Graph-based social recommendation (SocialRec) has emerged as a powerful extension of graph collaborative filtering (GCF), which leverages graph neural networks (GNNs) to capture multi-hop collaborative signals from user-item interactions. These methods enrich user representations by incorporating social network information into GCF, thereby integrating additional collaborative signals from social relations. However, existing GCF and graph-based SocialRec approaches face significant challenges: they incur high computational costs and suffer from limited scalability due to the large number of parameters required to assign explicit embeddings to all users and items. In this work, we propose PULSE (Parameter-efficient User representation Learning with Social Knowledge), a framework that addresses this limitation by constructing user representations from socially meaningful signals without creating an explicit learnable embedding for each user. PULSE reduces the parameter size by up to 50% compared to the most lightweight GCF baseline. Beyond parameter efficiency, our method achieves state-of-the-art performance, outperforming 13 GCF and graph-based social recommendation baselines across varying levels of interaction sparsity, from cold-start to highly active users, through a time- and memory-efficient modeling process.

Self-supervised learning enables effective model pre-training on large-scale unlabeled data, which is crucial for user-specific fine-tuning in mobile sensing applications. However, pre-trained models often face significant domain shifts during fine-tuning due to user diversity, leading to performance degradation. To address this, we propose SelfReplay, an adaptive approach designed to align self-supervised models to different domains. SelfReplay consists of two stages: MetaSSL, which leverages meta-learning with self-supervised learning to pre-train domain-adaptive weights, and ReplaySSL, which further adapts the pre-trained model to each user's domain by replaying the meta-learned self-supervised task with a few user-specific samples. This produces a personalized model tailored to each user. Evaluations on mobile sensing benchmarks demonstrate that SelfReplay outperforms existing baselines, improving the F1-score by 9.4%p on average. On-device analyses on a commodity smartphone show the efficiency of SelfReplay's adaptation step, required just once after deployment, with SimCLR completing in only 10 seconds while using less than 100MB of memory.

Large language models (LLMs) have demonstrated exceptional abilities across various domains.However, utilizing LLMs for ubiquitous sensing applications remains challenging as existing text-prompt methods show significant performance degradation when handling long sensor data sequences.We propose a visual prompting approach for sensor data using multimodal LLMs (MLLMs).We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions.Additionally, we introduce a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge.We evaluated our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts and reducing token costs by 15.8×.Our findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks.The source code is available at https://github. com/diamond264/ByMyEyes. ### InstructionYou are an expert in sensor data analysis.Given the sensor data, determine the correct answer from the options listed in the question.Provide the answer with the format of ANSWER , where ANSWER corresponds to one of the options listed in the question.If the answer is not in the options, choose the most possible option.The ECG data is collected from a lead II ECG sensor.The ECG data is recorded over 10 seconds.The data is normalized with the statistics of the user's data.Please refer to the provided examples and use them to answer the following question for the target data.### Examples *Example of normal*: Average heartbeat in the ECG signal (list of ['lead II']): [-0.34, -0.34, -0.35, -0.35, -0.36, … ECG_P_Peaks in the ECG signal (list of (index, value)): [(21, 0.08), (129, 0.19), (239, 0.22), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(35, -0.61), (137, -0.46), (246, -0.48), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(42, -1.4), (149, -1.35), (260, -1.33), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(63, 2.18), (171, 2.11), (282, 2.31), … *Example of conduction disturbance*: Average heartbeat in the ECG signal (list of ['lead II']): [-0.15, -0.24, -0.29, -0.31, -0.28, … ECG_P_Peaks in the ECG signal (list of (index, value)): [(4, 0.14), (57, 0.3), (103, 0.22), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(14, -0.05), (65, -0.05), (109, -0.07), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(22, -2.28), (73, -2.1), (124, -2.35), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(82, 0.03), (142, 0.64), (245, 0.25), … ### Question Average heartbeat in the ECG signal (list of ['lead II']): [-0.39, -0.39, -0.39, -0.39, -0.4,… ECG_P_Peaks in the ECG signal (list of (index, value)): [(15, 0.14), (94, -0.26), (173, -0.23), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(23, -0.27), (102, -0.81), (182, -0.55), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(34, 0.15), (116, -0.51), (192, -0.45), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(50, 1.39), (130, 1.1), (209, 1.31), … *Question*: When the sensor data is used for a task for classifying ECG data into 2 categories: conduction disturbance, normal, what is the most likely answer among ['conduction disturbance', 'normal']?*Answer*: