Semantic-aware Transmission for Robust Point Cloud Classification

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89\% when SNR is higher than 10 dB and still maintains accuracy above 66.6\% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8\% to 48\% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.Comment: submitted to globecom 202

Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol

summary:This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on $[0,1]$, is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper

The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks. However, their effectiveness in the financial domain, specifically in predicting stock market movements, remains to be explored. In this paper, we conduct an extensive zero-shot analysis of ChatGPT's capabilities in multimodal stock movement prediction, on three tweets and historical stock price datasets. Our findings indicate that ChatGPT is a "Wall Street Neophyte" with limited success in predicting stock movements, as it underperforms not only state-of-the-art methods but also traditional methods like linear regression using price features. Despite the potential of Chain-of-Thought prompting strategies and the inclusion of tweets, ChatGPT's performance remains subpar. Furthermore, we observe limitations in its explainability and stability, suggesting the need for more specialized training or fine-tuning. This research provides insights into ChatGPT's capabilities and serves as a foundation for future work aimed at improving financial market analysis and prediction by leveraging social media sentiment and historical stock data.Comment: 13 page

Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone

Knowledge of dispersion and attenuation is essential for better reservoir characterization and hydrocarbon identification. However, limited by reliable laboratory data at seismic frequency bands, the roles of rock and fluid properties in inducing dispersion and attenuation are still poorly understood. Here we perform a series of laboratory measurements on Bentheimer and Bandera sandstone under both vacuum-dry and partially water-saturated conditions at frequencies ranging from 2 to 600 Hz. At vacuum-dry conditions, the bulk dispersion and attenuation in Bandera sandstone with more clay contents are distinctly larger than those in Bentheimer sandstone, suggesting clay contents might contribute to the inelasticity of the rock frame. The partially water-saturated results show the combined effects of rock permeability and fluid saturation on bulk dispersion and attenuation. Even a few percent of gas can substantially dominate the pore-fluid relaxation by providing a quick and short communication path for pore pressure gradients. The consequent bulk dispersion and attenuation are negligible. Only as the samples are approaching fully water-saturated conditions, rock permeability begins to play an essential role in the pore-fluid relaxation. For Bandera sandstone with lower permeability, a partially relaxed status of pore fluids is achieved when the gas saturation is lower than 5%, accompanied by significant attenuation and dispersion.Cited as: Wei, Q., Wang, Y., Han, D., Sun, M., Huang, Q. Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone. Advances in Geo-Energy Research, 2021, 5(2): 181-190, doi: 10.46690/ager.2021.02.0

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.Comment: 10 pages, 6 figure

Mixed-model assembly line balancing problem in multi-demand scenarios

The mixed-model assembly line balancing problem (MMALBP) in multi-demand scenarios is investigated, which addresses demand fluctuations for each product in each scenario. The objective is to minimize the sum of costs associated with tasks allocation, workstation activation, and penalty costs for unbalanced workloads. A mixed integer programming model is developed to consider the constraint of workstation space capacity. A phased heuristic algorithm is designed to solve the problem. The computational results show that considering demand fluctuations in multiple demand scenarios leads to more balanced workstation loads and improved assembly line production efficiency. Finally, sensitivity analysis of important parameters is conducted to summarize the impact of parameter changes on the results and provide practical management insights

PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

Although large language models (LLMs) has shown great performance on natural language processing (NLP) in the financial domain, there are no publicly available financial tailtored LLMs, instruction tuning datasets, and evaluation benchmarks, which is critical for continually pushing forward the open-source development of financial artificial intelligence (AI). This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 136K data samples to support the fine-tuning, and an evaluation benchmark with 5 tasks and 9 datasets. We first construct the large-scale multi-task instruction data considering a variety of financial tasks, financial document types, and financial data modalities. We then propose a financial LLM called FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. To support the evaluation of financial LLMs, we propose a standardized benchmark that covers a set of critical financial tasks, including five financial NLP tasks and one financial prediction task. With this benchmark, we conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks. The model, datasets, benchmark, and experimental results are open-sourced to facilitate future research in financial AI.Comment: 12 pages, 1 figure