ABSTRACT The factorized sparse approximate inverse (FSAI) preconditioner has been proven to be effective in accelerating the convergence of iterative methods. Due to the high cost of constructing the FSAI preconditioner, accelerating it on graphics processing unit (GPU) has attracted considerable attention. However, despite the development of some existing FSAI preconditioning algorithms on GPU, their performance will significantly decrease when they encounter matrix types that are not suitable for them. This motivates us to investigate how to design an effective FSAI preconditioning algorithm on GPU. In this paper, we propose an adaptive FSAI preconditioning algorithm on GPU, called GFSAI‐Adaptive, to address the above problem. In GFSAI‐Adaptive, first, two adaptive thread allocation strategies are proposed for two special types of SPD matrices to ensure that the allocated threads can be fully utilized. Second, based on the proposed two thread allocation strategies, two FSAI kernels, called GFSAII and GFSAIII, are presented. Third, we construct a new graph convolutional network, and thus propose a search engine to select the optimal kernel from GFSAII and GFSAIII for matrices that do not belong to two special types based on it. Experimental results show that our proposed GFSAI‐Adaptive is effective and outperforms a popular preconditioning algorithm in the public CUSPARSE library and a recent parallel static FSAI preconditioning algorithm on GPU.

Supercapacitors are typically charged with constant current conditions. However, existing parameter identification methods can not effectively identify equivalent series resistance (ESR) under constant current conditons. To address this problem, this paper proposes a Switching Kalman Filter (SKF) parameter identification method for reconfigurable supercapacitors. By introducing a switching excitation strategy, transient current pulses are utilized to enhance the observable dimension of the system, overcoming the limitations of the traditional methods in recognizing parameters under constant current conditions and achieving high-precision identification. First, an RC model of the supercapacitor incorporating a reconfigurable topology is established. Then the model undergoes mathematical discretization, and a recursive algorithm framework for parameter identification is constructed. Through several sets of simulation experiments, it is verified that SKF is able to perform parameter identification under constant current conditions.

Aerial Vision-and-Language Navigation (VLN) is an emerging task that enables Unmanned Aerial Vehicles (UAVs) to navigate outdoor environments using natural language instructions and visual cues. However, due to the extended trajectories and complex maneuverability of UAVs, achieving reliable UAV-VLN performance is challenging and often requires human intervention or overly detailed instructions. To harness the advantages of UAVs' high mobility, which could provide multi-grained perspectives, while maintaining a manageable motion space for learning, we introduce a novel task called Dual-Altitude UAV Collaborative VLN (DuAl-VLN). In this task, two UAVs operate at distinct altitudes: a high-altitude UAV responsible for broad environmental reasoning, and a low-altitude UAV tasked with precise navigation. To support the training and evaluation of the DuAl-VLN, we construct the HaL-13k, a dataset comprising 13,838 collaborative high-low UAV demonstration trajectories, each paired with target-oriented language instructions. This dataset includes both unseen maps and an unseen object validation set to systematically evaluate the model's generalization capabilities across novel environments and unfamiliar targets. To consolidate their complementary strengths, we propose a dual-UAV collaborative VLN framework, AeroDuo, where the high-altitude UAV integrates a multimodal large language model (Pilot-LLM) for target reasoning, while the low-altitude UAV employs a lightweight multi-stage policy for navigation and target grounding. The two UAVs work collaboratively and only exchange minimal coordinate information to ensure efficiency. Experimental results indicate that AeroDuo achieves an evident 9.71% improvement in success rates compared to existing single-UAV methods, demonstrating the effectiveness of dual-altitude collaboration in balancing environmental coverage, precision, and operational autonomy.

To address the limitations of existing house price prediction methods that struggle with handling mixed data types, we developed a novel model utilizing a reinforcement learning-based weight fusion algorithm. First, we reconstruct the feature fusion vectors from categorical feature variables. Then, we create individual house price prediction models using a variety of algorithms: Multiple Linear Regression, Regression Decision Tree, Extreme Random Regression Tree, and Multilayer Perceptron. These models' weights are dynamically adjusted using Q-Learning to minimize the Root Mean Square Error (RMSE). Our experiments demonstrate substantial gains in RMSE performance using our weight fusion approach. Specifically, on the small-scale Shenzhen dataset, RMSE improvements vary from 8.65% to 64.61%. On the large-scale Boston dataset, the improvements range between 53.06% and 76.40% in comparison to the standalone prediction algorithms.

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. To this end, we propose a multi-task learning-based deep neural network (DNN) framework, namely${{\sf PRNet}}$$^+$, to incorporate outdoor position recovery and transportation mode detection. To make sure that${{\sf PRNet}}$$^+$can work, we develop a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that${{\sf PRNet}}$${^{+}}$greatly outperforms state-of-the-arts by lower localization errors.

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely${\sf RLoc}$, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work${\sf RLoc}$can greatly improve Telco location accuracy. For example,${\sf RLoc}$on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4 percent better than state-of-the-art.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, TLoc outperforms a non-transfer approach by 27.58 and 26.12 percent less median errors, and further leads to 47.77 and 49.22 percent less median errors than a recent fingerprinting approach NBL.

Understanding the fine-grained distribution of telecommunication (Telco) signals in terms of a three-dimensional (3D) space is important for Telco operators to manage, operate and optimize Telco networks. It is particularly true in nowadays urban cities with a large number of high buildings. One of the key tasks is to infer the location height of mobile devices, e.g., the floor within a high building where mobile devices are located. However, precise height estimation is challenging due to complex Telco signal propagation within an indoor 3D space, sparse cell tower deployment and scarce training samples. To tackle these issues, in this paper, we propose an indoor MR height estimation framework, namely IFLoc, via a machine learning model. IFLoc first builds a training MR database via a pre-processing step to comfortably tag raw MR samples by precisely inferred height from auxiliary data such as GPS and barometer readings. Next, IFLoc trains a regression model for height estimation by a set of developed techniques including 3D space division, post-processing techniques, feature augmentation and an improved SVR (Supported Vector Regression) model. Our evaluation on eight real datasets collected within five representative high buildings in Shanghai validates that IFLoc outperforms state-of-the-art counterparts in particularly with scarce training data.

Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco networks. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength, between mobile devices and Telco networks. MR data have been widely used to precisely recover outdoor locations of mobile devices for the applications e.g., human mobility, urban planning and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture the spatio-temporal locality in underlying mobility patterns for lower localization errors. Such HMM approaches typically assume stable mobility pattern of underlying mobile devices. Yet real MR datasets frequently exhibit heterogeneous mobility patterns due to mixed transportation modes of underlying mobile devices and uneven distribution of the positions associated with MR samples. To address this issue, we propose a deep neural network (DNN)-based position recovery framework, namely PRNet, which can ensemble the power of CNN, sequence model LSTM, and two attention mechanisms to learn local, short- and long-term spatio-temporal dependencies from input MR samples. Extensive evaluation on six datasets collected at three representative areas (core, urban, and suburban areas in Shanghai, China) indicates that PRNet greatly outperforms seven counterparts.

When Telecommunication (Telco) networks provide phone call and data services for mobile devices, measurement records (MRs) are generated to report connection states, e.g., signal strength, between mobile devices and nearby base stations. Telco outdoor localization is a technique to localize the mobile devices by using MR data. Unfortunately, city-scale Telco localization suffers from low localization accuracy, high cost of collecting sufficient MR samples, and noisy MR data. To tackle these issues, in this forum paper, we propose a machine learning-based Telco localization framework, consisting of three main components (localization models, the techniques to solve the data scarcity issue and to repair noisy data) and future directions.

Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco radio and core equipment. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength at the mobile device, when mobile devices give phone calls or access data services. Telco historical data (e.g., MRs) have been widely analyzed to understand human mobility and optimize the applications such as urban planning and traffic forecasting. The key of these applications is to precisely localize outdoor mobile devices from these historical MR data. Previous works calculate the location of a mobile device based on each single MR sample, ignoring the sequential and temporal locality hidden in the consecutive MR samples. To address the issue, we propose a deep neural network (DNN)-based localization framework namely DeepLoc to ensemble a recently popular sequence learning model LSTM and a CNN. Without skillful feature design and post-processing steps, DeepLoc can generate a smooth trajectory consisting of accurately predicted locations. Extensive evaluation on 6 datasets collected at three representative areas (core business, urban and suburban areas in Shanghai, China) indicates that DeepLoc greatly outperforms 10 counterparts.

When Telecommunication (Telco) networks provide phone call and data services for mobile users, measurement record (MR) data is generated by mobile devices during each call/session. MR data reports the connection states, e.g., signal strength, between mobile devices and nearby base stations. Given the MR data, the literature has proposed various Telco localization approaches, to localize mobile devices. Unfortunately, such approaches typically estimate the individual position independently, and could compromise the temporal and spatial locality in underlying mobility patterns. To address this issue, in this paper, we propose a deep neural network-based localization approach, namely RecuLSTM, to automatically extract contextual features and predict the positions of mobile devices from an input sequence of MR data. Our preliminary experiment validates that RecuLSTM greatly outperforms three recent works [1, 2, 4] which suffer from 3.2×, 1.91× and 3.56× median errors on the dataset in a 2G GSM suburban area, respectively.

Telecommunication (Telco) localization is a technique to localize mobile devices (MDs) outdoor by using measurement report (MR) data. Unfortunately, existing Telco localization approaches (with localization error > 50 meters) cannot achieve comparable localization accuracy as GPS (with localization error ±10 meters). In particular, due to signal interference and attenuation caused by high buildings in urban cities, it is hard to achieve high localization accuracy if MR records contain unstable signal data. To this end, we propose to first detect those MR records incurring high localization errors and next repair the predicted location (with high errors). Our experiments on two real MR data sets from 2G GSM and 4G LTE Telco networks verify that a Telco localization approach, enhanced by the proposed detection and repair algorithms, can greatly improve localization accuracy. For example, the enhancement algorithm on 2G and 4G data sets can achieve 29.5 and 13.2 meters of median errors, around 160.68% and 201.51% better than previous results. Such result indicates that our work can achieve nearly comparable localization accuracy as GPS.