MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification

Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for medical time series, e.g., Electrocardiography (ECG) signals, can help for early detection and diagnosis. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods; with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. Our Code is available.

Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting

We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an energy amplification block and an energy restoration block. The energy amplification block enhances the energy of low-energy components to improve the model's learning efficiency for these components, while the energy restoration block returns the energy to its original level. Moreover, considering that the energy-amplified data typically displays two distinct energy peaks in the frequency spectrum, we integrate the energy amplification technique with a seasonal-trend forecaster to model the temporal relationships of these two peaks independently, serving as the backbone for our proposed model, Amplifier. Additionally, we propose a semi-channel interaction temporal relationship enhancement block for Amplifier, which enhances the model's ability to capture temporal relationships from the perspective of the commonality and specificity of each channel in the data. Extensive experiments on eight time series forecasting benchmarks consistently demonstrate our model's superiority in both effectiveness and efficiency compared to state-of-the-art methods.

Channel Sounding Using Multiplicative Arrays Based on Successive Interference Cancellation Principle

Ultra-massive multiple-input and multiple-output (MIMO) systems have been seen as the key radio technology for the advancement of wireless communication systems, due to its capability to better utilize the spatial dimension of the propagation channels. Channel sounding is essential for developing accurate and realistic channel models for the massive MIMO systems. However, channel sounding with large-scale antenna systems has faced significant challenges in practice. The real antenna array based (RAA) sounder suffers from high complexity and cost, while virtual antenna array (VAA) solutions are known for its long measurement time. Notably, these issues will become more pronounced as the antenna array configuration gets larger for future radio systems. In this paper, we propose the concept of multiplicative array (MA) for channel sounding applications to achieve large antenna aperture size with reduced number of required antenna elements. The unique characteristics of the MA are exploited for wideband spatial channel sounding purposes, supported by both one-path and multi-path numerical simulations. To address the fake paths and distortion in the angle delay profile issues inherent for MA in multipath channel sounding, a novel channel parameter estimation algorithm for MA based on successive interference cancellation (SIC) principle is proposed. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed SIC algorithm for the MA. This research contributes significantly to the channel sounding and characterization of massive MIMO systems for future applications.

Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset

Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate time series forecasting, where available variables in the inference phase are only a subset of the variables in the training phase. VSF presents significant challenges as the entire time series may be missing, and neither inter- nor intra-variable correlations persist. Such conditions impede the effectiveness of traditional imputation methods, primarily focusing on filling in individual missing data points. Inspired by the principle of feature engineering that not all variables contribute positively to forecasting, we propose Task-Oriented Imputation for VSF (TOI-VSF), a novel framework shifts the focus from accurate data recovery to directly support the downstream forecasting task. TOI-VSF incorporates a self-supervised imputation module, agnostic to the forecasting model, designed to fill in missing variables while preserving the vital characteristics and temporal patterns of time series data. Additionally, we implement a joint learning strategy for imputation and forecasting, ensuring that the imputation process is directly aligned with and beneficial to the forecasting objective. Extensive experiments across four datasets demonstrate the superiority of TOI-VSF, outperforming baseline methods by $15\%$ on average.

AD-DINO: Attention-Dynamic DINO for Distance-Aware Embodied Reference Understanding

Embodied reference understanding is crucial for intelligent agents to predict referents based on human intention through gesture signals and language descriptions. This paper introduces the Attention-Dynamic DINO, a novel framework designed to mitigate misinterpretations of pointing gestures across various interaction contexts. Our approach integrates visual and textual features to simultaneously predict the target object's bounding box and the attention source in pointing gestures. Leveraging the distance-aware nature of nonverbal communication in visual perspective taking, we extend the virtual touch line mechanism and propose an attention-dynamic touch line to represent referring gesture based on interactive distances. The combination of this distance-aware approach and independent prediction of the attention source, enhances the alignment between objects and the gesture represented line. Extensive experiments on the YouRefIt dataset demonstrate the efficacy of our gesture information understanding method in significantly improving task performance. Our model achieves 76.4% accuracy at the 0.25 IoU threshold and, notably, surpasses human performance at the 0.75 IoU threshold, marking a first in this domain. Comparative experiments with distance-unaware understanding methods from previous research further validate the superiority of the Attention-Dynamic Touch Line across diverse contexts.

FilterNet: Harnessing Frequency Filters for Time Series Forecasting

While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. However, forecasters based on Transformers are still suffering from vulnerability to high-frequency signals, efficiency in computation, and bottleneck in full-spectrum utilization, which essentially are the cornerstones for accurately predicting time series with thousands of points. In this paper, we explore a novel perspective of enlightening signal processing for deep time series forecasting. Inspired by the filtering process, we introduce one simple yet effective network, namely FilterNet, built upon our proposed learnable frequency filters to extract key informative temporal patterns by selectively passing or attenuating certain components of time series signals. Concretely, we propose two kinds of learnable filters in the FilterNet: (i) Plain shaping filter, that adopts a universal frequency kernel for signal filtering and temporal modeling; (ii) Contextual shaping filter, that utilizes filtered frequencies examined in terms of its compatibility with input signals for dependency learning. Equipped with the two filters, FilterNet can approximately surrogate the linear and attention mappings widely adopted in time series literature, while enjoying superb abilities in handling high-frequency noises and utilizing the whole frequency spectrum that is beneficial for forecasting. Finally, we conduct extensive experiments on eight time series forecasting benchmarks, and experimental results have demonstrated our superior performance in terms of both effectiveness and efficiency compared with state-of-the-art methods. Code is available at this repository: https://github.com/aikunyi/FilterNet

Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal

Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. Nonetheless, deep learning is data-hungry, and thus it may not be trivially applicable to enormous small cities with limited data. To this end, we propose Meta-Transfer Learning Empowered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance. Specifically, by modeling the ever-growing real estate transactions with associated residential communities as a temporal event heterogeneous graph, we first design an Event-Triggered Temporal Graph Network to model the irregular spatiotemporal correlations between evolving real estate transactions. Besides, we formulate the city-wide real estate appraisal as a multi-task dynamic graph link label prediction problem, where the valuation of each community in a city is regarded as an individual task. A Hypernetwork-Based Multi-Task Learning module is proposed to simultaneously facilitate intra-city knowledge sharing between multiple communities and task-specific parameters generation to accommodate the community-wise real estate price distribution. Furthermore, we propose a Tri-Level Optimization Based Meta- Learning framework to adaptively re-weight training transaction instances from multiple source cities to mitigate negative transfer, and thus improve the cross-city knowledge transfer effectiveness. Finally, extensive experiments based on five real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.

Multiple-Frequency-Bands Channel Characterization for In-vehicle Wireless Networks

In-vehicle wireless networks are crucial for advancing smart transportation systems and enhancing interaction among vehicles and their occupants. However, there are limited studies in the current state of the art that investigate the in-vehicle channel characteristics in multiple frequency bands. In this paper, we present measurement campaigns conducted in a van and a car across below 7 GHz, millimeter-wave (mmWave), and sub-Terahertz (Sub-THz) bands. These campaigns aim to compare the channel characteristics for in-vehicle scenarios across various frequency bands. Channel impulse responses (CIRs) were measured at various locations distributed across the engine compartment of both the van and car. The CIR results reveal a high similarity in the delay properties between frequency bands below 7GHz and mmWave bands for the measurements in the engine bay. Sparse channels can be observed at Sub-THz bands in the engine bay scenarios. Channel spatial profiles in the passenger cabin of both the van and car are obtained by the directional scan sounding scheme for three bands. We compare the power angle delay profiles (PADPs) measured at different frequency bands in two line of sight (LOS) scenarios and one non-LOS (NLOS) scenario. Some major \added{multipath components (MPCs)} can be identified in all frequency bands and their trajectories are traced based on the geometry of the vehicles. The angular spread of arrival is also calculated for three scenarios. The analysis of channel characteristics in this paper can enhance our understanding of in-vehicle channels and foster the evolution of in-vehicle wireless networks.

Empowering Pre-Trained Language Models for Spatio-Temporal Forecasting via Decoupling Enhanced Discrete Reprogramming

Spatio-temporal time series forecasting plays a critical role in various real-world applications, such as transportation optimization, energy management, and climate analysis. The recent advancements in Pre-trained Language Models (PLMs) have inspired efforts to reprogram these models for time series forecasting tasks, by leveraging their superior reasoning and generalization capabilities. However, existing approaches fall short in handling complex spatial inter-series dependencies and intrinsic intra-series frequency components, limiting their spatio-temporal forecasting performance. Moreover, the linear mapping of continuous time series to a compressed subset vocabulary in reprogramming constrains the spatio-temporal semantic expressivity of PLMs and may lead to potential information bottleneck. To overcome the above limitations, we propose \textsc{RePST}, a tailored PLM reprogramming framework for spatio-temporal forecasting. The key insight of \textsc{RePST} is to decouple the spatio-temporal dynamics in the frequency domain, allowing better alignment with the PLM text space. Specifically, we first decouple spatio-temporal data in Fourier space and devise a structural diffusion operator to obtain temporal intrinsic and spatial diffusion signals, making the dynamics more comprehensible and predictable for PLMs. To avoid information bottleneck from a limited vocabulary, we further propose a discrete reprogramming strategy that selects relevant discrete textual information from an expanded vocabulary space in a differentiable manner. Extensive experiments on four real-world datasets show that our proposed approach significantly outperforms state-of-the-art spatio-temporal forecasting models, particularly in data-scarce scenarios.

Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory

Privacy research has attracted wide attention as individuals worry that their private data can be easily leaked during interactions with smart devices, social platforms, and AI applications. Computer science researchers, on the other hand, commonly study privacy issues through privacy attacks and defenses on segmented fields. Privacy research is conducted on various sub-fields, including Computer Vision (CV), Natural Language Processing (NLP), and Computer Networks. Within each field, privacy has its own formulation. Though pioneering works on attacks and defenses reveal sensitive privacy issues, they are narrowly trapped and cannot fully cover people's actual privacy concerns. Consequently, the research on general and human-centric privacy research remains rather unexplored. In this paper, we formulate the privacy issue as a reasoning problem rather than simple pattern matching. We ground on the Contextual Integrity (CI) theory which posits that people's perceptions of privacy are highly correlated with the corresponding social context. Based on such an assumption, we develop the first comprehensive checklist that covers social identities, private attributes, and existing privacy regulations. Unlike prior works on CI that either cover limited expert annotated norms or model incomplete social context, our proposed privacy checklist uses the whole Health Insurance Portability and Accountability Act of 1996 (HIPAA) as an example, to show that we can resort to large language models (LLMs) to completely cover the HIPAA's regulations. Additionally, our checklist also gathers expert annotations across multiple ontologies to determine private information including but not limited to personally identifiable information (PII). We use our preliminary results on the HIPAA to shed light on future context-centric privacy research to cover more privacy regulations, social norms and standards.