As a critical infrastructure in modern energy supply chains, the pipeline transportation enables efficient and safe long-distance delivery of liquid fuels. However, the optimal scheduling of multiproduct pipelines presents formidable computational challenges due to the combinatorial explosion of binary decision variables and rigorous operational constraints. To address this challenge, this paper develops an innovative hierarchical optimization framework that integrates (1) initialization with approximate modelling to quickly identify feasible solution space, (2) rolling-horizon optimization with hybrid discrete-time modelling to eliminate redundant binary variables and constraints, and (3) modification using continuous-time modelling to re-optimize the detailed schedules. Numerical experiments on five industrial-scale cases demonstrate that the proposed approach is able to obtain the optimal detailed schedules within 90 s, even where conventional full-scale MILP models fail to converge. It achieves a 71.3%–88.4% reduction in infeasible binary variables, leading to a computational time reduction of 88.2–99.3% compared to full-scale MILP models. Comprehensive parameter sensitivity analyses confirm the approach’s robustness and computational efficiency across varying parameter settings. This computational improvement makes it a practical decision-support tool capable of handling complex, large-scale and dynamic scheduling problems.

Urban warming differs markedly from regional background trends, highlighting the unique behavior of urban climates and the challenges they present. Accurately predicting local urban climate necessitates modeling the interactions between urban surfaces and atmospheric forcing. Although off-the-shelf machine learning (ML) algorithms offer considerable accuracy for climate prediction, they often function as black boxes, learning data mappings rather than capturing physical evolution. As a result, they struggle to capture key land-atmosphere interactions and may produce physically inconsistent predictions. To address these limitations, we propose UCformer, a novel multi-task, physics-guided Transformer architecture designed to emulate nonlinear urban climate processes. UCformer jointly estimates 2-m air temperature $\(T\)$, specific humidity $\(q\)$, and dew point temperature $\(t\)$ in urban areas, while embedding domain and physical priors into its learning structure. Experimental results demonstrate that incorporating domain and physical knowledge leads to significant improvements in emulation accuracy and generalizability under future urban climate scenarios. Further analysis reveals that learning shared correlations across cities enables the model to capture transferable urban surface–atmosphere interaction patterns, resulting in improved accuracy in urban climate emulation. Finally, UCformer shows strong potential to fit real-world data: when fine-tuned with limited observational data, it achieves competitive performance in estimating urban heat fluxes compared to a physics-based model.

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations.However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale.In this study, we utilize LLMs as "Counseling Teacher" to enhance smaller models' emotion support response abilities, significantly reducing the necessity of scaling up model size.To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset.This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies to ensure its quality and comprehensiveness.Based on this, we then devise a Diverse Response Inpainting (DRI) mechanism to harness the teacher model to produce multiple diverse responses by filling in the masked conversation context.This richness and variety serve as instructive examples, providing a robust foundation for finetuning smaller student models.Experiments across varied scenarios reveal that the teacherstudent scheme with DRI notably improves the response abilities of smaller models, even outperforming the teacher model in some cases.The dataset and codes are available 1 .

In conversational AI, large-scale models excel in various tasks but struggle with target-driven conversation planning.Current methods, such as chain-of-thought reasoning and tree-search policy learning techniques, either neglect plan rationality or require extensive human simulation procedures.Addressing this, we propose a novel two-stage framework, named EnPL, to improve the LLMs' capability in planning conversations towards designated targets, including (1) distilling natural language plans from target-driven conversation corpus and (2) generating new plans with demonstration-guided in-context learning.Specifically, we first propose a filter approach to distill a high-quality plan dataset, ConvPlan 1 .With the aid of corresponding conversational data and support from relevant knowledge bases, we validate the quality and rationality of these plans.Then, these plans are leveraged to help guide LLMs to further plan for new targets.Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations.Furthermore, EnPL is demonstrated to be quite effective in collecting target-driven conversation datasets and enhancing response generation, paving the way for constructing extensive target-driven conversational models.

In this work, we propose a hyperparameter optimization method named HyperTime to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based on this observation, we leverage the 'worst-case-oriented' philosophy from the robust optimization literature to help find such robust hyperparameter configurations. HyperTime imposes a lexicographic priority order on average validation loss and worst-case validation loss over chronological validation sets. We perform a theoretical analysis on the upper bound of the expected test loss, which reveals the unique advantages of our approach. We also demonstrate the strong empirical performance of the proposed method on multiple machine learning tasks with temporal distribution shifts. The algorihtm is available in ~https://microsoft.github.io/FLAML/.

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by establishing a new taxonomy concerning three key dimensions of human-centered PCAs, namely Intelligence, Adaptivity, and Civility. We discuss potential research opportunities and challenges based on this new taxonomy upon the five stages of PCA system construction. This perspectives paper lays a foundation for the emerging area of conversational information retrieval research and paves the way towards advancing human-centered proactive conversational systems.

With the popularity of social networking services, big social data analytics emerged in various applications, such as discovering trending topics, monitoring public sentiment, and identifying human mobility patterns. In this paper, we take the opportunity of The 2017 Great American Eclipse, a once-in-a-lifetime event, to look into its potential social, emotional, and human movement impacts at the national level. Specifically, we collected more than five million English eclipse- mentioning tweets in a real-time manner using Twitter Streaming APIs. Then we profiled spatio- temporal distributions of the data, extracted both hashtagged and latent topics, analyzed emotions using polarized words, emojis and emoticons, and revealed both interstate and intrastate eclipse- chasing travel patterns. Our study provides a comprehensive example of understanding big social data and its associated influence from diverse perspectives.