Segment Routing over IPv6 (SRv6) gives operators explicit path control and alleviates network congestion, making it a compelling technique for traffic engineering (TE). Yet two practical hurdles slow adoption. First, a one-shot upgrade of every traditional device is prohibitively expensive, so operators must prioritize which devices to upgrade. Second, the Segment Routing Header (SRH) increases packet size; if TE algorithms ignore this overhead, they will underestimate link load and may cause congestion in practice. We address both challenges with DRL-TE, an algorithm that couples deep reinforcement learning (DRL) with a lightweight local search (LS) step to minimize the network’s maximum link utilization (MLU). DRL-TE first identifies the smallest set of critical devices whose upgrade yields the largest drop in MLU, enabling hybrid IP/SRv6 networks to approach optimal performance with minimal investment. It then computes SRH-aware routes, and the DRL agent, augmented by a fast LS refinement, rapidly reduces MLU even under traffic variation. Experiments on an 11-node hardware testbed and three larger simulated topologies show that upgrading about 30% of devices allows DRL-TE to match fully upgraded networks and reduce MLU by up to 34% compared with existing algorithms. DRL-TE also maintains high performance under link failures and traffic variations, offering a cost-effective and robust path toward incremental SRv6 deployment.

Forecasting stock prices is complex and challenging. Uncovering correlations among stocks has proven to enhance stock price forecasting. However, existing correlation discovery methods, such as concept-based methods, are slow, inaccurate, and limited by their reliance on predefined concepts and manual analysis. In this paper, we propose COGRASP, a novel approach for stock price forecasting that constructs stock co-occurrence graphs automatically by analyzing rapidly updated sources such as reports, newspapers, and social media. Besides, we aggregate forecasts across multiple timescales (i.e., long-, medium-, and short-term) to capture multi-timescale trends fluctuations, thereby enhancing price forecasting accuracy. In experiments with real-world open-source stock market data, COGRASP outperforms state-of-the-art methods.

In this study, the authors present a novel ensemble tracking system by formulating the tracking task in terms of a linear regression which is a least‐squares problem. A set of weak classifiers are trained using least squares which are solved efficiently using the Moore–Penrose inverse. Then, these weak classifiers are combined into a strong classifier using bagging. The strong classifier is used to recognise the target and locate its position, which is obtained efficiently in the Fourier domain. For obtaining a good ensemble, a novel sampling strategy is proposed to train accurate and diverse weak classifiers. By exploiting historical targets to monitor the training process, pose change and occlusion are well‐handled. The proposed method is extensively evaluated using a variety of evaluation protocols on the recent standard datasets including OTB50, OTB100 and VOT2016. Experimental results show that the proposed methodology performs favourably against state‐of‐the‐art methods in terms of efficiency, accuracy and robustness.