MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as robustly merging the sub-maps built by the agents. We take the view that the existing approaches are either aggressive or conservative: Aggressive strategies merge two sub-maps built by different agents together when overlap is detected, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. Conservative strategies direct one agent to revisit an excessive amount of the historical trajectory of another agent for verification before merging, which can lower the exploration efficiency due to the repeated exploration of the same space. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a \emph{cooperative} manner by planning for agents with merged sub-maps together to further improve exploration efficiency. Our experiments show that our approach is up to 50\% more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA

Seawater nutrient and chlorophyll α distributions near the Great Wall Station, Antarctica

We examined the influences upon nutrient, temperature, salinity and chlorophyll a distributions in Great Wall Cove (GWC) and Ardley Cove (AC), near the Chinese Antarctic Great Wall Station, using measurements taken in January 2013 and other recent data. Nutrient concentrations were high, with phosphate concentrations of 1.94 (GWC) and 1.96 (AC) μmol·L−1, DIN(dissolved inorganic nitrogen) concentrations of 26.36 (GWC) and 25.94 (AC) μmol·L−1 and silicate concentrations of 78.6 (GWC) and 79.3 (AC) μmol·L−1. However, average concentrations of chlorophyll a were low (1.29 μg·L−1, GWC and 1.08 μg·L−1, AC), indicating that this region is a high-nutrient and low-chlorophyll (HNLC) area. Nutrient concentrations of freshwater (stream and snowmelt) discharge into GWC and AC in the austral summer are low, meaning freshwater discharge dilutes the nutrient concentrations in the two coves. Strong intrusion of nutrient-rich water from the Bransfield Current in the south was the main source of nutrients in GWC and AC. Low water temperature and strong wind-induced turbulence and instability in the upper layers of the water column were the two main factors that caused the low phytoplankton biomass during the austral summer

Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model

Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate inference. Currently, the widely used models such as Bi-encoder and Cross-encoder have their limitations in accuracy or inference speed respectively. In this work, we propose a novel model called the Entity-Based Relevance Model (EBRM). We identify the entities contained in an item and decompose the QI (query-item) relevance problem into multiple QE (query-entity) relevance problems; we then aggregate their results to form the QI prediction using a soft logic formulation. The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy as well as cache QE predictions for fast online inference. Utilizing soft logic makes the prediction procedure interpretable and intervenable. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance. The proposed method is evaluated on labeled data from e-commerce websites. Empirical results show that it achieves promising improvements with computation efficiency

The Function of MoGlk1 in Integration of Glucose and Ammonium Utilization in Magnaporthe oryzae

Hexokinases are conserved proteins functioning in glucose sensing and signaling. The rice blast fungus Magnaporthe oryzae contains several hexokinases, including MoHxk1 (hexokinase) and MoGlk1 (glucokinase) encoded respectively by MoHXK1 and MoGLK1 genes. The heterologous expression of MoGlk1 and MoHxk1 in Saccharomyces cerevisiae confirmed their conserved functions. Disruption of MoHXK1 resulted in growth reduction in medium containing fructose as the sole carbon source, whereas disruption of MoGLK1 did not cause the similar defect. However, the ΔMoglk1 mutant displayed decreased proton extrusion and a lower biomass in the presence of ammonium, suggesting a decline in the utilization of ammonium. Additionally, the MoGLK1 allele lacking catalytic activity restored growth to the ΔMoglk1 mutant. Moreover, the expression of MoPMA1 encoding a plasma membrane H+-ATPase decreased in the ΔMoglk1 mutant that can be suppressed by glucose and G-6-P. Thus, MoGlk1, but not MoHxk1, regulates ammonium utilization through a mechanism that is independent from its catalytic activity

Heuristic Search for the Orienteering Problem with Time-Varying Reward

The Orienteering Problem (OP) seeks a path on a graph to maximize total rewards collected subject to a path length budget. Typically, a reward is achieved by visiting a vertex in the graph, and such a reward is constant for all time. This paper considers a variant of OP where the reward of each vertex is an arbitrary time-dependent function, and hence the name time-varying reward OP (TR-OP). To solve this problem, we develop a novel heuristic search algorithm called Reward Maximization A* (RMA*), which is guaranteed to find an optimal solution to TR-OP. We also develop a fast method to compute an admissible heuristic for RMA* that can effectively direct the search to save computational effort. Furthermore, we introduce a hyper-parameter in RMA* that trades off between solution quality and runtime efficiency for RMA*. We benchmark RMA* against a recent dynamic programming (DP) approach, which runs fast in practice, but has no guarantee of the solution optimality. In our tests, RMA* reduces the runtime by up to 70% compared to DP. By adjusting the hyper-parameter, RMA* is able to find solutions with up to 30% more rewards than those found by DP

Additional file 3 of A comprehensive assessment of fungal communities in various habitats from an ice-free area of maritime Antarctica: diversity, distribution, and ecological trait

Additional file 3. Table S3. Information on the 17,236 fungal ASVs in the 213 samples collected from the Fildes Region (maritime Antarctica)

Additional file 1 of A comprehensive assessment of fungal communities in various habitats from an ice-free area of maritime Antarctica: diversity, distribution, and ecological trait

Additional file 1. Table S1. Information on the 213 samples collected from the Fildes Region (maritime Antarctica). Table S5. An overview of potentially pathogenic fungi found in the eleven habitats from the Fildes Region (maritime Antarctica). Fig. S1. Dendrogram showing fungal communities in the 202 samples of eleven habitats from the Fildes Region (maritime Antarctica). Fig. S2. LEfSe analysis showing the fungal phyla that are significantly different among the eleven habitats in the Fildes Region (maritime Antarctica). Significant phyla are ranked by their LDA scores (x-axis). The right heatmap shows whether the relative abundances of phyla are higher (red) or lower (blue). Fig. S3. LEfSe analysis showing the fungal classes that are significantly different among the eleven habitats in the Fildes Region (maritime Antarctica). Significant classes are ranked by their LDA scores (x-axis). The right heatmap shows whether the relative abundances of classes are higher (red) or lower (blue). Fig. S4. LEfSe analysis showing the fungal families that are significantly different among the eleven habitats in the Fildes Region (maritime Antarctica). Significant families are ranked by their LDA scores (x-axis). The right heatmap shows whether the relative abundances of families are higher (red) or lower (blue)

Additional file 2 of A comprehensive assessment of fungal communities in various habitats from an ice-free area of maritime Antarctica: diversity, distribution, and ecological trait

Additional file 2. Table S2. Representative sequence of fungal ASV detected in the 213 samples

Additional file 4 of A comprehensive assessment of fungal communities in various habitats from an ice-free area of maritime Antarctica: diversity, distribution, and ecological trait

Additional file 4. Table S4. Information on the 14,814 fungal ASVs in the 202 samples collected from the Fildes Region (maritime Antarctica)