FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have different computing capacities and communication capabilities. To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model. Our solution enables the training of heterogeneous models with varying computational complexities and aggregates them into a single global model. Furthermore, FedHM significantly reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that FedHM is superior in the performance and robustness of models of different sizes, compared with state-of-the-art heterogeneous FL methods under various FL settings. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed

I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench

Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this paper, we introduce AwareBench, a benchmark designed to evaluate awareness in LLMs. Drawing from theories in psychology and philosophy, we define awareness in LLMs as the ability to understand themselves as AI models and to exhibit social intelligence. Subsequently, we categorize awareness in LLMs into five dimensions, including capability, mission, emotion, culture, and perspective. Based on this taxonomy, we create a dataset called AwareEval, which contains binary, multiple-choice, and open-ended questions to assess LLMs' understandings of specific awareness dimensions. Our experiments, conducted on 13 LLMs, reveal that the majority of them struggle to fully recognize their capabilities and missions while demonstrating decent social intelligence. We conclude by connecting awareness of LLMs with AI alignment and safety, emphasizing its significance to the trustworthy and ethical development of LLMs. Our dataset and code are available at https://github.com/HowieHwong/Awareness-in-LLM

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs

Instance Segmentation of Buildings using Keypoints

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details

Aggressive Environment Resistance of Concrete Products Modified With Nano Alumina and Nano Silica

The service life of concrete products with exposure to an aggressive environment has raised great concerns in the past decades. Nanomaterials have been used as a promising approach to improve the environmental resistance of concrete products when exposed to synergistic attacks. The impacts of CaCl2 on nano-modified concrete, especially along with freeze/thaw (F/T) and wet/dry (W/D) cycles, were barely discussed. In this study, the impacts of CaCl2 along with F/T and W/D cycles on the nano SiO2 and Al2O3 modified concrete were investigated. The mass loss, flexural strength, compressive strength, and relative dynamic modulus of elasticity were tested to evaluate the durability of concrete products. The testing results indicate that the addition of nanoparticles has a distinctive effect on the environment resistance enhancement of concrete samples. The microstructure analysis demonstrates that with the addition of nanoparticles, high-density hydration products were formed, which is beneficial to the properties enhancement of concrete products. This study not only provides an approach to realize the nano modification on the durability of concrete products but also helps to design and fabricate environmentally resistant concrete products when exposed to a synergistic aggressive environment

Permian-Triassic boundary microbialites (PTBMs) in soutwest China: implications for paleoenvironment reconstruction

Permian–Triassic boundary microbialites (PTBMs) are commonly interpreted to be a sedimentary response to upwelling of anoxic alkaline seawater and indicate a harsh marine environment in the Permian–Triassic transition. However, recent studies propose that PTBMs may instead be developed in an oxic environment, therefore necessitating the need to reassess the paleoenvironment of formation of PTBMs. This paper is an integrated study of the PTBM sequence at Yudongzi, northwest Sichuan Basin, which is one of the thickest units of PTBMs in south China. Analysis of conodont biostratigraphy, mega- to microscopic microbialite structures, stratigraphic variations in abundance and size of metazoan fossils, and total organic carbon (TOC) and total sulfur (TS) contents within the PTBM reveals the following results: (1) the microbialites occur mainly in the Hindeodus parvus Zone but may cross the Permian–Triassic boundary, and are comprised of, from bottom to top: lamellar thrombolites, dendritic thrombolites and lamellar-reticular thrombolites; (2) most metazoan fossils of the microbialite succession increase in abundance upsection, so does the sizes of bivalve and brachiopod fossils; (3) TOC and TS values of microbialites account respectively for 0.07 and 0.31 wt% on average, both of which are very low. The combination of increase in abundance and size of metazoan fossils upsection, together with the low TOC and TS contents, is evidence that the Yudongzi PTBMs developed in oxic seawater. We thus dispute the previous view, at least for the Chinese sequences, of low-oxygen seawater for microbialite growth, and question whether it is now appropriate to associate PTBMs with anoxic, harsh environments associated with the end-Permian extinction. Instead, we interpret those conditions as fully oxygenated.13th Five-Year Plan National Scientific and Technology Major Project (2016ZX05004002-001); National Natural Science Foundation of China (41602166)

Research of ontology model of heading face

The paper pointed out that building ontology model of heading face was an important step towards the construction of intelligent decision-making system based on deep analysis of production model of the heading face, obtained relationship between the staff, devices and environment of underground heading face by analyzing factors of mine production safety, and built safety production ontology model of the heading face. Hazardous workplace was taken as an example to simply verify the validity of construction method of the ontology model of heading face

CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images

Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets. The segmentation results can be applied to reconstruct 3D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments