Inpatient pathways demand complex clinical decision-making based on comprehensive patient information, posing critical challenges for clinicians. Despite advancements in large language models (LLMs) in medical applications, limited research focused on artificial intelligence (AI) inpatient pathways systems, due to the lack of large-scale inpatient datasets. Moreover, existing medical benchmarks typically concentrated on medical question-answering and examinations, ignoring the multifaceted nature of clinical decision-making in inpatient settings. To address these gaps, we first developed the Inpatient Pathway Decision Support (IPDS) benchmark from the MIMIC-IV database, encompassing 51,274 cases across nine triage departments and 17 major disease categories alongside 16 standardized treatment options. Then, we proposed the Multi-Agent Inpatient Pathways (MAP) framework to accomplish inpatient pathways with three clinical agents, including a triage agent managing the patient admission, a diagnosis agent serving as the primary decision maker at the department, and a treatment agent providing treatment plans. Additionally, our MAP framework includes a chief agent overseeing the inpatient pathways to guide and promote these three clinician agents. Extensive experiments showed our MAP improved the diagnosis accuracy by 25.10% compared to the state-of-the-art LLM HuatuoGPT2-13B. It is worth noting that our MAP demonstrated significant clinical compliance, outperforming three board-certified clinicians by 10%-12%, establishing a foundation for inpatient pathways systems.

We investigate the impact of robot appearance on users' spoken behavior during real-world interactions by comparing a human-like android, ERICA, with a less anthropomorphic humanoid, TELECO. Analyzing data from 42 participants at SIGDIAL 2024, we extracted linguistic features such as disfluencies and syntactic complexity from conversation transcripts. The results showed moderate effect sizes, suggesting that participants produced fewer disfluencies and employed more complex syntax when interacting with ERICA. Further analysis involving training classification models like Na\"ive Bayes, which achieved an F1-score of 71.60\%, and conducting feature importance analysis, highlighted the significant role of disfluencies and syntactic complexity in interactions with robots of varying human-like appearances. Discussing these findings within the frameworks of cognitive load and Communication Accommodation Theory, we conclude that designing robots to elicit more structured and fluent user speech can enhance their communicative alignment with humans.

An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project.

Comprehensive forecasts of space tourism demand are crucial for businesses to optimize strategies and customer experiences in this burgeoning industry. Traditional methods struggle to capture the complex factors influencing an individual's decision to travel to space. In this paper, we propose an explainable and trustworthy artificial intelligence framework to address the challenge of predicting space tourism demand by following the National Institute of Standards and Technology guidelines. We develop a novel machine learning network, called SpaceNet, capable of learning wide-range dependencies in data and allowing us to analyze the relationships between various factors such as age, income, and risk tolerance. We investigate space travel demand in the US, categorizing it into four types: no travel, moon travel, suborbital, and orbital travel. To this end, we collected 1860 data points in many states and cities with different ages and then conducted our experiment with the data. From our experiments, the SpaceNet achieves an average ROC-AUC of 0.82 $\pm$ 0.088, indicating strong classification performance. Our investigation demonstrated that travel price, age, annual income, gender, and fatality probability are important features in deciding whether a person wants to travel or not. Beyond demand forecasting, we use explainable AI to provide interpretation for the travel-type decisions of an individual, offering insights into the factors driving interest in space travel, which is not possible with traditional classification methods. This knowledge enables businesses to tailor marketing strategies and optimize service offerings in this rapidly evolving market. To the best of our knowledge, this is the first work to implement an explainable and interpretable AI framework for investigating the factors influencing space tourism.

Despite extensive research efforts focused on OOD detection on images, OOD detection on nodes in graph learning remains underexplored. The dependence among graph nodes hinders the trivial adaptation of existing approaches on images that assume inputs to be i.i.d. sampled, since many unique features and challenges specific to graphs are not considered, such as the heterophily issue. Recently, GNNSafe, which considers node dependence, adapted energy-based detection to the graph domain with state-of-the-art performance, however, it has two serious issues: 1) it derives node energy from classification logits without specifically tailored training for modeling data distribution, making it less effective at recognizing OOD data; 2) it highly relies on energy propagation, which is based on homophily assumption and will cause significant performance degradation on heterophilic graphs, where the node tends to have dissimilar distribution with its neighbors. To address the above issues, we suggest training EBMs by MLE to enhance data distribution modeling and remove energy propagation to overcome the heterophily issues. However, training EBMs via MLE requires performing MCMC sampling on both node feature and node neighbors, which is challenging due to the node interdependence and discrete graph topology. To tackle the sampling challenge, we introduce DeGEM, which decomposes the learning process into two parts: a graph encoder that leverages topology information for node representations and an energy head that operates in latent space. Extensive experiments validate that DeGEM, without OOD exposure during training, surpasses previous state-of-the-art methods, achieving an average AUROC improvement of 6.71% on homophilic graphs and 20.29% on heterophilic graphs, and even outperform methods trained with OOD exposure. Our code is available at: https://github.com/draym28/DeGEM.

In today's business landscape, organizations need to find the right balance between using their customers' data ethically to power AI solutions and being compliant regarding data privacy and data usage regulations. In this paper, we discuss synthetic data as a possible solution to this dilemma. Synthetic data is simulated data that mimics the real data. We explore how organizations in heavily regulated industries, such as financial institutions or healthcare organizations, can leverage synthetic data to build robust AI solutions while staying compliant. We demonstrate that synthetic data offers two significant advantages by allowing AI models to learn from more diverse data and by helping organizations stay compliant against data privacy laws with the use of synthetic data instead of customer information. We discuss case studies to show how synthetic data can be effectively used in the finance and healthcare sector while discussing the challenges of using synthetic data and some ethical questions it raises. Our research finds that synthetic data could be a game-changer for AI in regulated industries. The potential can be realized when industry, academia, and regulators collaborate to build solutions. We aim to initiate discussions on the use of synthetic data to build ethical, responsible, and effective AI systems in regulated enterprise industries.

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98\% accuracy in differentiating T4 cells from B cells and 93\% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02\% of the teacher model's parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5~$\mu$s and a complete cell detection-to-sorting trigger time of 24.7~$\mu$s, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware.

Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. It is challenging to timely prevent such model-stealing attacks to achieve strong protection and maintain utility. In this paper, we propose a novel non-parametric detector called Account-aware Distribution Discrepancy (ADD) to recognize queries from malicious users by leveraging account-wise local dependency. We formulate each class as a Multivariate Normal distribution (MVN) in the feature space and measure the malicious score as the sum of weighted class-wise distribution discrepancy. The ADD detector is combined with random-based prediction poisoning to yield a plug-and-play defense module named D-ADD for image classification models. Results of extensive experimental studies show that D-ADD achieves strong defense against different types of attacks with little interference in serving benign users for both soft and hard-label settings.

Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation, as these sites enable transcription factors (TFs) to bind to DNA and modulate gene expression. Despite advances in high-throughput sequencing, accurately identifying TFBS remains challenging due to the vast genomic data and complex binding patterns. GCBLANE, a graph-enhanced convolutional bidirectional Long Short-Term Memory (LSTM) attention network, is introduced to address this issue. It integrates convolutional, multi-head attention, and recurrent layers with a graph neural network to detect key features for TFBS prediction. On 690 ENCODE ChIP-Seq datasets, GCBLANE achieved an average AUC of 0.943, and on 165 ENCODE datasets, it reached an AUC of 0.9495, outperforming advanced models that utilize multimodal approaches, including DNA shape information. This result underscores GCBLANE's effectiveness compared to other methods. By combining graph-based learning with sequence analysis, GCBLANE significantly advances TFBS prediction.

This study introduces a cutting-edge regional weather forecasting model based on the SwinTransformer 3D architecture. This model is specifically designed to deliver precise hourly weather predictions ranging from 1 hour to 5 days, significantly improving the reliability and practicality of short-term weather forecasts. Our model has demonstrated generally superior performance when compared to Pangu, a well-established global model. The evaluation indicates that our model excels in predicting most weather variables, highlighting its potential as a more effective alternative in the field of limited area modeling. A noteworthy feature of this model is the integration of enhanced boundary conditions, inspired by traditional numerical weather prediction (NWP) techniques. This integration has substantially improved the model's predictive accuracy. Additionally, the model includes an innovative approach for diagnosing hourly total precipitation at a high spatial resolution of approximately 5 kilometers. This is achieved through a latent diffusion model, offering an alternative method for generating high-resolution precipitation data.