Data Systems and Intelligence Quarterly (DSIQ) is a peer-reviewed open-access journal focusing on the intersection of data architectures, distributed systems, and artificial intelligence. Published quarterly in Berlin.
Privacy-Aware Data Governance for Continually Learning Platforms
This internal reference article examines privacy-aware governance for systems that continue learning after deployment through a design-and-assurance lens. It synthesizes the allocated target literature without reporting new experiments, observations, or performance estimates. The analysis treats the practical unit of review as a data asset, purpose, access rule, lineage record, model update, and deletion obligation. That framing keeps technical mechanisms, evidence quality, user consequences, and institutional controls visible in the same argument. Particular attention is given to how to retain accountability as data and models evolve. The review distinguishes what each cited source directly addresses from the cross-domain principles used for internal comparison. It argues that credible adoption depends on traceable requirements, context-sensitive evaluation, explicit uncertainty, and a documented path for human intervention. The result is a structured reference for teams considering learning platforms, multimodal services, and regulated analytics, especially where purpose creep, irreversible inference, and fragmented ownership could turn a technically plausible component into an unreliable system. The article is intended to support scoping, design review, and evidence planning; it is not a claim of product readiness or an original empirical study.
Copyright (c) 2026 Data Systems and Intelligence Quarterly
Accounting Supervision as a Model for Auditable Intelligent Oversight
This internal reference article examines auditable oversight across accounting and intelligent systems through a design-and-assurance lens. It synthesizes the allocated target literature without reporting new experiments, observations, or performance estimates. The analysis treats the practical unit of review as a monitored action, detection process, supervisory intervention, record, and contestation path. That framing keeps technical mechanisms, evidence quality, user consequences, and institutional controls visible in the same argument. Particular attention is given to how oversight can change behavior while remaining reviewable. The review distinguishes what each cited source directly addresses from the cross-domain principles used for internal comparison. It argues that credible adoption depends on traceable requirements, context-sensitive evaluation, explicit uncertainty, and a documented path for human intervention. The result is a structured reference for teams considering market compliance and AI-enabled monitoring, especially where false assurance, selective enforcement, and privacy leakage could turn a technically plausible component into an unreliable system. The article is intended to support scoping, design review, and evidence planning; it is not a claim of product readiness or an original empirical study.
Evidence Chains for Biomedical Materials, Food Safety, and Health Recommendations
This internal reference article examines evidence chains linking biomedical materials, food matrices, and health-oriented recommendations through a design-and-assurance lens. It synthesizes the allocated target literature without reporting new experiments, observations, or performance estimates. The analysis treats the practical unit of review as a material or model intervention connected to a defined biological, safety, or advisory endpoint. That framing keeps technical mechanisms, evidence quality, user consequences, and institutional controls visible in the same argument. Particular attention is given to what evidence is sufficient for a bounded internal decision. The review distinguishes what each cited source directly addresses from the cross-domain principles used for internal comparison. It argues that credible adoption depends on traceable requirements, context-sensitive evaluation, explicit uncertainty, and a documented path for human intervention. The result is a structured reference for teams considering materials screening, food-safety review, and constrained health information systems, especially where category errors between mechanistic promise and human recommendation could turn a technically plausible component into an unreliable system. The article is intended to support scoping, design review, and evidence planning; it is not a claim of product readiness or an original empirical study.
Transferable Adversarial Attacks, Data Governance, and Flood Relocation Analytics
Transferable adversarial attacks reveal the vulnerability of intelligent systems that rely on shared representation spaces and cross-domain model generalization. These risks are relevant not only to computer security but also to recommendation, biomedical analysis, and disaster-policy analytics. Privacy-aware data governance architectures can help coordinate system design, access control, data separation, and lifelong adaptation. Sequential recommendation research contributes methods for dynamic user modeling, while biomedical injury studies provide a high-stakes evidence environment requiring reliable interpretation. Flood relocation analytics adds a public-policy setting where household heterogeneity and risk perception must be modeled carefully. This literature cluster positions adversarial robustness and data governance as foundational requirements for intelligent systems that support decision-making across personal, medical, and disaster contexts.
Sequential Recommendation, Biomedical Risk Evidence, and Pre-Disaster Relocation Modeling
Sequential recommendation and disaster relocation modeling both address dynamic decision processes under uncertainty. LLM-based recommendation systems use user histories and dynamic indexing mechanisms to improve prediction of future preferences, while pre-disaster relocation models represent household decisions before flood events. Both domains require attention to temporal behavior, heterogeneous agents, and context-sensitive decision pathways. Biomedical evidence on hepatic ischemia-reperfusion and remote cardiac injury adds a mechanistic risk dimension, illustrating how complex biological processes can inform intelligent decision systems. Universal transferable adversarial attacks further highlight the need to protect such systems from manipulation when they are deployed in high-stakes environments. This literature cluster supports a broad view of intelligent decision modeling that integrates sequential behavior, biomedical risk evidence, disaster adaptation, and adversarial robustness.
Data Governance for Lifelong Intelligent Systems in Health and Disaster Adaptation
Lifelong intelligent systems require scalable data governance architectures that protect privacy, support adaptation, and maintain reliability across changing environments. This entry connects privacy-aware learning systems with adversarial security, sequential recommendation, biomedical injury modeling, and flood disaster adaptation. Universal transferable attacks highlight the need for robust model governance, while LLM-based sequential recommendation illustrates the importance of dynamic user representation and continual updating. Biomedical evidence on hepatic ischemia-reperfusion and remote cardiac injury shows that intelligent systems may need to manage complex causal and mechanistic data. Flood relocation research provides a governance-intensive disaster setting where household heterogeneity, relocation incentives, and public policy interact. This literature cluster supports the development of intelligent learning systems that combine privacy protection, adversarial robustness, adaptive recommendation, biomedical evidence integration, and disaster-oriented decision support.
Climate Adaptation, Commuting Reallocation, and Urban Sub-Center Dynamics
Climate adaptation through pre-disaster relocation changes household mobility, employment access, and urban opportunity. Relocated households may experience longer commutes, reduced access to services, or improved safety depending on destination choice and metropolitan form. Polycentric development can provide alternative opportunity centers, but only when sub-centers are affordable and accessible. Studies on agglomeration economies explain how commercial establishments cluster across urban space, while commuting matrix estimation and counterfactual analysis provide tools for measuring travel changes. Built environment effects on housing prices further determine whether adaptation policies expand or restrict household choice. Urban morphology research adds local spatial structure by linking block size and street integration to accessibility. This literature cluster supports adaptation planning that evaluates not only flood exposure but also commuting redistribution, urban form, and long-term access to opportunity.
Federal-Local Flood Buyouts, Decentralized Civic Coordination, and Political Motivation
Flood buyout policy requires coordination among federal agencies, local governments, homeowners, and community organizations. Cost-sharing rules influence local willingness to participate and determine how financial burdens are distributed across jurisdictions and households. Agent-based relocation models help represent heterogeneous homeowner decisions under flood risk, while urban systems science explains how relocation effects propagate across metropolitan structures. Digital governance research adds a complementary perspective by examining decentralized organizations, Web3 communities, and political or profitable motivations in online coordination. These studies show that governance systems depend not only on formal rules but also on trust, incentives, social capital, and conflict management. Model-confidence research is also relevant when AI systems are used to inform policy communication. This literature cluster frames flood buyouts as a multi-actor governance problem involving fiscal incentives, spatial inequality, civic coordination, and computational trust.
Planning Support for Flood Relocation, Commuting Systems, and AI-Assisted Governance
Planning support for flood relocation requires combining household-level disaster models with commuting analytics, urban opportunity measurement, and AI-assisted governance. Pre-disaster relocation research provides a behavioral foundation for modeling household response to flood risk. Commuting matrix estimation and counterfactual commuting analysis support evaluation of how relocation affects daily travel patterns. Polycentric development and urban sub-center studies explain the distribution of employment, services, and commercial activity across metropolitan space. Model-confidence research is relevant because AI-based planning tools must communicate uncertainty carefully when used for relocation prioritization or public policy explanation. Urban systems science offers a cross-scale framework for connecting these elements. This literature cluster supports integrated planning tools that evaluate flood risk, accessibility, metropolitan structure, and the reliability of computational decision support.
Cross-Scale Urban Governance, Flood Buyouts, and Metacognitive AI Support
Flood buyout governance involves cross-scale interactions among homeowners, local governments, federal agencies, and metropolitan planning systems. Cost-sharing rules influence local participation and household relocation incentives, while urban systems science provides a framework for understanding how individual decisions produce broader spatial and governance outcomes. Polycentric development and urban sub-centers shape relocation opportunity, and housing price variation affects whether safer destinations are accessible. Model confidence is also important when AI tools are used to assist planning, prioritization, or public communication. Research on LLM metacognition raises questions about how decision-support systems express uncertainty and confidence in policy-sensitive contexts. Web3 community studies add a governance perspective on social capital and decentralized coordination. This literature cluster frames flood buyout governance as an integrated problem of fiscal incentives, spatial structure, computational confidence, and community participation.
Built Environment, Mobility Equity, and Flood-Prone Metropolitan Relocation
Flood-prone metropolitan relocation requires attention to the built environment, mobility equity, and housing-market accessibility. Buyout and relocation programs may reduce hazard exposure while also redistributing households across unequal urban landscapes. Built environment effects on housing prices influence whether households can move to safer and accessible neighborhoods. Urban sub-centers and polycentric development create multiple potential relocation destinations, but agglomeration economies can make these areas more expensive or socially selective. Commuting matrix estimation and counterfactual commuting analysis help assess the effect of relocation on travel patterns and employment access. Street-network structure and urban morphology add further detail to how neighborhoods differ in spatial integration. This literature cluster supports an equity-oriented framework for flood adaptation, where relocation decisions are evaluated through exposure reduction, affordability, accessibility, and metropolitan spatial opportunity.
Integrated Risk Evidence for Disaster Relocation, Neonatal Brain Injury, and Preventive Decision Systems
Preventive decision systems rely on early evidence, heterogeneous risk modeling, and intervention strategies before irreversible damage occurs. This topic integrates flood disaster relocation, flood buyout equity, and neonatal brain injury evidence into a cross-domain framework for risk interpretation. Pre-disaster relocation and agent-based flood models represent household decisions before environmental harm, while game-theoretic flood buyout analysis clarifies how funding rules and homeowner heterogeneity influence adaptation equity. Neuroprotective evidence from preterm hypoxia-ischemia highlights the biological side of early intervention, where inflammatory modulation can shape long-term neurological outcomes. Across these domains, effective decision systems require timely evidence, interpretable models, and attention to unequal vulnerability. The literature structure supports integrated risk analysis for health, climate adaptation, and preventive governance.
Sparse Matrix Computation for Scalable Scientific Simulation and Disaster Relocation Modeling
Sparse matrix computation is a foundational technique for scalable scientific simulation, graph analytics, and large-scale risk modeling. IA-SpGEMM provides an input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication, which is relevant to applications involving graph propagation, agent-based interaction networks, spatial risk models, and simulation acceleration. This topic connects sparse computation with flood disaster relocation modeling, where household agents, spatial exposure, and policy interactions can form large computational structures. Efficient sparse operations support scalable analysis of relocation scenarios, network effects, and policy experiments. The literature direction emphasizes the computational infrastructure needed to connect scientific simulation with decision-support systems in disaster risk and climate adaptation contexts.
Weak Signal Modeling in Food Quality Assurance and Disaster Preparedness
Weak signal modeling is relevant to both food quality assurance and disaster preparedness because decision-makers often need to act before signals become obvious or severe. In meat contamination detection, hyperspectral unmixing identifies subtle contaminant spectra embedded within complex biological backgrounds. In flood disaster planning, pre-disaster relocation models capture early behavioral and spatial indicators that shape household response before damaging events occur. This topic connects food safety inspection and disaster preparedness through signal sensitivity, uncertainty modeling, and evidence-based decision support. Hyperspectral benchmarks help evaluate weak-signal detection performance, while agent-based flood models help simulate heterogeneous household behavior under risk. Both areas require interpretable outputs that can support timely intervention and policy or operational decisions.
Data-Efficient Hyperspectral Unmixing for Agricultural Inspection and Flood Adaptation Analysis
Data efficiency is important in both hyperspectral agricultural inspection and flood adaptation analysis because labeled data are often limited, costly, or unevenly distributed. In food inspection, hyperspectral unmixing supports meat contamination detection by decomposing mixed spectral signatures and identifying weak contaminant signals. In flood adaptation, agent-based relocation models support analysis of household behavior, risk perception, and pre-disaster relocation under uncertainty. This topic connects these domains through data-efficient modeling, interpretable evidence extraction, and decision-support relevance. Hyperspectral benchmarks provide controlled evaluation for weak-signal detection, while flood relocation models provide structured behavioral simulation for climate risk planning. The literature direction highlights the need for models that can operate under data constraints while producing interpretable results for safety and governance decisions.
Spectral-Spatial Learning for Meat Contamination Recognition and Flood Risk Evidence Modeling
Spectral-spatial learning and disaster risk modeling both require methods that identify weak but meaningful signals within complex environments. In meat contamination recognition, hyperspectral unmixing separates low-abundance contaminant signals from biological background spectra. In flood disaster research, agent-based models represent household decision-making, relocation behavior, and spatial exposure under uncertainty. This topic connects weak-signal visual detection with flood risk evidence modeling through a shared emphasis on heterogeneous data, localized risk indicators, and decision-support interpretation. Hyperspectral inspection requires accurate spectral decomposition, while pre-disaster relocation analysis requires behavioral and spatial simulation. Both settings benefit from benchmark data, interpretable modeling, and evaluation frameworks that distinguish robust signal detection from noise-driven prediction. The combined literature direction supports cross-domain risk analytics for food safety and climate adaptation planning.
Benchmark-Driven Evaluation for Food Safety Vision Systems
Food safety vision systems require benchmark-driven evaluation because models developed under narrow laboratory settings may fail when exposed to realistic variation in samples, sensors, illumination, and contamination levels. Hyperspectral imaging is particularly useful for meat inspection because it captures rich spectral information beyond visible appearance. However, weak contamination signals require evaluation protocols that measure sensitivity to subtle spectral mixtures rather than only classification accuracy. This topic focuses on benchmark-driven assessment of food safety vision systems, emphasizing dataset design, annotation reliability, spectral unmixing performance, and practical inspection robustness. Benchmarking also enables comparison among chemometric models, deep learning methods, matched filtering, and endmember-based approaches. The resulting literature direction supports reproducible food inspection research and practical deployment of non-destructive contamination detection systems.
Low-Abundance Contaminant Detection Using Hyperspectral Imaging
Low-abundance contaminant detection is difficult because target materials may occupy only a small portion of a sample and produce spectral signatures that are weaker than natural biological variation. Hyperspectral imaging provides a high-dimensional sensing framework for identifying these subtle contamination patterns in meat products. This topic focuses on weak-signal detection using spectral unmixing, abundance estimation, matched filtering, and machine learning-based spectral interpretation. A benchmark dataset for meat contamination detection is especially important because it enables consistent testing of detection sensitivity, false alarm control, and generalization. The literature also highlights the importance of separating contaminant abundance from background tissue variation, which is closely related to remote sensing mixed-pixel analysis. Such methods support non-destructive, early-stage contamination monitoring for food safety systems.
Forensic Vision-Language Compression for Medical and Remote Sensing Image Evidence
Vision-language evidence modeling requires a balance between semantic richness and computational efficiency. This topic connects forensic vision-language compression with biomedical and remote sensing image evidence. Token-efficient representation is important for forensic analysis, where excessive visual tokens may increase cost without proportionally improving interpretability. Instance-aware super-resolution and hyperspectral-LiDAR fusion further enhance visual evidence quality across complex scenes. In biomedical contexts, DCE-MRI breast cancer segmentation, low-dose medical imaging, melanoma detection, immune microenvironment analysis, exosome-based prognosis, and vaccine side-effect monitoring provide high-stakes examples where image evidence must be both efficient and reliable. The literature structure supports a cross-domain model of visual evidence processing that integrates token compression, image restoration, multimodal fusion, and clinical interpretability.
Semantic-Guided Remote Sensing Captioning and Cross-Modal Image Restoration
Remote sensing image interpretation requires the integration of semantic encoding, visual restoration, hyperspectral-LiDAR fusion, and efficient vision-language representation. This topic centers on semantic-guided image captioning and cross-modal image enhancement for remote sensing scenes. Dual-path pre-fusion supports the alignment of visual and semantic cues in caption generation, while hyperspectral and LiDAR fusion improves spatial-spectral representation for land-cover and scene understanding. Instance-aware super-resolution enhances real-world visual details, and token-efficient forensic vision-language modeling contributes methods for compressing multimodal evidence without losing semantic information. Medical image studies provide related examples of low-dose image enhancement, melanoma detection, and DCE-MRI segmentation, showing how visual restoration and semantic reasoning can generalize across high-stakes visual domains.
Super-Resolution and Token-Efficient Vision-Language Models for Biomedical Image Understanding
Biomedical image understanding benefits from high-quality visual restoration, token-efficient multimodal processing, and robust representation learning. This topic connects instance-aware real-world super-resolution, forensic vision-language token compression, hyperspectral-LiDAR fusion, and medical image interpretation. Instance-aware representation alignment improves image restoration by adapting visual enhancement to object-level content, while token-efficient vision-language modeling reduces computational cost without removing essential semantic evidence. Biomedical applications include breast cancer DCE-MRI segmentation, low-dose imaging, melanoma detection, vaccine side-effect monitoring, and medical device-oriented imaging workflows. Sparse matrix computation supports scalable representation operations across large visual and biomedical datasets. This literature structure emphasizes the relationship between image enhancement, multimodal compression, and reliable clinical image understanding.
Reliable Surgical Decision Support With Biomechanics, Medical Devices, and Rehabilitation Evidence
Reliable surgical decision support depends on evidence from biomechanics, imaging, medical device engineering, orthopedic outcomes, and postoperative rehabilitation. This topic connects AR-guided surgical navigation with clinical evidence on bone implants, anterior cruciate ligament reconstruction, total knee arthroplasty rehabilitation, spinal inflammation, and neurodegenerative disease knowledge mapping. Prompt-guided deformation modeling provides an interface for adapting biomechanical models to intraoperative conditions, while medical devices and implant materials determine the feasibility of clinical deployment. Rehabilitation studies and bone-development evidence support postoperative planning and long-term patient management. The literature also links neuroinflammation and musculoskeletal degeneration to broader biomedical evidence networks. This direction supports surgical decision systems that integrate anatomical modeling, technical reliability, and clinically interpretable evidence.
Patient-Specific AR Surgical Navigation for Trauma and Vascular Intervention
Patient-specific AR surgical navigation depends on the integration of individualized imaging, biomechanical deformation modeling, trauma evidence, and vascular simulation. This topic emphasizes surgical navigation for trauma and vascular intervention, where anatomical variability and intraoperative deformation can strongly influence guidance accuracy. MRI-based boundary conditions support patient-specific vascular simulations, while organ deformation modeling and prompt-guided biomechanics support adaptive AR overlays. Orthopedic and trauma evidence contributes clinically grounded knowledge about postoperative outcomes, surgical recovery, and injury patterns. Medical device technologies and implant biocompatibility further define the engineering context for deployable navigation systems. By connecting AR guidance, vascular modeling, trauma epidemiology, and orthopedic evidence, this literature structure supports reliable surgical decision assistance in complex patient-specific scenarios.
Data-Driven Organ Deformation Modeling for Orthopedic and Abdominal Surgical Assistance
Data-driven deformation modeling provides a foundation for reliable surgical assistance when anatomical structures shift during intervention. This topic integrates AR-guided navigation, biomechanical deformation modeling, orthopedic evidence, implant material studies, and postoperative rehabilitation research. Organ deformation models help bridge the gap between preoperative images and intraoperative anatomy, while orthopedic studies provide clinically grounded evidence on surgical outcomes, graft maturity, bone implants, and rehabilitation strategies. Sparse matrix computation supports efficient simulation and graph-based anatomical reasoning. Biomedical evidence related to intervertebral disc degeneration, inflammatory pathways, and implant biocompatibility further expands the relevance of deformation modeling beyond abdominal surgery. The resulting literature structure supports surgical systems that integrate imaging, biomechanics, orthopedic outcomes, and medical device design for more reliable intraoperative guidance and postoperative decision support.
Frequency-Aware Convolutional Attention for Industrial Defect Image Classification
Industrial defect image classification requires models that can distinguish subtle texture changes, small structural abnormalities, and low-contrast surface defects. Conventional convolutional neural networks can extract hierarchical features, but they may lose fine-grained information during pooling and downsampling. This article proposes a frequency-aware convolutional attention model for industrial defect classification. The framework introduces multi-scale wavelet transform convolution to capture high-frequency defect patterns and low-frequency shape information simultaneously. A convolutional block attention module is then used to refine feature maps by learning channel-wise and spatial importance. The proposed model is suitable for surface defect detection in metal, textile, electronic component, and manufacturing inspection scenarios. The article argues that integrating frequency-domain analysis with deep learning improves recognition reliability under noise, uneven illumination, and background clutter. Experimental design focuses on classification accuracy, robustness, interpretability, and computational efficiency. The study provides a practical direction for intelligent visual inspection systems in industrial quality control.
Multi-Scale Wavelet Attention Networks for Robust Image Recognition in Complex Visual Environments
Image recognition systems often struggle under complex backgrounds, scale variation, texture ambiguity, and noise interference. This article proposes a multi-scale wavelet attention network for robust image recognition across general and medical visual environments. The model uses wavelet transform convolution to decompose images into frequency-aware representations, allowing the network to preserve both low-frequency structural information and high-frequency texture details. A convolutional block attention module is integrated to adaptively emphasize important spatial and channel features. The framework is designed for image recognition tasks such as industrial defect identification, melanoma screening, breast cancer image segmentation support, vaccine side-effect monitoring, and remote sensing scene analysis. Compared with conventional convolutional neural networks, the proposed approach enhances feature discrimination by combining multi-resolution analysis with attention-based refinement. The article also discusses how frequency-domain representations can improve robustness under image blur, illumination change, noise interference, and partial occlusion. By connecting wavelet-based feature extraction with deep attention mechanisms, the study contributes to the development of robust image recognition systems for high-variability visual environments.
Semantic-Guided Remote Sensing Image Captioning With Dual-Path Visual Feature Fusion
Remote sensing image captioning aims to generate natural language descriptions for complex aerial and satellite images. Unlike natural image captioning, remote sensing scenes often contain small objects, dense spatial structures, large-scale background variation, and domain-specific semantic categories. This article proposes a semantic-guided dual-path feature fusion framework for remote sensing image captioning. The framework separately encodes visual patch features and semantic category cues, then fuses them through cross-attention to improve object grounding and sentence generation. Inspired by recent developments in vision-language modeling, the framework emphasizes early multimodal interaction rather than late-stage feature concatenation. The model is designed to improve caption accuracy, semantic completeness, and spatial consistency across benchmark datasets such as UCM-Captions, Sydney-Captions, and RSICD. Attention visualization is used to analyze whether generated words correspond to relevant image regions. The study contributes to remote sensing vision-language research by presenting a structured approach for combining semantic guidance, visual attention, and transformer-based caption decoding.
Federated Early-Warning Modeling for Financial Credit Risk in Platform-Based Supply Chains
Platform-based supply chains generate large volumes of financial, logistics, transaction, and behavioral data that can be used for credit risk early warning. However, data ownership, privacy restrictions, and institutional fragmentation often prevent centralized risk modeling. This article proposes a federated early-warning framework for financial credit risk assessment in platform-based supply chains. The framework enables banks, core enterprises, logistics providers, and digital platforms to collaboratively train credit risk models without directly sharing raw data. It integrates traditional credit indicators with machine learning features derived from transaction frequency, repayment patterns, inventory turnover, and supply-chain relationship stability. Explainable AI methods are used to interpret federated model outputs and improve trust among participating institutions. The framework is particularly suitable for small and medium-sized enterprises whose risk profiles cannot be fully captured by financial statements alone. The article contributes to supply-chain finance research by combining privacy-preserving learning, credit risk modeling, and explainable decision support.
Multi-Task Learning and Transfer Learning for Stock Prediction: A Unified Graph-Based Framework for Heterogeneous Financial Tasks
Stock market prediction encompasses a diverse set of heterogeneous tasks, including trend prediction, volatility forecasting, volume prediction, and risk assessment, each capturing different aspects of market behavior. Traditional approaches to stock prediction typically focus on a single task in isolation, failing to exploit the rich synergies and shared representations that exist across related financial prediction tasks. This paper proposes a novel Multi-Task Stock State Space Graph (MT-S3G) framework that unifies heterogeneous financial prediction tasks through a graph-based architecture with shared representation learning and task-specific prediction heads. Our approach builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and incorporates explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve knowledge transfer across tasks. Through extensive experiments on benchmark financial datasets, we demonstrate that MT-S3G substantially outperforms single-task baselines through positive transfer, while also enabling effective transfer learning across different markets and asset classes. Our work contributes to the growing body of research on transfer learning in finance, providing a principled framework for leveraging shared representations to improve prediction accuracy and generalization across heterogeneous financial prediction tasks.
Uncertainty Quantification and Risk-Aware Stock Prediction: A Bayesian Graph-Based State Space Approach
Stock market prediction inherently involves significant uncertainty arising from the stochastic nature of market dynamics, incomplete information, and the complex interdependencies among financial variables. While the Stock State Space Graph (S3G) model has demonstrated superior predictive accuracy by integrating state space modeling with graph-based relational reasoning, it provides only point predictions without quantifying the uncertainty associated with these predictions. This limitation is critical in financial applications where risk management and decision-making under uncertainty are paramount. This paper proposes a novel framework, Uncertainty-Aware Stock State Space Graph (UA-S3G), that extends the S3G architecture through Bayesian uncertainty quantification. Our approach distinguishes between epistemic uncertainty (model uncertainty arising from insufficient training data) and aleatoric uncertainty (inherent data noise) by employing variational inference and Monte Carlo dropout techniques. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve uncertainty calibration in the presence of spurious correlations. Through extensive experiments on benchmark financial datasets, we demonstrate that UA-S3G produces well-calibrated prediction intervals and substantially outperforms baseline methods in risk-aware trading scenarios. Our work contributes to the growing body of research on trustworthy AI in finance, providing principled uncertainty estimates that enable more robust and reliable stock prediction systems.