The aeromagnetic compensation method is critical for mitigating magnetic interference in airborne geophysical surveys. In the case of complex magnetic interference, the model-driven linear regression method based on the Toles-Lawson (T-L) model cannot guarantee compensation accuracy. Additionally, purely data-driven methods often require large datasets and lack interpretability. Recent hybrid solutions, particularly Physics-Informed Neural Network (PINN), effectively combine the advantages of model-driven and data-driven methods. However, PINN is heavily reliant on manual architecture tuning, which severely limits optimization efficiency and compensation accuracy. To address this issue, this letter proposes a Differentiable Architecture Search-Guided Physics-Informed Neural Network (DARTS-PINN) method. Experimental results demonstrate that DARTS-PINN can effectively mitigate magnetic interference, improve optimization efficiency, and substantially reduce data dependence. For aeromagnetic compensation of magnetic sensors fixed inside the cabin, the root mean square error (RMSE) of DARTS-PINN is 1.089 nT. In contrast, the optimal RMSEs of the model-driven and data-driven methods are 6.195 nT and 2.026 nT, respectively.

Energy harvesting act as one of the promising techniques to provide sustainable energy for self-powered sensor nodes by converting environmental energy into electricity. Energy harvested from single micro-energy source suffers from low power density and vulnerable to environmental changes, while the hybrid energy harvesting system can supply energy sustainably. However, cumulative power from various energy sources leads to a challenge of low energy conversion efficiency. In this paper, a Combined Maximum Power Point Tracking (Com-MPPT) method for hybrid energy harvesting is designed to improve the overall harvesting efficiency. By analyzing output characteristic curves under different environmental states, a mathematic model for hybrid energy harvesting system is proposed. It is found that with the change of environmental factors, multiple power peaks exist with varying voltage on the load, where traditional MPPT method for single energy source is no longer applicable. Therefore, the proposed Com-MPPT algorithm based on Search skip and linear extrapolation proposed in this paper can quickly search for the global maximum power peak in the case of multiple power peaks in hybrid energy collection. Simulation and experimental results show that the proposed algorithm can track the global maximum power point rapidly under different environmental conditions, and the tracking time is more than 50% shorter than that of the improved particle swarm optimization and flower pollination algorithm.

Oriented object detection in optical remote-sensing images has been a challenging task due to arbitrary orientations and densely packed distribution of objects. Specifically, most existing methods lack adaptivity when regressing objects with different shapes and orientations. Although the point set representation is relatively flexible, the initial distribution of the point set is fixed in advance. In addition, some models based on the point set cannot get high location precision of points, affecting the bounding box generation. In this letter, we propose an Adaptive Point Set Network (APS-Net) for optical remote-sensing object detection, including three improvements. First, we propose the initial distribution learner (IDL) to learn the optimal initial aspect ratio, which helps the point set fit the object’s shape well. Second, we design the uncertainty measurement module (UMM), which considers the uncertainty of point location to improve location precision. Third, we introduce the local outlier factor (LOF) in the loss to punish outlier points more reasonably. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on three commonly used datasets (i.e., DOTA-v1.0, UCAS-AOD, and HRSC2016) in the remote-sensing field.

Answering reachability queries is a fundamental graph operation. Considering that the size of the input graph has a great impact on query performance, there are studies focusing on reducing the graph size, such that queries can be answered over a smaller graph. Although the input graph can be compressed significantly by existing approaches, a good compression ratio does not always mean a positive effect on query performance. In this paper, we study graph reduction to accelerate reachability queries answering. We propose a novel graph reduction approach, namely RCN reduction, to compress the input graph into a smaller one. Let a be the compression ratio of the number of nodes in the reduced graph over that of the input graph, we show that based on our approach, the lower bound probability that a query q can be answered in constant time is 1-a^2. We show the difficulties of RCN reduction and propose efficient algorithms to improve the compression ratio. Based on the result of RCN reduction, we further propose a novel labeling scheme to accelerate queries answering. We confirm the efficiency of our approach by extensive experimental results for graph reduction and reachability queries processing using 20 real datasets.

We study graph reduction to accelerate reachability queries answering. We propose a novel graph reduction approach, namely RCN reduction, to reduce the input graph$G$of$\vert V\vert$nodes into a smaller one with$\vert V^{r}\vert$nodes. Assume that the probability of a node of$G$to be a query node is$1/\vert V\vert$, we show that based on our approach, the lower bound probability that a query$q$can be answered in constant time is$1-(\frac{\vert V^{r}\vert}{\vert V\vert})^{2}$, denoting that the smaller the reduced graph, the larger the probability that$q$can be answered in constant time. We show the difficulties of RCN reduction and propose efficient algorithms to improve the reduction ratio. We confirm the benefits of our approach by rich experimental results using real datasets.

Online deposit-free leasing service has witnessed rapid growth in China and shows a promising market in the future. While eliminating the requirement of a deposit does attract more users to the service, it also lowers the cost for fraudsters. Since the emergence of this service is relatively new, there are few works in literature focusing on detecting fraud transactions in it. Existing efforts mainly fall into hard-coded solutions such as block-listing or scorecard methods, which can be impotent in the face of the diverse fraud tactics, e.g., identity theft, or even suffering concept drift problem as the tactics evolve. In this paper, we contribute Turbo, an efficient graph-based anti-fraud system, to fully exploit the abundant user behavior logs in a real-time manner. Turbo is able to additionally make use of the implicit user relationships beyond the user features in the logs. To capture the user relationships, we first propose a novel algorithm to construct a time-evolving user behavior network called BN. Empirical analysis demonstrates that fraudsters in BN exhibit unique temporal aggregation and homophilic patterns, which inspires us to develop a novel heterogeneous adaptive graph neural network algorithm called HAG. Specifically, in HAG two graph operators are presented to mitigate the over-smoothing problem and make better use of the heterogeneous behavior relations in BN. Extensive experiments on a real-world dataset show that our method outperforms state-of-the-art methods significantly and can give a response in seconds for each detection request.

Robots are usually equipped with microphones and cameras to perceive and understand the physical world. Though visual object detection technology has achieved great success, the detection in other modalities remains unsolved. In this paper, we establish a novel robotic sound-indicated visual object detection framework, and develop a two-stream weakly-supervised deep learning architecture to connect the visual and audio modalities for localizing the sounding object. A dataset is constructed from the AudioSet to validate the proposed method and some promising applications are demonstrated on robotic platforms.

It is challenging to estimate wind speed of tropical cyclones directly using remote sensing image patterns. This paper approaches the task in two major steps: cyclone category estimation and wind speed regression. A novel framework based on Tensor Convolutional Neural Network (Tensor CNN) is proposed to solve the problem. Not only does the framework combine Tensor analysis for dimensionality reduction and deep neural networks for pattern recognition, the Tensor CNN also provides a unitary and concise mathematical representation form of the two significant models. The proposed framework is able to categorize cyclones by classification based on the Tensor CNN as well as exploit the estimated categories and predict the wind speed by a successive regression model. Experiments are conducted on multispectral imagery acquired by the FY-4 Satellite. Results show that the framework outperforms several classic models in cyclone category estimation and one state-of-the-art method, Deviation Angle Variance Technique (DAVT), in wind speed regression.

Answering reachability queries is one of the fundamental graph operations. The existing approaches build indexes and answer reachability queries on a directed acyclic graph (DAG) G, which is constructed by coalescing each strongly connected component of the given directed graph G into a node of G. Considering that G can still be large to be processed efficiently, there are studies to further reduce G to a smaller graph. However, these approaches suffer from either inefficiency in answering reachability queries, or cannot scale to large graphs.

Efficiently answering XML keyword queries has attracted much research effort in the last decade. The key factors resulting in the inefficiency of existing methods are thecommon-ancestor-repetition(CAR) andvisiting-useless-nodes(VUN) problems. To address the CAR problem, we propose agenerictop-downprocessing strategy to answer a given keyword query w.r.t. LCA/SLCA/ELCA semantics. By “top-down”, we mean that we visit allcommon ancestor(CA) nodes in a depth-first, left-to-right order; by “generic”, we mean that our method is independent of the query semantics. To address the VUN problem, we propose to use child nodes, rather than descendant nodes to test the satisfiability of a node$v$w.r.t. the given semantics. We propose two algorithms that are based on either traditional inverted lists or our newly proposed LLists to improve the overall performance. We further propose several algorithms that are based on hash search to simplify the operation of finding CA nodes from all involved LLists. The experimental results verify the benefits of our methods according to various evaluation metrics.

Efficiently answering XML keyword queries has attracted much research effort in the last decade. One key factors resulting in the inefficiency of existing methods are the common-ancestor-repetition (CAR) and visiting-useless-nodes (VUN) problems. In this paper, we propose a generic top-down processing strategy to answer a given keyword query w.r.t. LCA/SLCA/ELCA semantics. By top-down, we mean that we visit all common ancestor (CA) nodes in a depth-first, left-to-right order, thus avoid the CAR problem; by generic, we mean that our method is independent of the labeling schemes and query semantics. We show that the satisfiability of a node v w.r.t. the given semantics can be determined by v's child nodes, based on which our methods avoid the VUN problem. We propose two algorithms that are based on either traditional inverted lists or our newly proposed LLists to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

In this paper, we focus on efficient keyword query processing for XML data based on the SLCA and ELCA semantics. We propose a novel form of inverted lists for keywords which include IDs of nodes that directly or indirectly contain a given keyword. We propose a family of efficient algorithms that are based on the set intersection operation for both semantics. We show that the problem of SLCA/ELCA computation becomes finding a set of nodes that appear in all involved inverted lists and satisfy certain conditions. We also propose several optimization techniques to further improve the query processing performance. We have conducted extensive experiments with many alternative methods. The results demonstrate that our proposed methods outperform previous methods by up to two orders of magnitude in many cases.

We investigated the problem of finding from a collection of strings those similar to a given query string based on edit distance, for which the critical operation is merging inverted lists of grams generated from the collection of strings. We present an efficient algorithm to accelerate the merging operation.

We perform a finite-difference time-domain numerical simulation for metal ore detection by borehole radar. The ore-body model is a practical Ni-Cu-Pt one which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross section perpendicular to the geological strike of the formation which is composed of overburden, ore zone, iron formation, peridotite, granite gneiss, and sediments. We analyze the simulated borehole radar profiles and find that some interfaces could be detected. The ore zone is very absorptive to electromagnetic wave. By combined interpretation of the data from several boreholes, the azimuth ambiguity of the borehole radar could be overcome with some a priori geological information, and the geological structure could be delineated clearly.

We perform finite difference time domain (FDTD) numerical simulation for metal ore detection by borehole radar. The ore-body model is adopted from a practical Ni-Cu-Pt ore body which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross-section perpendicular to the geological strike of the formation which is composed with overburden, ore zone, iron formation, peridotite, granite-gneiss, and sediments. We analyzed the simulated borehole radar profiles and found that some interfaces could be detected and some could not, also the ore zone is very absorptive to the wave.

Numerous mobility solutions have been proposed in the past, but none of them have been widely deployed today. To address the deployment difficulty in previous work, we propose an end-system-based mobility solution for IPv6 (EMIPv6). In our design, we adhere to the end-to-end principle (Saltzer et al., 1984) by directly performing connection maintenance and data packet delivery between the two communicating hosts. And we leverage distributed hash table-based peer-to-peer (P2P) systems to carry out self-organized, scalable, and robust name lookup for mobile hosts. When the mobility messages such as address updates cannot be delivered directly between the end hosts (e.g., due to firewalls, network address translators, or simultaneous movement), we propose a distributed subscription/notification (S/N) service on top of the previously introduced P2P overlay to deliver them. This leads to a complete end-system solution with small handoff latency and efficient packet delivery. Our simulation results showed that our scheme achieves small name resolution latency by considering host heterogeneity into the design. We have implemented EMIPv6-based end-systems. The experiments in our testbed demonstrated that a complete end-system-based mobility solution is technically feasible and is easy to deploy in the real world without the need of introducing new network components.