric, knowledge-sharing bioinformatic platform

With the rapid progress of biological research, great demands are proposed for integrative knowledge-sharing systems to efficiently support collaboration of biological researchers from various fields. To fulfill such requirements, we have developed a data-centric knowledge-sharing platform WebLab for biologists to fetch, analyze, manipulate and share data under an intuitive web interface. Dedicated space is provided for users to store their input data and analysis results. Users can upload local data or fetch public data from remote databases, and then perform analysis using more than 260 integrated bioinformatic tools. These tools can be further organized as customized analysis workflows to accomplish complex tasks automatically. In addition to conventional biological data, WebLab also provides rich supports for scientific literatures, such as searching against full text of uploaded literatures and exporting citations into various well-known citation managers such as EndNote and BibTex. To facilitate team work among colleagues, WebLab provides a powerful and flexible sharing mechanism, which allows users to share input data, analysis results, scientific literatures and customized workflows to specified users or groups with sophisticated privilege settings. WebLab is publicly available at http://weblab.cbi.pku.edu.cn, with all source code released as Free Software

Usage Pattern-Driven Dynamic Data Layout Reorganization:

As scientific simulations and experiments move toward extremely large scales and generate massive amounts of data, the data access performance of analytic applications becomes crucial. A mismatch often happens between write and read patterns of data accesses, typically resulting in poor read performance. Data layout reorganization has been used to improve the locality of data accesses. However, current data reorganizations are static and focus on generating a single (or set of) optimized layouts that rely on prior knowledge of exact future access patterns. We propose a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern. This framework supports reading individual elements and chunks of a multi-dimensional array of variables. Our pattern-driven layout selection strategy achieves multi-fold speedups compared to reading from the original dataset

Physiological Characteristics and Transcriptomic Analysis of Saccharomyces cerevisiae under Carvacrol Stress

This study investigated the effect of carvacrol stress on the physiological characteristics and transcriptome of Saccharomyces cerevisiae, a common spoilage yeast in fruit and vegetable products. The results showed that the minimum inhibitory concentration (MIC) of carvacrol on S. cerevisiae was 160 mg/mL. With the increase in temperature (20–40 ℃), the inhibitory effect of carvacrol (160 mg/mL) on yeast growth gradually increased. Environmental pH (3–4.5) had no significant impact on the inhibitory effect of carvacrol. However, the addition of fructose (20–80 g/L) significantly reduced the antifungal activity. Carvacrol treatment damaged the structure of the cell envelope of S. cerevisiae, increased the plasma membrane permeability and caused plasma membrane depolarization, resulting in leakage of intracellular substances, a decrease in mitochondrial membrane potential, an imbalance in intracellular Ca2＋ homeostasis, and a decrease in intracellular ATP content. The transcriptomic analysis revealed that carvacrol stress inhibited the biosynthesis of mitochondrial respiratory chain complexes, ATP synthase and mitochondrial ribosome (MR) proteins, which could block electron transport along the respiratory chain and interfere with life activities related to MR