Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addressing this unfairness problem is important for equally attending to all variables and avoiding vulnerable model biases/risks. However, fair MTS forecasting is challenging and has been less studied in the literature. To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables. Accordingly, we propose a novel framework, named FairFor, for fairness-aware MTS forecasting. FairFor is based on adversarial learning to generate both group-independent and group-relevant representations for the downstream forecasting. The framework first leverages a spectral relaxation of the K-means objective to infer variable correlations and thus to group variables. Then, it utilizes a filtering&fusion component to filter the group-relevant information and generate group-independent representations via orthogonality regularization. The group-independent and group-relevant representations form highly informative representations, facilitating to sharing knowledge from advantaged variables to disadvantaged variables to guarantee fairness. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed FairFor for fair forecasting and significant performance improvement.Comment: 13 pages, 5 figures, accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

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A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. It is also unclear why FT can enhance time series analysis and what its limitations in the field are. To address these gaps, we present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT. Specifically, we explore the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis. We propose a novel taxonomy to categorize the existing methods in this field, providing a structured overview of the diverse approaches employed in incorporating FT into deep learning models for time series analysis. Finally, we highlight the advantages and limitations of FT for time series modeling and identify potential future research directions that can further contribute to the community of time series analysis

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, another kind of approaches based on multi-layer perceptrons (MLPs) are proposed with simple structure, low complexity, and {superior performance}. However, most MLP-based forecasting methods suffer from the point-wise mappings and information bottleneck, which largely hinders the forecasting performance. To overcome this problem, we explore a novel direction of applying MLPs in the frequency domain for time series forecasting. We investigate the learned patterns of frequency-domain MLPs and discover their two inherent characteristic benefiting forecasting, (i) global view: frequency spectrum makes MLPs own a complete view for signals and learn global dependencies more easily, and (ii) energy compaction: frequency-domain MLPs concentrate on smaller key part of frequency components with compact signal energy. Then, we propose FreTS, a simple yet effective architecture built upon Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components. The above stages operated on both inter-series and intra-series scales further contribute to channel-wise and time-wise dependency learning. Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods

Radiofrequency ablation with or without transarterial chemoembolization for hepatocellular carcinoma meeting Milan criteria: a focus on tumor progression and recurrence patterns

Background/objectiveThe aim of this study was to evaluate tumor progression and recurrence patterns of radiofrequency ablation (RFA) with or without transarterial chemoembolization (TACE) for treating hepatocellular carcinoma (HCC) that meets Milan criteria.MethodsThis retrospective study included consecutive HCC patients meeting Milan criteria who underwent percutaneous RFA with or without TACE as initial treatment at a tertiary academic center between December 2017 and 2022. Technical success rate, local recurrence-free survival (LRFS), progression-free survival (PFS) and recurrence patterns were recorded.ResultsA total of 135 HCC patients (109 male [80.7%]) with a mean age of 62 years and 147 target lesions were retrospectively enrolled. The technical success rate was 99.3%. The median LRFS was 60 months, and the cumulative 1-, 3-, and 5-year LRFS were 88.9%, 70.1%, and 30.0%, respectively. Additionally, the median PFS was 23 months, with cumulative 1-, 3-, and 5-year PFS of 74%, 30%, and 0%, respectively. Multivariate analysis confirmed that age > 60, alpha-fetoprotein (AFP) (> 10), and albumin were associated with PFS (2.34, p = 0.004; 1.96, p = 0.021; 0.94, p = 0.007, respectively). Six recurrence patterns were identified: local tumor progression (LTP) alone (n = 15, 25.0%), intrahepatic distant recurrence (IDR) alone (n = 34, 56.7%), extrahepatic recurrence (ER) alone (n = 2, 3.3%), IDR + ER (n = 2, 3.3%), LTP + IDR (n = 5, 8.8%), and LTP + IDR + ER (n = 2, 3.3%). IDR occurred most frequently as a sign of good local treatment.ConclusionsRFA in combination with TACE does not appear to provide an advantage over RFA alone in improving tumor progression in patients with HCC meeting the Milan criteria. However, further prospective studies are needed to confirm these findings and to determine the optimal treatment approach for this patient population

Comparative Transcriptome Analysis Reveals Candidate Genes and Pathways for Potential Branch Growth in Elm (Ulmus pumila) Cultivars

Wood plays a vital role in human life. It is important to study the thickening mechanism of tree branches and explore the mechanism of wood formation. Elm (Ulmus pumila) is a strong essential wood, and it is widely used in cabinets, sculptures, and ship making. In the present study, phenotypic and comparative transcriptomic analyses were performed in U. pumila fast- (UGu17 and UZuantian) and slow-growing cultivars (U81-07 and U82-39). Phenotypic observation showed that the thickness of secondary xylem of 2-year-old fast-growing branches was greater compared with slow-growing cultivars. A total of 9367 (up = 4363, down = 5004), 7159 (3413/3746), 7436 (3566/3870), and 5707 (2719/2988) differentially expressed genes (DEGs) were identified between fast- and slow-growing cultivars. Moreover, GO and KEGG enrichment analyses predicted that many pathways were involved in vascular development and transcriptional regulation in elm, such as “plant-type secondary cell wall biogenesis”, “cell wall thickening”, and “phenylpropanoid biosynthesis”. NAC domain transcriptional factors (TFs) and their master regulators (VND1/MYB26), cellulose synthase catalytic subunits (CESAs) (such as IRX5/IRX3/IRX1), xylan synthesis, and secondary wall thickness (such as IRX9/IRX10/IRX8) were supposed to function in the thickening mechanism of elm branches. Our results indicated that the general phenylpropanoid pathway (such as PAL/C4H/4CL) and lignin metabolism (such as HCL/CSE/CCoAOMT/CCR/F5H) had vital functions in the growth of elm branches. Our transcriptome data were consistent with molecular results for branch thickening in elm cultivars

Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches