Since large language models (LLMs) generally have a large parameter scale and acquire knowledge across multidisciplinary corpora, their parameters tend to exhibit varying levels of importance for GIS knowledge alignment. Specifically, certain model layers of LLMs may already align well with GIS knowledge, while others may require significant adaptation and thus play a more critical role during the fine-tuning process. However, representative fine-tuning methods, such as low-rank adaptation (LoRA), apply a uniform treatment to all parameters, overlooking their varying importance for GIS downstream tasks. This strategy may disrupt well-adapted layers or limit less-adapted ones from effectively encoding GIS knowledge. Therefore, we develop a novel GIS knowledge-aware LoRA allocation (GLoRA) scheme that adaptively allocates trainable parameters to layers based on their importance. Specifically, GLoRA first investigates a GIS knowledge-aware strategy to identify the importance of model layers for a given GIS task in a data-dependent way. Second, it dynamically adjusts the size of LoRA modules, allocating more parameters to more important layers to enhance their representation capability. This adaptive approach ensures efficient parameter utilization while preserving the strengths of well-adapted layers. We evaluated GLoRA on three GIS-related tasks, and the results show its improved performance compared to recent baselines with comparable parameter budgets.

Large Language Models (LLMs) excel in natural language-relevant tasks like text generation and question answering Q&A. To further expand their application, efforts focus on enabling LLMs to utilize real-world tools. However, their tool-use ability in professional GIS remains under explored due to two main challenges. Firstly, LLMs are usually trained on general-domain corpora, lacking sufficient and comprehensive GIS-specific data to align with professional knowledge, including understanding the functions of GIS tools. Secondly, researchers often need to combine multiple GIS tools to solve geospatial tasks. To address these challenges, we propose a trainable method to enable LLMs to master GIS tools. We curated a comprehensive set of resources: instruction-response data (GeoTool, 1950 instructions) to enhance the understanding of LLMs for GIS tools, instruction-solution data (GeoSolution, 3645 instructions) to improve their ability to generate tool-use solutions for geospatial tasks, and annotated instruction-solution evaluation data (GeoTask, 300 instructions) for evaluating LLMs’ GIS tool-use proficiency. Using the collected training data (GeoTool and GeoSolution), we fine-tuned a professional-domain LLM called GeoTool-GPT based on an open-source general-domain LLM, the LLaMA-2-7b model. The experiment based on evaluation data validates our method’s effectiveness in enhancing the tool-use ability of general-domain LLMs in the professional GIS domain, with the performance of our model closely approaching that of GPT-4.

With the high proportion integration of photovoltaic power, the grid-tie inverter as a power electronic device has become one of the mainstream solutions. Considering that the sensors of the grid-tie inverter are vulnerable to exploitation by cyber and physical attacks, this article conducts a synthetic analysis of sensor attacks from the perspective of locations, strategies, and consequences. We find that sensor attacks in the low-frequency domain can cause a range of damages, including damping the output power, reducing power quality, and even burning out the inverter. To detect sensor attacks in changing environments, we propose a robust detector in the finite frequency domain, while the unknowns of varying system dynamics are decoupled. The detector design is formulated as a tractable optimization problem that can be solved numerically. To support real-time detection, an analytical solution form based on a quadratic programming reformulation with relaxed constraints is constructed. To the best of our knowledge, this is the first attempt to conduct the synthetic analysis of sensor attacks on the grid-tie inverter and address its robust detector design in the finite frequency domain under the parameter-varying model. Numerical simulations show that sensor attacks could bring severe damage but our detector can detect them effectively.

With the increasing popularity of the Internet-of-Things (IoT) devices, the demand for fast and convenient battery charging services grows rapidly. Wireless charging is a promising technology for such a purpose and its usage has become ubiquitous. However, the close distance between the charger and the device being charged not only makes proximity-based and near-field communication attacks possible but also introduces a new type of vulnerabilities. In this article, we propose to create fingerprints for wireless chargers based on the intrinsic nonlinear distortion effects of the underlying charging circuit. Using such fingerprints, we design the WirelessID system to detect potential short-range malicious wireless charging attacks. WirelessID collects signals in the standby state of the charging process and sends them to a trusted server, which can extract the fingerprint and then identify the charger. We conduct experiments on eight commercial chargers over a period of five months and collect 8000 traces of signal. We use 10% of the traces as the training data set and the rest for testing. The results show that on the standard performance metrics, we have achieved 99.0% precision, 98.9% recall, and 98.9% F1 -score.

This paper proposes an optimization method of data saving for application-level checkpointing based on the live-variable analysis method for MPI programs. We presents the implementation of a source-to-source precompiler (CAC) for automating applicationlevel checkpointing based on the optimization method. The experiment shows that CAC is capable of automating application-level checkpointing correctly and reducing checkpoint data effectively.

The meridian system conceived by the ancient Chinese has been described and referenced for more than a thousand years. The meridian meaning paths are the main trunks that run longitudinal and interiorly-exteriorly within the body. It consists of about 400 acupuncture points and 20 meridians connecting most of the points. Meridian system deals with the route and distribution and physiological functions. It integrates meridian, tissues and organs into an organic whole. In this paper we model the meridian system using Petri nets. To the author's knowledge, this is the first work that adopt the PN's methods to define and model the meridian system. Finally we analyze a real case on the treatment of tennis elbow.