Objective: We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from clinical records. Methods: We used four core DS information extraction tasks--namely, named entity recognition (NER: 2,949 clinical sentences), relation extraction (RE: 4,892 sentences), triple extraction (TE: 2,949 sentences), and usage classification (UC: 2,460 sentences) as our multitasks. We introduced a novel Retrieval-Augmented Multi-task Information Extraction (RAMIE) Framework, including: 1) employed instruction fine-tuning techniques with task-specific prompts, 2) trained LLMs for multiple tasks with improved storage efficiency and lower training costs, and 3) incorporated retrieval augmentation generation (RAG) techniques by retrieving similar examples from the training set. We compared RAMIE's performance to LLMs with instruction fine-tuning alone and conducted an ablation study to assess the contributions of multi-task learning and RAG to improved multitasking performance. Results: With the aid of the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 (3.51% improvement) on the NER task and demonstrated outstanding performance on the RE task with an F1 score of 93.74 (1.15% improvement). For the TE task, Llama2-7B scored 79.45 (14.26% improvement), and MedAlpaca-7B achieved the highest F1 score of 93.45 (0.94% improvement) on the UC task. The ablation study revealed that while MTL increased efficiency with a slight trade-off in performance, RAG significantly boosted overall accuracy. Conclusion: This study presents a novel RAMIE framework that demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records. Our framework can potentially be applied to other domains.

Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicators into parsimonious multimodal ones. Using a high-fidelity healthcare simulation context, we collected positional, audio, and physiological data, deriving 17 monomodal indicators. LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement, each capturing unique monomodal patterns. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious while offering higher explanatory power regarding students' task and collaboration performances. The findings highlight the potential of LCA in simplifying the analysis of complex multimodal data while capturing nuanced, cross-modality behaviours, offering actionable insights for educators and enhancing the design of collaborative learning interventions. This study proposes a pathway for advancing MMLA, making it more parsimonious and manageable, and aligning with the principles of learner-centred education.

Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and physiological circuits. This study builds on machine learning techniques -- such as dimensionality reduction, boosted decision trees, and anomaly classification -- to develop a conceptual framework for biological circuits. One problem is that machine learning circuits tend to be unrealistically large for use by cellular and physiological systems. I therefore focus on minimal circuits inspired by machine learning concepts, reduced to cellular scale. Through illustrative models, I demonstrate that small circuits can provide useful classification of anomalies. The analysis also shows how principles from machine learning -- such as temporal and atemporal anomaly detection, multivariate signal integration, and hierarchical decision-making cascades -- can inform hypotheses about the design and evolution of cellular circuits. This interdisciplinary approach enhances our understanding of cellular circuits and highlights the universal nature of computational strategies across biological and artificial systems.

Video recorded by a passenger at the Cincinnati/Northern Kentucky International Airport this year shows a federal agent seizing a traveler's bag. The Justice Department has now ordered the DEA to halt random searches at transit hubs. The Drug Enforcement Administration is no longer allowed to randomly search travelers at airports and other transit hubs after a scathing report from the Justice Department found "serious concerns" with the practice. DEA agents failed to properly document searches, may have illegally targeted minorities and, in at least one case, paid an airline employee tens of thousands of dollars over several years to suggest targets for searches, according to the report released Thursday by Justice Department Inspector General Michael Horowitz. The deputy attorney general ordered the DEA to suspend the random searches Nov. 12 after seeing a draft of the memo.

Artificial intelligence (AI) systems are increasingly supplementing or taking over tasks previously performed by humans. On the one hand, this relates to low-risk tasks, such as recommending books or movies, or recommending purchases based on previous buying behavior. But it also includes crucial decision making by highly autonomous systems. Many current systems are opaque in the sense that their internal principles of operation are unknown, leading to severe safety and regulation problems. Once trained, deep-learning systems perform well, but they are subject to surprising vulnerabilities when confronted with adversarial images.9 The decisions may be explicated after the fact, but these systems carry the risk of wrong decisions affecting the well being of people.

In her quest to design socially assistive robots--robots that provide social, not physical, support in realms like rehabilitation, education, and therapy--she realized that personalizing interactions would boost both engagement and outcomes. Artificial Intelligence (AI) has made that easier, though as always, surprises are never far when human beings are involved. Here, Matarić shares what she's learned about meeting people where they are. Let's talk about your work on socially assistive robots. You've said that having kids inspired you to build robots that help people. How did that interest develop into the mission of supporting specific behavioral interventions in health, wellness, and education?

Joe Wolfel is the CEO and founder of Terradepth. He is passionate about helping people make better and faster decisions regarding what we do (and don't do) in the ocean. Terradepth designs and builds ocean-going robots at scale, deploys them, and delivers data through an ocean data platform tailored for the maritime community. Prior to Terradepth, Joe has helped start a couple other companies, worked as a management consultant, and served as a US Navy SEAL officer with deployments throughout the Middle East and Africa. Joe was educated at the US Naval Academy.

Digital twins are widely considered enablers of groundbreaking changes in the development, operation, and maintenance of novel generations of products. They are meant to provide reliable and timely predictions to inform decisions along the entire product life cycle. One of their most interesting applications in the naval field is the digital twinning of ship performances in waves, a crucial aspect in design and operation safety. In this paper, a Bayesian extension of the Hankel dynamic mode decomposition method is proposed for ship motion's nowcasting as a prediction tool for naval digital twins. The proposed algorithm meets all the requirements for formulations devoted to digital twinning, being able to adapt the resulting models with the data incoming from the physical system, using a limited amount of data, producing real-time predictions, and estimating their reliability. Results are presented and discussed for the course-keeping of the 5415M model in beam-quartering sea state 7 irregular waves at Fr = 0.33, using data from three different CFD solvers. The results show predictions keeping good accuracy levels up to five wave encounter periods, with the Bayesian formulation improving the deterministic forecasts. In addition, a connection between the predicted uncertainty and prediction accuracy is found.

Throughout nature, the intricate and disordered lattice structures that are observed in bones, plant stems, dragonfly wings, coral, radiolarians [1], amongst many other examples, demonstrate how powerful geometry is for designing structures with extreme mechanical properties from a very limited selection of base materials [2]. Metamaterials [3] are a recent example of human-engineered lattice structures that utilise the geometric design space of unit cells to change the properties of the lattice obtained by tiling this motive, often producing structures with different properties than those of the underlying lattice material - for instance, having a soft and compressible lattice made of a very brittle material such as ceramic [4]. In addition to metamaterials that follow a periodic design philosophy, there is a growing interest in (inversely) designing disordered lattice materials and structures [5-12], allowing us to fully tap into the functional design space explored by nature. Since lattices can be constructed using additive manufacturing, they combine ease of manufacturing with a highly expressive design space that only requires a small amount of building materials. It is not surprising that lattices have found applications on a variety of scales, ranging from nano-and mesoscale materials to large-scale structures such as space habitats [13-16]. The static nature of lattices also means that once they have been constructed, their properties are fixed - unless physically stimulating the lattice changes the properties of its base materials or allows switching between different shapes (e.g., magnetically [17-19]), therefore enabling a certain degree of reprogrammability of the lattice's properties; also known as active metamaterials [20, 21].

Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called region2vec based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.