With the increasing concerns about privacy and data regulations, federated learning (FL) has been emerging as a solution to train machine learning models collaboratively with non-exchangeable data from multiple clients. As a result of data locality, data is usually not identically or independently (non-IID) distributed across clients, and the non-IID property has long been the key challenge in FL. Furthermore, in real-world cross-silo scenarios, it is ubiquitous that clients are organizations owning private data from multiple domains internally, which exacerbates the non-IID issue. For example, in healthcare applications, each client (hospital) gathers data from patients with heterogeneous demographics. While previous works have made efforts to address the non-IID challenge across clients by assuming various relations among client-level data distributions and enabling personalized models at the client level, they ignore the internal data heterogeneity within each client or require explicit data domain indicators, which are hardly accessible in real-world data. Here, we propose Sample-Level Prototypical Federated Learning (SL-PFL) to bridge the gap. SL-PFL incorporates prototypical learning under the FL framework and provides a fine-grained personalized model for each data sample instead of learning one uniform model for all samples of each client. Meanwhile, it can be trained using data without ground-truth domain indicators. Experimental results demonstrate that our proposed method with sample-level personalized models outperforms existing FL methods with a global model or client-level personalized models on various real-world regression and classification tasks from weather, computer vision, and healthcare applications.

The rise of Large Visual-Language Models (LVLMs) has unlocked new possibilities for seamlessly integrating visual and textual information. However, their ability to interpret cartographic maps remains largely unexplored. In this paper, we introduce CartoMapQA, a benchmark specifically designed to evaluate LVLMs' understanding of cartographic maps through question-answering tasks. The dataset includes over 2000 samples, each composed of a cartographic map, a question (with open-ended or multiple-choice answers), and a ground-truth answer. These tasks span key low-, mid- and high-level map interpretation skills, including symbol recognition, embedded information extraction, scale interpretation, and route-based reasoning. Our evaluation of both open-source and proprietary LVLMs reveals persistent challenges: models frequently struggle with map-specific semantics, exhibit limited geospatial reasoning, and are prone to Optical Character Recognition (OCR)-related errors. By isolating these weaknesses, CartoMapQA offers a valuable tool for guiding future improvements in LVLM architectures. Ultimately, it supports the development of models better equipped for real-world applications that depend on robust and reliable map understanding, such as navigation, geographic search, and urban planning. Our source code and data are openly available to the research community at: https://github.com/ungquanghuy-kddi/CartoMapQA.git

Multivariate long-term time series forecasting (MLTSF), applicable across various domains, has gained increasing research attention. Channel-independent (CI) models, including Linear and Transformer-based architectures, have recently achieved state-of-the-art (SOTA) performance for MLTSF. Notably, Linear models can deliver satisfactory forecasting performance even with just a single linear projection layer. However, we identify a limitation in this architecture: a single linear projection struggles to adequately capture the inter- and intra-variate heterogeneity in temporal patterns. Similarly, any complex models like Transformer-based models that use a single projection layer to generate final predictions, may face capacity bottlenecks. To overcome this, we propose the Mixture of Projection Experts (MoPE), which replaces the single linear projection with multiple projection branches, and employs a gate network to dynamically assign weights to each branch based on the input data. We applied MoPE to multiple SOTA models and evaluated it on nine real-world datasets. Results show that MoPE boosts forecasting accuracy by an average of 9.59%, demonstrating its effectiveness in mitigating the limitations of a single projection layer. Additionally, our experiments demonstrate that integrating our proposal into CI models enhances their generalization to unseen variates. Interpretability analysis also reveals MoPE's ability to disentangle different temporal patterns. Overall, our paper establishes MoPE as an effective solution for MLTSF tasks.

Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc. Real world time series, i.e., large-scale irregular or sparse and intermittent time series, raise significant challenges to existing work attempting to estimate treatment effects. Specifically, the existence of hidden confounders can lead to biased treatment estimates and complicate the causal inference process. In particular, anomaly hidden confounders which exceed the typical range can lead to high variance estimates. Moreover, in continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality. In this paper, we leverage recent advances in Lipschitz regularization and neural controlled differential equations (CDE) to develop an effective and scalable solution, namely LipCDE, to address the above challenges. LipCDE can directly model the dynamic causal relationships between historical data and outcomes with irregular samples by considering the boundary of hidden confounders given by Lipschitz constrained neural networks. Furthermore, we conduct extensive experiments on both synthetic and real world datasets to demonstrate the effectiveness and scalability of LipCDE.

A major issue with real-time monitoring is to collect complete data. Hardware or software failures, network issues or, more frequently, time delays can disrupt such a collection. This results in having two versions of the same information: one in real-time but with potentially missing data, and the another, albeit complete, is delayed. Many works have studied how to handle missing data for classification and prediction. However, to the best of our knowledge, they do not consider how to leverage the delayed complete data to assist in learning the representation of real-time available data with missing values. This is despite the fact that the delayed complete data contain all the information (e.g., periodicities and trends). In this paper, we propose a framework to enhance the representation learning of the real-time available data by aligning the representation of past real-time but with missing data to that of past delayed but complete data. We test both a distance metric and contrastive learning to achieve this alignment. We implement our framework on a Transformer-based model and experiment it on three datasets. The efficiency of our solution is evaluated against seven baselines and considering four distinct patterns of missing data. Our experiments show that this proposal has a significant improvement in prediction accuracy (5.21% on average) over the baselines.

Human activity recognition using ambient sensors has become particularly important due to social demands of applications in smart homes. To address the problem of labeling sensing data for every individual house, cross-house human activity recognition is proposed to use available labeled houses (source domains) to train recognition models for applying to unlabeled houses (target domains). In this paper, we propose a method of source domain selection for cross-house human activity recognition. We first improve the method for representing semantic relationships of sensors. To select the best similar source houses for a target house, we then propose a method for calculating similarity score between two houses. Using 19 houses of the CASAS dataset, we evaluate the recognition performance in target houses using models trained by several similar source houses, randomly selected houses, dissimilar source houses, and all source houses without selection. Experimental results illustrate that the average accuracy of models trained from the small number of the best similar houses achieve the best performance, and thus they confirm the effectiveness of our proposed method.

Spatiotemporal data aggregated over regions or time windows at various resolutions demonstrate heterogeneous patterns and dynamics in each resolution. Meanwhile, the multi-resolution characteristic provides rich contextual information, which is critical for effective long-sequence forecasting. The importance of such inter-resolution information is more significant in practical cases, where fine-grained data is usually collected via approaches with lower costs but also lower qualities compared to those for coarse-grained data. However, existing works focus on uni-resolution data and cannot be directly applied to fully utilize the aforementioned extra information in multi-resolution data. In this work, we propose Spatiotemporal Koopman Multi-Resolution Network (ST-KMRN), a physics-informed learning framework for long-sequence forecasting from multi-resolution spatiotemporal data. Our method jointly models data aggregated in multiple resolutions and captures the inter-resolution dynamics with the self-attention mechanism. We also propose downsampling and upsampling modules among resolutions to further strengthen the connections among data of multiple resolutions. Moreover, we enhance the modeling of intra-resolution dynamics with physics-informed modules based on Koopman theory. Experimental results demonstrate that our proposed approach achieves the best performance on the long-sequence forecasting tasks compared to baselines without a specific design for multi-resolution data.

With the deployment of smart homes, we find that human activity recognition (HAR) is essentially important to many applications, e.g., child/senior care, intelligent information push and exercise promotion. Although it is always better to build HAR model for each smart home to resolve the practical problem that homes have different floorplans or adopted sensors, it is intractable to acquire labeled data for each home due to cost and privacy. We thus propose a method to transfer the HAR model from multiple labeled source homes to the unlabeled target home. Specifically, we first generate transferable representations for the sensors of these homes, based on which we build the HAR model using the data of labeled source homes. Then, we employ the built HAR model into the unlabeled target home. Experiment results on CASAS dataset illustrate that our proposed method outperforms baseline methods in general and also avoids potential negative transfer caused by using only one source home.

Nowadays, advertising (ad) deliveries are conducted in a targeted manner to improve their effectiveness and efficiency. However, human behavior data in ad-platforms such as Web browsing history is complex and contains a lot of “noise”. On the other hand, information in the advertiser's domain (e.g. e-commerce sites) seems to contain less noise (e.g. product browsing history) with respect to ad-targeting. We introduce a new denoising method for behavioral ad-targeting based on the idea of feature distribution alignment induced by the advertiser's domain. This denoised distribution induction can be achieved by employing domain adversarial training with stabilization techniques. We evaluate our model on real world data originating from an e-commerce site and an ad-platform. The results of an ablation study have demonstrated the advantage of utilizing an advertiser's domain for denoising human behavior data of an ad-platform domain.

Behavioral targeting (BT) is an important function for a company to reach a wide range of potential users. Trans-domain BT which targets potential users of one (source) domain (e.g. E-Commerce) who lie in another (target) domain (e.g. Ad Network) is a promising method to expand the range. However, it is difficult for trans-domain BT to keep its targeting quality high in case when ID linkage across domains is limited. To realize high quality trans-domain BT with limited ID linkage, we propose a method to cross-connect private touchpoints to users in each domain, which we call Virtual Touch-Point (VTP). Here, we utilize transfer learning to acquire knowledge to tie two domains. We made a VTP prototype by implementing typical transfer learning algorithms and evaluated its effectiveness using real-world data of two domains: (source) E-Commerce → (target) Ad Network.

In this paper, the user cooperation behaviors under eavesdropping attack are analyzed through game theory. Considering the physical layer security, we prove that the conventional cooperation scheme actually deteriorates the secrecy performance compared to the direct transmission, given that the eavesdropper has a better channel condition to the users than the destination. In this case, the necessary condition of the cooperation that the users should obtain additional utilities from the cooperation is not satisfied, which makes the users have no incentive to participate in the cooperation game. In order to motivate users, an adaptive cooperation scheme is designed to improve the secrecy performance even if the eavesdropping channel is superior to the destination's channel, and it is also observed that the mutual cooperation is one of the Nash equilibriums. We further exploit the Stackelberg game with a punishment mechanism to make the mutual cooperation as the unique Nash equilibrium.

In this paper, we present a framework to analyze the outage performance of the cooperative systems exploiting multiuser diversity (MUD). A two-hop wireless network consisting of a destination node, a relay node and multiple source nodes is considered. The asymptotic expressions of the system outage probability are derived for amplify-and-forward (AF) and decode-and-forward (DF) protocols. From these expressions it is shown that the diversity orders of K+1 and K can be achieved for AF and DF, respectively, where K is the number of source nodes in the network.

This Letter studies the two-path digital relaying systems, where two relay nodes alternately forward messages from the source to the destination. By applying the signal space diversity (SSD) technique, a novel adaptive scheme is proposed to deal with the inter-relay interference (IRI). Our work reveals that, with careful protocol design, the IRI becomes a beneficial resource that can be utilized rather than a detrimental factor that has to be suppressed. Simulation results demonstrate that, in high average SNR regions, the proposed method outperforms the existing alternatives in terms of symbol error probability.

This letter presents an analysis of the combined use of cooperative diversity and multiuser diversity (MUD) in multi-source multi-relay networks. A joint selection scheme, which selects the best source-relay pair to access the channel, is proposed. The main contribution of our work is the derivation of the exact and asymptotic expressions for the outage probability of the system with amplify-and-forward (AF) protocol. From these expressions it is indicated that the total diversity order ofM+Ncan be achieved, whereMandNare the number of source nodes and relay nodes, respectively. Based on the outage probability in high signal-to-noise-ratio (SNR) region, the optimum power allocation scheme is also given to improve the system performance.

This letter considers the amplify-and-forward (AF) system consisting of a destination node with M antennas, and a source node and N relay nodes with a single antenna each. The “best” relay, which is selected depending on the instantaneous and partial channel knowledge, is assigned to assist the source transmission. By deriving the lower and upper bounds for the outage probability of the system, we show that the diversity order of min(M,N) can be achieved. This analysis gives a promising solution to improve the diversity performance of partial relay selection scheme.