How can we efficiently and accurately factorize multi-source data in dynamic and real-time environments? Coupled matrix-tensor factorization (CMTF) is a powerful tool for such tasks, but existing methods often struggle with scalability, particularly when dealing with continuously streaming data. Traditional CMTF approaches, while effective at capturing complex relationships, suffer from computational inefficiencies and the need for retraining as new data arrive. Moreover, many techniques fail to properly incorporate the inherent temporal characteristics of the data, which could significantly enhance both accuracy and convergence speed.

How can we accurately recognize overlapping entity spans in text while effectively capturing global context among spans? Nested Named Entity Recognition (nested NER) becomes challenging in the presence of nested or overlapping entity spans. Traditional span-based methods enumerate all possible spans, resulting in high computational costs and severe label imbalance from excessive negative spans which are non-entities. Additionally, they often fail to fully capture global context among overlapping entities.

Modeling the progression of neurodegenerative diseases such as Alzheimer’s disease (AD) is crucial for early detection and prevention given their irreversible nature. However, the scarcity of longitudinal data and complex disease dynamics make the analysis highly challenging. Moreover, longitudinal samples often contain irregular and large intervals between subject visits, which underscore the necessity for advanced data generation techniques that can accurately simulate disease progression over time. In this regime, we propose a novel conditional generative model for synthesizing longitudinal sequences and present its application to neurodegenerative disease data generation conditioned on multiple time-dependent ordinal factors, such as age and disease severity. Our method sequentially generates continuous data by bridging gaps between sparse data points with a diffusion model, ensuring a realistic representation of disease progression. The synthetic data are curated to integrate both cohort-level and individual-specific characteristics, where the cohort-level representations are modeled with an ordinal regression to capture longitudinally monotonic behavior. Extensive experiments on four AD biomarkers validate the superiority of our method over nine baseline approaches, highlighting its potential to be applied to a variety of longitudinal data generation.

How can we accurately decompose a temporal irregular tensor along with a related knowledge graph tensor? The PARAFAC2 decomposition is widely used for analyzing irregular tensors composed of matrices with varying row sizes. Recent advancements in PARAFAC2 methods primarily focus on capturing dynamic features that change over time, since data irregularities often arise from temporal fluctuations. However, these methods often neglect static features, such as knowledge information, which remain constant over time.In this paper, we propose KG-CTF (Knowledge Graph-based Coupled Tensor Factorization), a coupled tensor factorization method designed to capture both dynamic and static features within an irregular tensor. To incorporate knowledge graph tensors as static features, KG-CTF couples an irregular temporal tensor with a knowledge graph tensor that share a common axis. Additionally, KG-CTF employs a relational regularization to capture relationships among the factor matrices of the knowledge graph tensor. For accelerated convergence of the factor matrices, KG-CTF utilizes momentum update techniques. Extensive experiments show that KG-CTF reduces error rates by up to 1.64× compared to existing PARAFAC2 methods.

Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case in practice. Federated Semi-Supervised Learning (FSSL) addresses this label deficiency problem, targeting situations where only the server has a small amount of labeled data while clients do not. However, a significant performance gap exists between Centralized Semi-Supervised Learning (SSL) and FSSL. This gap arises from confirmation bias, which is more pronounced in FSSL due to multiple local training epochs and the separation of labeled and unlabeled data. We propose $(FL)^2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization. We show that regularizing the original pseudo-labeling loss is suboptimal, and hence we carefully select unlabeled samples for regularization. We further introduce client-specific adaptive thresholding and learning status-aware aggregation to adjust the training process based on the learning progress of each client. Our experiments on three benchmark datasets demonstrate that our approach significantly improves performance and bridges the gap with SSL, particularly in scenarios with scarce labeled data.

Psychiatrists diagnose mental disorders via the linguistic use of patients.Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices.We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning.We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones.We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing.Our IRBapproved evaluation of the prediction of selfreported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AU-ROC improvement and 8.21% MAE reduction.

Various automated eating detection wearables have been proposed to monitor food intakes. While these systems overcome the forgetfulness of manual user journaling, they typically show low accuracy at outside-the-lab environments or have intrusive form-factors (e.g., headgear). Eyeglasses are emerging as a socially-acceptable eating detection wearable, but existing approaches require custom-built frames and consume large power. We propose MyDJ, an eating detection system that could be attached to any eyeglass frame. MyDJ achieves accurate and energy-efficient eating detection by capturing complementary chewing signals on a piezoelectric sensor and an accelerometer. We evaluated the accuracy and wearability of MyDJ with 30 subjects in uncontrolled environments, where six subjects attached MyDJ on their own eyeglasses for a week. Our study shows that MyDJ achieves 0.919 F1-score in eating episode coverage, with 4.03 × battery time over the state-of-the-art systems. In addition, participants reported wearing MyDJ was almost as comfortable (94.95%) as wearing regular eyeglasses.

According a research report by Fortune Business Insights™, the global Electric Vehicles market is expected to grow from USD 287.36 billion in 2021 to USD 1,318.22 billion in 2028. This is mainly due to the growing consumer interest and rising concerns over environment.

While there are many research proposals for wearable Automatic Dietary Monitoring (ADM) systems that detect eating of a user, it is difficult to notice real-world users wearing such devices in public. We propose a new wearable ADM system that could be used daily by real-world users. It is designed in a form of necklace, providing natural and firm contact of sensor on user's skin to accurately capture eating activities. At our preliminary experiments, our wearable ADM system detected eating of a user with 86.1% accuracy.

This correspondence presents the application of an existing deadlock-free resource allocation control method for a reconfigurable manufacturing system (RMS) with serial and parallel configuration, and further proposes a new higher level deadlock avoidance control method. RMSs have been introduced to replace traditional large-volume production systems such as dedicated manufacturing systems, adding more flexibility and convertibility. Such manufacturing systems require that their controls be changed rapidly to cope with unpredictable market demands; their desired control behaviors must also be verified in advance of running the system to reduce the ramp-up time. One important desired control behavior is the deadlock freeness in the resource allocation. A rule-based matrix method that has been proposed to develop deadlock-free resource allocation control is applied to an example RMS with serial and parallel configuration. Through this application, higher level deadlocks were found in the example RMS that are not prevented with the existing method. A new control method to avoid the higher level deadlocks is developed.

The main purpose of this paper is to model and analyze a reconfigurable manufacturing system (RMS) applying existing supervisory controller design and analysis methods. The RMS introduced in this paper was originally developed to replace traditional large-volume production lines, allowing more flexibility. In this paper, the modelling for supervisory controllers and the analysis of the deadlock for RMS are performed with two existing supervisory controller design and analysis methods. One method is based on the Petri net (PN) synthesis method using resource control nets (RCN). The other is a rule-based matrix formalism constructed with traditional industrial engineering tools. A typical example of RMS with serial and parallel part-flow is presented. Also, the applicability of each method for RMS is investigated.