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Human Activity Recognition (HAR) is essential in ubiquitous computing, with far-reaching real-world applications. While recent SOTA HAR research has demonstrated impressive performance, some key aspects remain under-explored. Firstly, HAR can be both highly contextualized and personalized. However, prior work has predominantly focused on being Context-Aware (CA) while largely ignoring the necessity of being User-Aware (UA). We argue that addressing the impact of innate user action-performing differences is equally crucial as considering external contextual environment settings in HAR tasks. Secondly, being user-aware makes the model acknowledge user discrepancies but does not necessarily guarantee mitigation of these discrepancies, i.e., unified predictions under the same activities. There is a need for a methodology that explicitly enforces closer (different user, same activity) representations. To bridge this gap, we introduce CLAUDIA, a novel framework designed to address these issues. Specifically, we expand the contextual scope of the CA-HAR task by integrating User Identification (UI) within the CA-HAR framework, jointly predicting both CA-HAR and UI in a new task called User and Context-Aware HAR (UCA-HAR). This approach enriches personalized and contextual understanding by jointly learning user-invariant and user-specific patterns. Inspired by SOTA designs in the visual domain, we introduce a supervised contrastive loss objective on instance-instance pairs to enhance model efficacy and improve learned feature quality. Evaluation across three real-world CA-HAR datasets reveals substantial performance enhancements, with average improvements ranging from 5.8% to 14.1% in Matthew's Correlation Coefficient and 3.0% to 7.2% in Macro F1 score., Comment: Accepted in ICMLA 2024

Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different device placements). This paper proposes a Deep Heterogeneous Contrastive Hyper-Graph Learning (DHC-HGL) framework that captures heterogenous Context-Aware HAR (CA-HAR) hypergraph properties in a message-passing and neighborhood-aggregation fashion. Prior work only explored homogeneous or shallow-node-heterogeneous graphs. DHC-HGL handles heterogeneous CA-HAR data by innovatively 1) Constructing three different types of sub-hypergraphs that are each passed through different custom HyperGraph Convolution (HGC) layers designed to handle edge-heterogeneity and 2) Adopting a contrastive loss function to ensure node-heterogeneity. In rigorous evaluation on two CA-HAR datasets, DHC-HGL significantly outperformed state-of-the-art baselines by 5.8% to 16.7% on Matthews Correlation Coefficient (MCC) and 3.0% to 8.4% on Macro F1 scores. UMAP visualizations of learned CA-HAR node embeddings are also presented to enhance model explainability., Comment: IMWUT 2023

Context-aware Human Activity Recognition (CHAR) is challenging due to the need to recognize the user's current activity from signals that vary significantly with contextual factors such as phone placements and the varied styles with which different users perform the same activity. In this paper, we argue that context-aware activity visit patterns in realistic in-the-wild data can equivocally be considered as a general graph representation learning task. We posit that exploiting underlying graphical patterns in CHAR data can improve CHAR task performance and representation learning. Building on the intuition that certain activities are frequently performed with the phone placed in certain positions, we focus on the context-aware human activity problem of recognizing the tuple. We demonstrate that CHAR data has an underlying graph structure that can be viewed as a heterogenous hypergraph that has multiple types of nodes and hyperedges (an edge connecting more than two nodes). Subsequently, learning representations becomes a graph node representation learning problem. After task transformation, we further propose a novel Heterogeneous HyperGraph Neural Network architecture for Context-aware Human Activity Recognition (HHGNN-CHAR), with three types of heterogeneous nodes (user, phone placement, and activity). Connections between all types of nodes are represented by hyperedges. Rigorous evaluation demonstrated that on an unscripted, in-the-wild CHAR dataset, our proposed framework significantly outperforms state-of-the-art (SOTA) baselines including CHAR models that do not exploit graphs, and GNN variants that do not incorporate heterogeneous nodes or hyperedges with overall improvements 14.04% on Matthews Correlation Coefficient (MCC) and 7.01% on Macro F1 scores., Comment: PerCom 2023

The morphology of molecular clouds is crucial for understanding their origin and evolution. In this work, we investigate the morphology of the filamentary molecular clouds (filaments for short) using a portion of the $^{12}\text{CO} (J=1-0)$ data from the Milky Way Imaging Scroll Painting (MWISP) project. The data cover an area spanning $104.75^\circ

A foil-microchannel plate (MCP) detector, which uses electrostatic lenses and possesses both good position and timing resolutions, has been designed and simulated for beam diagnostics and mass measurements at the next-generation heavy-ion-beam facility HIAF in China. Characterized by low energy loss and good performances of timing and position measurements, it would be located at focal planes in fragment separator HFRS for position monitoring, beam turning, B${\rho}$ measurement, and trajectory reconstruction. Moreover, it will benefit the building-up of a magnetic-rigidity-energy-loss-time-of-flight (B${\rho}$-$\Delta$E-TOF) method at HFRS for high-precision in-flight particle identification (PID) of radioactive isotope (RI) beams on an event-by-event basis. Most importantly, the detector can be utilized for in-ring TOF and position measurements, beam-line TOF measurements at two achromatic foci, and position measurements at a dispersive focus of HFRS, thus making it possible to use two complementary mass measurement methods (isochronous mass spectrometry (IMS) at the storage ring SRing and magnetic-rigidity-time-of-flight (B${\rho}$-TOF) at the beam-line HFRS) in one single experimental run., Comment: 10 pages, 6 figures, NST

Laser cooling of relativistic heavy ion beams of Li-like C$^{3+}$ and O$^{4+}$ is being in preparation at the experimental Cooler Storage Ring (CSRe). Recently, a preparatory experiment to test important prerequisites for laser cooling of relativistic $^{12}$C$^{3+}$ ion beams using a pulsed laser system has been performed at the CSRe. Unfortunately, the interaction between the ions and the pulsed laser cannot be detected. In order to study the laser cooling process and find the optimized parameters for future laser cooling experiment, a multi-particle tracking method was developed to simulate the detailed longitudinal dynamics of laser-cooled ion beams at the CSRe. The simulations of laser cooling of the $^{12}$C$^{3+}$ ion beams by scanning the frequency of RF-buncher or continuous wave (CW) laser wavelength were performed. The simulation results indicated that a large momentum spread ion beams could be laser-cooled by the combination of only one CW laser and the RF-buncher and shown the requirements of a successful laser cooling experiment. The optimized parameters for scanning the RF-buncher frequency or laser frequency were obtained. Furthermore, the heating effects were estimated for laser cooling at the CSRe. The Schottky noise spectra of longitudinally modulated and laser-cooled ion beams was simulated to fully explain and anticipate the experimental results. The combination of Schottky spectra from the highly sensitive resonant Schottky pick-up and the simulation methods developed in this paper will be helpful to investigate the longitudinal dynamics of RF-bunched and ultra-cold ion beams in the upcoming laser cooling experiments at the CSRe., Comment: To be published in Chinese Physics C