Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation

Encoding frequency stability constraints in the operation problem is challenging due to its complex dynamics. Recently, data-driven approaches have been proposed to learn the stability criteria offline with the trained model embedded as a constraint of online optimization. However, random sampling of stationary operation points is less efficient in generating balanced stable and unstable samples. Meanwhile, the performance of such a model is strongly dependent on the quality of the training dataset. Observing this research gap, we propose a gradient-based data generation method via forward-mode automatic differentiation. In this method, the original dynamic system is augmented with new states that represent the dynamic of sensitivities of the original states, which can be solved by invoking any ODE solver for a single time. To compensate for the contradiction between the gradient of various frequency stability criteria, gradient surgery is proposed by projecting the gradient on the normal plane of the other. In the end, we demonstrate the superior performance of the proposed sampling algorithm, compared with the unrolling differentiation and finite difference. All codes are available at https://github.com/xuwkk/frequency_sample_ad.

Cardinality Estimation on Hyper-relational Knowledge Graphs

Cardinality Estimation (CE) for query is to estimate the number of results without execution, which is an effective index in query optimization. Recently, CE over has achieved great success in knowledge graphs (KGs) that consist of triple facts. To more precisely represent facts, current researchers propose hyper-relational KGs (HKGs) to represent a triple fact with qualifiers, where qualifiers provide additional context to the fact. However, existing CE methods over KGs achieve unsatisfying performance on HKGs due to the complexity of qualifiers in HKGs. Also, there is only one dataset for HKG query cardinality estimation, i.e., WD50K-QE, which is not comprehensive and only covers limited patterns. The lack of querysets over HKG also becomes a bottleneck to comprehensively investigate CE problems on HKGs. In this work, we first construct diverse and unbiased hyper-relational querysets over three popular HKGs for investigating CE. Besides, we also propose a novel qualifier-attached graph neural network (GNN) model that effectively incorporates qualifier information and adaptively combines outputs from multiple GNN layers, to accurately predict the cardinality. Our experiments illustrate that the proposed hyper-relational query encoder outperforms all state-of-the-art CE methods over three popular HKGs on the diverse and unbiased benchmark.

Continual Learning for Smart City: A Survey

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) Application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.

Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning

Recent works have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought (CoT) rationales or using them as correct examples in few-shot prompting. While humans can indeed imitate correct examples, learning from our mistakes is another vital aspect of human cognition. Hence, a question naturally arises: \textit{can LLMs learn and benefit from their mistakes, especially for their reasoning? } This study investigates this problem from both the prompting and model-tuning perspectives. We begin by introducing \textsc{CoTErrorSet}, a new benchmark with 609,432 questions, each designed with both correct and error references, and demonstrating the types and reasons for making such mistakes. To explore the effectiveness of those mistakes, we design two methods: (1) \textbf{Self-rethinking} prompting guides LLMs to rethink whether they have made similar previous mistakes; and (2) \textbf{Mistake tuning} involves finetuning models in both correct and incorrect reasoning domains, rather than only tuning models to learn ground truth in traditional methodology. We conduct a series of experiments to prove LLMs can obtain benefits from mistakes in both directions. Our two methods offer potentially cost-effective strategies by leveraging errors to enhance reasoning capabilities, which costs significantly less than creating meticulously hand-crafted golden references. We ultimately make a thorough analysis of the reasons behind LLMs' errors, which provides directions that future research needs to overcome. \textsc{CoTErrorSet} will be published soon on \texttt{Anonymity Link}.

Self-supervised Contrastive Learning for Implicit Collaborative Filtering

Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering. However, the presence of false-positive and false-negative examples in recommendation systems hampers accurate preference learning. In this study, we propose a simple self-supervised contrastive learning framework that leverages positive feature augmentation and negative label augmentation to improve the self-supervisory signal. Theoretical analysis demonstrates that our learning method is equivalent to maximizing the likelihood estimation with latent variables representing user interest centers. Additionally, we establish an efficient negative label augmentation technique that samples unlabeled examples with a probability linearly dependent on their relative ranking positions, enabling efficient augmentation in constant time complexity. Through validation on multiple datasets, we illustrate the significant improvements our method achieves over the widely used BPR optimization objective while maintaining comparable runtime.

A Survey of Route Recommendations: Methods, Applications, and Opportunities

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens` travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of traditional machine learning and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) Application\-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.

Learning Contrastive Feature Representations for Facial Action Unit Detection

The predominant approach to facial action unit (AU) detection revolves around a supervised multi-label binary classification problem. Existing methodologies often encode pixel-level information of AUs, thereby imposing substantial demands on model complexity and expressiveness. Moreover, this practice elevates the susceptibility to overfitting due to the presence of noisy AU labels. In the present study, we introduce a contrastive learning framework enhanced by both supervised and self-supervised signals. The objective is to acquire discriminative features, deviating from the conventional pixel-level learning paradigm within the domain of AU detection. To address the challenge posed by noisy AU labels, we augment the supervised signal through the introduction of a self-supervised signal. This augmentation is achieved through positive sample sampling, encompassing three distinct types of positive sample pairs. Furthermore, to mitigate the imbalanced distribution of each AU type, we employ an importance re-weighting strategy tailored for minority AUs. The resulting loss, denoted as AUNCE, is proposed to encapsulate this strategy. Our experimental assessments, conducted on two widely-utilized benchmark datasets (BP4D and DISFA), underscore the superior performance of our approach compared to state-of-the-art methods in the realm of AU detection.

LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras

Leveraging the rich information extracted from light field (LF) cameras is instrumental for dense prediction tasks. However, adapting light field data to enhance Salient Object Detection (SOD) still follows the traditional RGB methods and remains under-explored in the community. Previous approaches predominantly employ a custom two-stream design to discover the implicit angular feature within light field cameras, leading to significant information isolation between different LF representations. In this study, we propose an efficient paradigm (LF Tracy) to address this limitation. We eschew the conventional specialized fusion and decoder architecture for a dual-stream backbone in favor of a unified, single-pipeline approach. This comprises firstly a simple yet effective data augmentation strategy called MixLD to bridge the connection of spatial, depth, and implicit angular information under different LF representations. A highly efficient information aggregation (IA) module is then introduced to boost asymmetric feature-wise information fusion. Owing to this innovative approach, our model surpasses the existing state-of-the-art methods, particularly demonstrating a 23% improvement over previous results on the latest large-scale PKU dataset. By utilizing only 28.9M parameters, the model achieves a 10% increase in accuracy with 3M additional parameters compared to its backbone using RGB images and an 86% rise to its backbone using LF images. The source code will be made publicly available at https://github.com/FeiBryantkit/LF-Tracy.

Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on pre-defined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatio-temporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning

Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of attention within the constrained optimization (CO) and machine learning (ML) communities towards combining prediction and optimization models. The so-called end-to-end (E2E) learning captures the task-based objective for which they will be used for decision making. Although a large variety of E2E algorithms have been presented, it has not been fully investigated how to systematically address uncertainties involved in such models. Most of the existing work considers the uncertainties of ML in the input space and improves robustness through adversarial training. We extend this idea to E2E learning and prove that there is a robustness certification procedure by solving augmented integer programming. Furthermore, we show that neglecting the uncertainty of COs during training causes a new trigger for generalization errors. To include all these components, we propose a unified framework that covers the uncertainties emerging in both the input feature space of the ML models and the COs. The framework is described as a robust optimization problem and is practically solved via end-to-end adversarial training (E2E-AT). Finally, the performance of E2E-AT is evaluated by a real-world end-to-end power system operation problem, including load forecasting and sequential scheduling tasks.