Recent studies have shown that reinforcement learning (RL) methods have brought significant performance gains for stock portfolio management (PM) because they effectively utilize historical price information and directly generate portfolio weights. We found, however, that there is still great room for improvement in how to fully consider the impact of financial news and stock trends on PM while avoiding model instability caused by feature disparity and fragile convergence due to RL itself. Addressing this allows us to develop more profitable and robust PM strategies. To this end, we propose TrendTrader, a novel RL framework for news-oriented stock portfolio management with trend heuristics. Specifically, TrendTrader utilizes a large language model (LLM) to obtain sentiment scores of stock news and generates heterogeneous contexts based on global sentiment embedding, which enhances model stability in processing multimodal features. Further, TrendTrader incorporates trend heuristics into both the network architecture and the reward function, while combining supervised learning and reinforcement learning in the form of incremental training to facilitate policy network convergence. Extensive experiments in the U.S. market and the China market verify the state-of-the-art performance of TrendTrader.

Long-term series forecasting is crucial in real-world applications. Existing works have leveraged self-attention mechanisms and multi-scale temporal data to learn complex dependencies and patterns in long-term series prediction. However, most prediction models directly aggregate together multi-scale data resulting in the inability to appropriately capture key implicit information at each time step and lack of flexibility. To alleviate this problem, in this paper, we propose a novel Transformer-based network, dubbed ASformer, which can capture the spatio-temporal dependence of time series from adjacent scale features while obtaining global information. Specifically, we design a Scale Bonder that empowers ASformer with progressive capacities in capturing the correlations between data of two adjacent scales. We achieve the state-of-the-art performances with a 20.18% relative improvement on three benchmarks covering two practical applications: energy and disease. Our code will be made publicly available at: https://github.com/Hanpengjiang/ASformer.

Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://github.com/ZNan-Chen/diffCOD.

It is universally acknowledged that the financial trading is a thorny issue in time-series scenarios.On the one hand, due to the great randomness and instability in financial markets, the existing machine learning methods are inadequate for modeling high-frequency financial data.On the other hand, it remains a challenge to identify the validity of transaction actions to avoid high fees.To address these issues, we propose a novel trading framework, namely mTrader, to offer suitable trading strategies automatically.We creatively design a multiscale signal matrix to describe the temporal trends of markets.On this basis, Vector Quantized Variational AutoEncoder (VQ-VAE) was introduced to capture discrete latent variables.In addition, an offline Action Optimizer (AO) based on Proximal Policy Optimization (PPO) could help filter out sub-optimal trading action.Extensive experiments have shown that our model achieves state-of-the-art performance on many popular stock markets.

Person-job fit aims to use the algorithms to match jobseekers with job postings to overcome information overload on online recruitment platforms.Traditional matching algorithms are not ideal in the feature representation and interaction of resumes and job postings.To this end, we propose a person-job fit model PJFFRFI based on multi-domain feature representation and multi-dimensional feature interaction, which comprehensively considers the features of various domains and learns feature correlation vectors in different dimensions.Specifically, we first divide the features in resumes and job postings into seven domains, and design different representation methods according to the data type.Then we propose a feature enhancement module (FEM) based on multi-head self-attention to learn the feature correlation vectors in resumes and job postings.Moreover, we propose a feature interaction module (FIM) to facilitate feature interaction both inside and outside the domain.Extensive experiments on a real-world dataset demonstrate that the proposed method significantly surpasses the state-of-the-art methods.

It is universally acknowledged that the prediction of the stock movement is a thorny issue of the time-series prediction tasks. Often high-frequency stock behavior in the financial market is a prerequisite for investors to make effective investment strategies. Besides, the existing solution to the traditional stock movement prediction is generally converted into a classification task, which affects the practicability of the model. In this paper, we propose a Kernel-based Hybrid Interpretable Transformer(KHIT) model, which combines with a novel loss function to cope with the prediction task of non-stationary stock markets. Specifically, inspired by the Donchian price channels theory, we propose an adaptive renormalization kernel function that converts the original binary classification task (Rise or Fall) into the time-series prediction. Furthermore, we design a multi-order differential sequence loss function to identify the movement of high-frequency stock in future multi-scale periods. Finally, based on the Information Bottleneck (IB) theory and Transformer structure, we introduce the tensor decomposition and interpretable attention mechanism for improving the discrimination to the multi-types factors and the interpretability of our model. To be the best of our knowledge, it is the first work to achieve the high-frequency stock movement prediction task rather than classification. Experimental results show our proposed model outperforms several competitive methods in stock movement prediction on two non-stationary datasets.