Now that disease-modifying therapies for Alzheimer disease have been approved by regulatory agencies, the early, objective, and accurate clinical diagnosis of AD based on the lowest-cost measurement modalities possible has become an increasingly urgent need. In this study, we propose a novel feature extraction method using persistent homology to analyze structural MRI of the brain. This approach converts topological features into powerful feature vectors through Betti functions. By integrating these feature vectors with a simple machine learning model like XGBoost, we achieve a computationally efficient machine learning model. Our model outperforms state-of-the-art deep learning models in both binary and three-class classification tasks for ADNI 3D MRI disease diagnosis. Using 10-fold cross-validation, our model achieved an average accuracy of 97.43 percent and sensitivity of 99.09 percent for binary classification. For three-class classification, it achieved an average accuracy of 95.47 percent and sensitivity of 94.98 percent. Unlike many deep learning models, our approach does not require data augmentation or extensive preprocessing, making it particularly suitable for smaller datasets. Topological features differ significantly from those commonly extracted using convolutional filters and other deep learning machinery. Because it provides an entirely different type of information from machine learning models, it has the potential to combine topological features with other models later on.

Large Artificial Neural Network (ANN) models have demonstrated success in various domains, including general text and image generation, drug discovery, and protein-RNA (ribonucleic acid) binding tasks. However, these models typically demand substantial computational resources, time, and data for effective training. Given that such extensive resources are often inaccessible to many researchers and that life sciences data sets are frequently limited, we investigated whether small ANN models could achieve acceptable accuracy in protein-RNA prediction. We experimented with shallow feed-forward ANNs comprising two hidden layers and various non-linearities. These models did not utilize explicit structural information; instead, a sliding window approach was employed to implicitly consider the context of neighboring residues and bases. We explored different training techniques to address the issue of highly unbalanced data. Among the seven most popular non-linearities for feed-forward ANNs, only three: Rectified Linear Unit (ReLU), Gated Linear Unit (GLU), and Hyperbolic Tangent (Tanh) yielded converging models. Common re-balancing techniques, such as under- and over-sampling of training sets, proved ineffective, whereas increasing the volume of training data and using model ensembles significantly improved performance. The optimal context window size, balancing both false negative and false positive errors, was found to be approximately 30 residues and bases. Our findings indicate that high-accuracy protein-RNA binding prediction is achievable using computing hardware accessible to most educational and research institutions.

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.

In total, this dataset comprises 244 strong SEP events that clearly exceed the threshold of 10 pfu in the GOES P3 channel and 189 weak events observed in near-Earth space from 1986 to 2018. Additionally, the dataset includes time-series slices of GOES proton and X-ray fluxes for all the events, where each slice consists of a 12-hour observation window prior to the event onset time, and the peak flux period of events. A detailed description of dataset generation and available parameters can be found in [29]. B. Experimental Settings In supervised classification tasks, datasets with labeled samples are commonly divided into distinct subsets with knowledge of the included labels [8]. The extracted features are used to configure the parameters of the chosen algorithm in the training set, and the classifier's predictive performance on new data is determined using the testing set. Given our prediction task as a classification problem, we partition our dataset into two non-overlapping subsets: a training set (i.e., 996 samples) and a testing set (i.e., 922 samples). Similar but extending to the forecasting approach in [30], we explore the model capabilities for different short-term prediction windows of 6, 8, and 10 hours, as well as lag windows of 5, 15, 30, 45, 60, 120, and 180 minutes.

GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large, computationally intensive models, it employs a supervised approach to non-negative matrix factorization (NMF) for dimensionality reduction. This ensures a fast, efficient, and adaptable analysis while reducing computational costs. gamma_flow achieves classification accuracies above 90% and enables reliable automated spectral interpretation. Originally developed for gamma-ray spectra, it is applicable to any type of one-dimensional spectral data. As an open and flexible alternative to proprietary software, it supports various applications in research and industry.

Abstract--Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sensitive groups. While extensive studies have examined attacks targeting either objective, strategies that simultaneously degrade both utility and fairness remain largely unexplored. T o bridge this gap, we introduce the Dual-Facet Attack (DF A), a novel threat model that concurrently undermines predictive accuracy and group fairness. Two variants, Synchronous DF A (S-DF A) and Split DF A (Sp-DF A), are further proposed to capture distinct real-world collusion scenarios. Experimental results show that existing robust FL defenses, including hybrid aggregation schemes, fail to resist DF As effectively. T o counter these threats, we propose GuardFed, a self-adaptive defense framework that maintains a fairness-aware reference model using a small amount of clean server data augmented with synthetic samples. In each training round, GuardFed computes a dual-perspective trust score for every client by jointly evaluating its utility deviation and fairness degradation, thereby enabling selective aggregation of trustworthy updates. Extensive experiments on real-world datasets demonstrate that GuardFed consistently preserves both accuracy and fairness under diverse non-IID and adversarial conditions, achieving state-of-the-art performance compared with existing robust FL methods. The rapid advancement of deep learning (DL) has greatly accelerated the deployment of intelligent automation systems [1], providing smart services across diverse application domains. Alongside this evolution, there is an increasing emphasis on human-centered values such as privacy, fairness, and security, which extend beyond traditional performance-oriented objectives. Y anli Li is with the School of Artificial Intelligence and Computer Science, Nantong University, Nantong, 226019, China, and also with the School of Electrical and Computer Engineering, The University of Sydney, Sydney, 2006, Australia (e-mail: yanli.li@sydney.edu.au).

Fairness in AI-driven stress detection is critical for equitable mental healthcare, yet existing models frequently exhibit gender bias, particularly in data-scarce scenarios. To address this, we propose FairM2S, a fairness-aware meta-learning framework for stress detection leveraging audio-visual data. FairM2S integrates Equalized Odds constraints during both meta-training and adaptation phases, employing adversarial gradient masking and fairness-constrained meta-updates to effectively mitigate bias. Evaluated against five state-of-the-art baselines, FairM2S achieves 78.1% accuracy while reducing the Equal Opportunity to 0.06, demonstrating substantial fairness gains. We also release SAVSD, a smartphone-captured dataset with gender annotations, designed to support fairness research in low-resource, real-world contexts. Together, these contributions position FairM2S as a state-of-the-art approach for equitable and scalable few-shot stress detection in mental health AI. We release our dataset and FairM2S publicly with this paper.

End-to-end autonomous driving systems (ADSs), with their strong capabilities in environmental perception and generalizable driving decisions, are attracting growing attention from both academia and industry. However, once deployed on public roads, ADSs are inevitably exposed to diverse driving hazards that may compromise safety and degrade system performance. This raises a strong demand for resilience of ADSs, particularly the capability to continuously monitor driving hazards and adaptively respond to potential safety violations, which is crucial for maintaining robust driving behaviors in complex driving scenarios. To bridge this gap, we propose a runtime resilience-oriented framework, Argus, to mitigate the driving hazards, thus preventing potential safety violations and improving the driving performance of an ADS. Argus continuously monitors the trajectories generated by the ADS for potential hazards and, whenever the EGO vehicle is deemed unsafe, seamlessly takes control through a hazard mitigator. We integrate Argus with three state-of-the-art end-to-end ADSs, i.e., TCP, UniAD and VAD. Our evaluation has demonstrated that Argus effectively and efficiently enhances the resilience of ADSs, improving the driving score of the ADS by up to 150.30% on average, and preventing up to 64.38% of the violations, with little additional time overhead.

Large Language Models perform well at natural language interpretation and reasoning, but their inherent stochasticity limits their adoption in regulated industries like finance and healthcare that operate under strict policies. To address this limitation, we present a two-stage neurosymbolic framework that (1) uses LLMs with optional human guidance to formalize natural language policies, allowing fine-grained control of the formalization process, and (2) uses inference-time autofor-malization to validate logical correctness of natural language statements against those policies. When correctness is paramount, we perform multiple redundant formalization steps at inference time, cross checking the formalizations for semantic equivalence. Our benchmarks demonstrate that our approach exceeds 99% soundness, indicating a near-zero false positive rate in identifying logical validity. Our approach produces auditable logical artifacts that substantiate the verification outcomes and can be used to improve the original text. The content generation and reasoning capabilities of Large Language Models (LLMs) continue to advance rapidly, demonstrating unprecedented improvements in coherence and analytical accuracy (Wei et al., 2022; Y ao et al., 2023; Lewis et al., 2021). Despite these advances, their probabilistic nature and tendency to generate plausible but incorrect information (hallucinations, cf. Xu et al. 2024b) remain barriers to widespread adoption in regulated sectors. Industries such as healthcare, financial services, and legal practices have legal and regulatory obligations for accuracy and auditability that current LLM technology has yet to meet (Haltaufderheide & Ranisch, 2024). Companies develop institutional policies to ensure compliance with applicable laws and regulations. Such policies are typically captured in natural language (NL) documents that define rules, procedures, or guidelines. A challenge thus emerges when organizations look to deploy LLMs to answer questions about such documents: can we develop guardrails to ensure that LLM outputs conform to institutional policies? Consider an airline implementing a chatbot to assist customer service representatives in navigating refund policies: if the chatbot incorrectly claims that a customer is eligible for a refund when they are not, this could lead to legal exposure and loss of customer trust. An effective guardrail would help representatives decide if they can rely on a chatbot response without spending additional human effort to verify it. The key concern would be to ensure that when the guardrail reports an answer is valid, it actually is.

Abstract--X-band radar serves as the primary sensor on maritime vessels, however, its application in autonomous navigation has been limited due to low sensor resolution and insufficient information content. T o enable X-band radar-only autonomous navigation in maritime environments, this paper proposes a place recognition algorithm specifically tailored for X-band radar, incorporating an object density-based rule for efficient candidate selection and intentional degradation of radar detections to achieve robust retrieval performance. The proposed algorithm was evaluated on both public maritime radar datasets and our own collected dataset, and its performance was compared against state-of-the-art radar place recognition methods. An ablation study was conducted to assess the algorithm's performance sensitivity with respect to key parameters. ARL Y maritime autopilot systems were primarily designed for open-sea navigation, where the sparse and relatively unstructured environment allowed for sufficient autonomy despite intermittent sensor noise and signal fluctuations. As demonstrated by Han et al. [1] and Jang et al. [2], global positioning system (GPS) signals in maritime environments are frequently subject to degradation and interference, complicating real-time decision-making in safety-critical scenarios. Additionally, these environments are characterized by high traffic density and dynamic obstacles, which complicate situational awareness and hinder robust localization due to the frequent occlusion and unpredictability of surrounding agents. Furthermore, geographic features shift over time under the influence of tidal effects and constructions. These challenges render the estimation of vessel location based solely on fixed Electronic Navigational Chart (ENC) or satellite images unreliable and necessitate the incorporation of real-time place recognition (PR) with perception to account for dynamic environmental changes. Previous studies [3] have utilized camera and Light Detection and Ranging (LiDAR) sensors to perceive complex near-shore environments and enable autonomous sailing.