OpenRaiser/Intern-Atlas huggingface dataset:
Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learning research in climate modeling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modeling: from surrogate modeling, ML parameterization, probabilistic programming, to simulation-based inference, and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data, and designed to integrate observations. We aim to offer a framework for ensuring rigor in scientific machine learning through more transparent model development, critical evaluation, informed adaptation, and reproducibility, and to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modeling.
The QIC-Index: A Novel, Data-Centric Metric for Quantifying the Impact of Research Data Sharing
Venue: arXiv cs.DL 2025
Year: 2025

We introduce the QIC-Index, a novel metric to address the failure of publication-centric metrics to value research data sharing. The QIC-Index quantifies the impact of individual data objects by calculating a score based on their Quality (Q), Impact (I), and Collaboration (C). By rewarding the sharing of high-quality, impactful, and collaborative data, our framework aligns individual incentives with the goals of open science and aims to foster a more transparent and efficient research culture.
Creative synthesis of kinematic mechanisms
Venue: arXiv cs.GR 2025
Year: 2025

In this paper, we formulate the problem of kinematic synthesis for planar linkages as a cross-domain image generation task. We develop a planar linkages dataset using RGB image representations, covering a range of mechanisms: from simple types such as crank-rocker and crank-slider to more complex eight-bar linkages like Jansen's mechanism. A shared-latent variational autoencoder (VAE) is employed to explore the potential of image generative models for synthesizing unseen motion curves and simulating novel kinematics. By encoding the drawing speed of trajectory points as color gradients, the same architecture also supports kinematic synthesis conditioned on both trajectory shape and velocity profiles. We validate our method on three datasets of increasing complexity: a standard four-bar linkage set, a mixed set of four-bar and crank-slider mechanisms, and a complex set including multi-loop mechanisms. Preliminary results demonstrate the effectiveness of image-based representations for generative mechanical design, showing that mechanisms with revolute and prismatic joints, and potentially cams and gears, can be represented and synthesized within a unified image generation framework.
The View From Space: Navigating Instrumentation Differences with EOFMs
Venue: arXiv cs.CV 2025
Year: 2025

Earth Observation Foundation Models (EOFMs) have exploded in prevalence as tools for processing the massive volumes of remotely sensed and other earth observation data, and for delivering impact on the many essential earth monitoring tasks. An emerging trend posits using the outputs of pre-trained models as 'embeddings' which summarize high dimensional data to be used for generic tasks such as similarity search and content-specific queries. However, most EOFM models are trained only on single modalities of data and then applied or benchmarked by matching bands across different modalities. It is not clear from existing work what impact diverse sensor architectures have on the internal representations of the present suite of EOFMs. We show in this work that the representation space of EOFMs is highly sensitive to sensor architecture and that understanding this difference gives a vital perspective on the pitfalls of current EOFM design and signals for how to move forward as model developers, users, and a community guided by robust remote-sensing science.
SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning (FL) enables collaborative model training without sharing raw data but is vulnerable to gradient inversion attacks (GIAs), where adversaries reconstruct private data from shared gradients. Existing defenses either incur impractical computational overhead for embedded platforms or fail to achieve privacy protection and good model utility at the same time. Moreover, many defenses can be easily bypassed by adaptive adversaries who have obtained the defense details. To address these limitations, we propose SVDefense, a novel defense framework against GIAs that leverages the truncated Singular Value Decomposition (SVD) to obfuscate gradient updates. SVDefense introduces three key innovations, a Self-Adaptive Energy Threshold that adapts to client vulnerability, a Channel-Wise Weighted Approximation that selectively preserves essential gradient information for effective model training while enhancing privacy protection, and a Layer-Wise Weighted Aggregation for effective model aggregation under class imbalance. Our extensive evaluation shows that SVDefense outperforms existing defenses across multiple applications, including image classification, human activity recognition, and keyword spotting, by offering robust privacy protection with minimal impact on model accuracy. Furthermore, SVDefense is practical for deployment on various resource-constrained embedded platforms. We will make our code publicly available upon paper acceptance.
Embedding Sustainability in Software Engineering Curriculum: A Case Study
Venue: arXiv cs.CY 2025
Year: 2025

Sustainability is increasingly recognized as a critical dimension of engineering education, yet its integration into Software Engineering curricula remains a challenge. This paper reports on a case study that examines how sustainability is being embedded across modules in the Software Engineering program at one university. The paper outlines the process through which academics and students co-identified opportunities for integration, guided by the five dimensions of the Sustainability Awareness Framework, targeted discussion questions, and good practice examples drawn from the Green Software Foundation patterns. The study highlights practical steps - including the use of frameworks, illustrative examples, student engagement, and iterative consultative processes - that can support other institutions seeking to embed sustainability into their programs. We also discuss strategies for integrating sustainability into the Software Engineering curriculum and argue that such integration is a necessary and urgent step to prepare Software Engineering graduates as sustainability-aware professionals in our changing society.
When Patients Go to "Dr. Google" Before They Go to the Emergency Department
Venue: arXiv cs.CY 2025
Year: 2025

Approximately one-third of adults search the internet for health information before visiting an emergency department (ED), with 75% encountering inaccurate content. This study examines how such searches influence patient care. We conducted an observational study of ED visits over a 12-month period, surveying 214 of 576 patients about pre-ED internet use. Data on demographics, comorbidities, acuity, orders, prescriptions, and dispositions were extracted. Patients who searched were typically younger, healthier, and more educated. Most used a general search engine to ask symptom-related questions. Compared to non-searchers, they were less likely to receive lab tests (RR 0.78, p=0.053), imaging (RR 0.75, p=0.094), medications (RR 0.67, p=0.038), or admission (RR 0.68, p=0.175). They were more likely to leave against medical advice (RR 1.67, p=0.067) and receive opioids (RR 1.56, p=0.151). Findings suggest inaccurate health information may contribute to mismatched expectations and altered care delivery.
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
Venue: arXiv cs.LG 2025
Year: 2025

With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select the right pilots in a cost-efficient manner has become an important research question. In the current study, twenty-three pilots were recruited from China Eastern Airlines, and 23 novices were from the community of Tsinghua University. A novel approach incorporating machine learning and virtual reality technology was applied to distinguish features between these participants with different flight skills. Results indicate that SVM with the MIC feature selection method consistently achieved the highest prediction performance on all metrics with an Accuracy of 0.93, an AUC of 0.96, and an F1 of 0.93, which outperforms four other classifier algorithms and two other feature selection methods. From the perspective of feature selection methods, the MIC method can select features with a nonlinear relationship to sampling labels, instead of a simple filter-out. Our new implementation of the SVM + MIC algorithm outperforms all existing pilot selection algorithms and perhaps provides the first implementation based on eye tracking and flight dynamics data. This study's VR simulation platforms and algorithms can be used for pilot selection and training.
Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
Venue: arXiv cs.LG 2025
Year: 2025

Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly to models trained to other modalities. In this paper, we analyze Transformers through the lens of rank structure. Our focus is on the time series setting, where the structural properties of the data differ remarkably from those of text or vision. We show that time-series embeddings, unlike text or vision, exhibit sharply decaying singular value spectra: small patch sizes and smooth continuous mappings concentrate the data into low-rank subspaces. From this, we prove that the associated $Q/K/V$ projections admit accurate low-rank approximations, and that attention layers become compressible in proportion to the decay of the embedding spectrum. We introduce the concept of flow-of-ranks, a phenomenon by which nonlinear mixing across depth inflates the rank, explaining why early layers are most amenable to compression and why ranks grow with depth. Guided by these theoretical and empirical results, we use these insights to compress Chronos, a large time series foundation model, achieving a reduction of $65\%$ in inference time and $81\%$ in memory, without loss of accuracy. Our findings provide principled guidance for allocating width, depth, and heads in time series foundation models, and for exploiting their inherent compressibility.
Unified Unsupervised Anomaly Detection via Matching Cost Filtering
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and medical analysis, where anomalies are scarce due to privacy concerns and cold-start constraints. Existing methods, whether reconstruction-based (restoring normal counterparts) or embedding-based (pretrained representations), fundamentally conduct image- or feature-level matching to generate anomaly maps. Nonetheless, matching noise has been largely overlooked, limiting their detection ability. Beyond earlier focus on unimodal RGB-based UAD, recent advances expand to multimodal scenarios, e.g., RGB-3D and RGB-Text, enabled by point cloud sensing and vision-language models. Despite shared challenges, these lines remain largely isolated, hindering a comprehensive understanding and knowledge transfer. In this paper, we advocate unified UAD for both unimodal and multimodal settings in the matching perspective. Under this insight, we present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model. The cost volume is constructed by matching a test sample against normal samples from the same or different modalities, followed by a learnable filtering module with multi-layer attention guidance from the test sample, mitigating matching noise and highlighting subtle anomalies. Comprehensive experiments on 22 diverse benchmarks demonstrate the efficacy of UCF in enhancing a variety of UAD methods, consistently achieving new state-of-the-art results in both unimodal (RGB) and multimodal (RGB-3D, RGB-Text) UAD scenarios. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.
TriQuest:An AI Copilot-Powered Platform for Interdisciplinary Curriculum Design
Venue: arXiv cs.CY 2025
Year: 2025

Interdisciplinary teaching is a cornerstone of modern curriculum reform, but its implementation is hindered by challenges in knowledge integration and time-consuming lesson planning. Existing tools often lack the required pedagogical and domain-specific depth.We introduce TriQuest, an AI-copilot platform designed to solve these problems. TriQuest uses large language models and knowledge graphs via an intuitive GUI to help teachers efficiently generate high-quality interdisciplinary lesson plans. Its core features include intelligent knowledge integration from various disciplines and a human-computer collaborative review process to ensure quality and innovation.In a study with 43 teachers, TriQuest increased curriculum design efficiency and improved lesson plan quality. It also significantly lowered design barriers and cognitive load. Our work presents a new paradigm for empowering teacher professional development with intelligent technologies.
Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints
Venue: arXiv cs.AI 2025
Year: 2025

The scarcity of non-renewable energy sources, geopolitical problems in its supply, increasing prices, and the impact of climate change, force the global economy to develop more energy-efficient solutions for their operations. The Manufacturing sector is not excluded from this challenge as one of the largest consumers of energy. Energy-efficient scheduling is a method that attracts manufacturing companies to reduce their consumption as it can be quickly deployed and can show impact immediately. In this study, the hybrid flow shop scheduling problem with blocking constraint (BHFS) is investigated in which we seek to minimize the latest completion time (i.e. makespan) and overall energy consumption, a typical manufacturing setting across many industries from automotive to pharmaceutical. Energy consumption and the latest completion time of customer orders are usually conflicting objectives. Therefore, we first formulate the problem as a novel multi-objective mixed integer programming (MIP) model and propose an augmented epsilon-constraint method for finding the Pareto-optimal solutions. Also, an effective multi-objective metaheuristic algorithm. Refined Iterated Pareto Greedy (RIPG), is developed to solve large instances in reasonable time. Our proposed methods are benchmarked using small, medium, and large-size instances to evaluate their efficiency. Two well-known algorithms are adopted for comparing our novel approaches. The computational results show the effectiveness of our method.
Can an AI-Powered Presentation Platform Based On The Game "Just a Minute" Be Used To Improve Students' Public Speaking Skills?
Venue: arXiv cs.CY 2025
Year: 2025

This study explores the effectiveness of applying AI and gamification into a presentation platform aimed at University students wanting to improve their public speaking skills in their native tongue. Specifically, a platform based on the radio show, Just a Minute (JAM), is explored. In this game, players are challenged to speak fluently on a topic for 60 seconds without repeating themselves, hesitating or deviating from the topic. JAM has proposed benefits such as allowing students to improve their spontaneous speaking skills and reduce their use of speech disfluencies ("um", "uh", etc.). Previous research has highlighted the difficulties students face when speaking publicly, the main one being anxiety. AI Powered Presentation Platforms (AI-PPPs), where students can speak with an immersive AI audience and receive real-time feedback, have been explored as a method to improve student's speaking skills and confidence. So far they have shown promising results which this study aims to build upon. A group of students from the University of York are enlisted to evaluate the effectiveness of the JAM platform. They are asked to fill in a questionnaire, play through the game twice and then complete a final questionnaire to discuss their experiences playing the game. Various statistics are gathered during their gameplay such as the number of points they gained and the number of rules they broke. The results showed that students found the game promising and believed that their speaking skills could improve if they played the game for longer. More work will need to be carried out to prove the effectiveness of the game beyond the short term.
Cross-Modal Reconstruction Pretraining for Ramp Flow Prediction at Highway Interchanges
Venue: arXiv cs.LG 2025
Year: 2025

Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction. To address this, we propose a Spatio-Temporal Decoupled Autoencoder (STDAE), a two-stage framework that leverages cross-modal reconstruction pretraining. In the first stage, STDAE reconstructs historical ramp flows from mainline data, forcing the model to capture intrinsic spatio-temporal relations. Its decoupled architecture with parallel spatial and temporal autoencoders efficiently extracts heterogeneous features. In the prediction stage, the learned representations are integrated with models such as GWNet to enhance accuracy. Experiments on three real-world interchange datasets show that STDAE-GWNET consistently outperforms thirteen state-of-the-art baselines and achieves performance comparable to models using historical ramp data. This demonstrates its effectiveness in overcoming detector scarcity and its plug-and-play potential for diverse forecasting pipelines.
Security Analysis and Threat Modeling of Research Management Applications [Extended Version]
Venue: arXiv cs.CR 2025
Year: 2025

Research management applications (RMA) are widely used in clinical research environments to collect, transmit, analyze, and store sensitive data. This data is so valuable making RMAs susceptible to security threats. This analysis, analyzes RMAs' security, focusing on Research Electronic Data Capture (REDCap) as an example. We explore the strengths and vulnerabilities within RMAs by evaluating the architecture, data flow, and security features. We identify and assess potential risks using the MITRE ATT\&CK framework and STRIDE model. We assess REDCap's defenses against common attack vectors focusing on security to provide confidentiality, integrity, availability, non-repudiation, and authentication. We conclude by proposing recommendations for enhancing the security of RMAs, ensuring that critical research data remains protected without compromising usability. This research aims to contribute towards a more secure framework for managing sensitive information in research-intensive environments.
LHGEL: Large Heterogeneous Graph Ensemble Learning using Batch View Aggregation
Venue: arXiv cs.LG 2025
Year: 2025

Learning from large heterogeneous graphs presents significant challenges due to the scale of networks, heterogeneity in node and edge types, variations in nodal features, and complex local neighborhood structures. This paper advocates for ensemble learning as a natural solution to this problem, whereby training multiple graph learners under distinct sampling conditions, the ensemble inherently captures different aspects of graph heterogeneity. Yet, the crux lies in combining these learners to meet global optimization objective while maintaining computational efficiency on large-scale graphs. In response, we propose LHGEL, an ensemble framework that addresses these challenges through batch sampling with three key components, namely batch view aggregation, residual attention, and diversity regularization. Specifically, batch view aggregation samples subgraphs and forms multiple graph views, while residual attention adaptively weights the contributions of these views to guide node embeddings toward informative subgraphs, thereby improving the accuracy of base learners. Diversity regularization encourages representational disparity across embedding matrices derived from different views, promoting model diversity and ensemble robustness. Our theoretical study demonstrates that residual attention mitigates gradient vanishing issues commonly faced in ensemble learning. Empirical results on five real heterogeneous networks validate that our LHGEL approach consistently outperforms its state-of-the-art competitors by substantial margin. Codes and datasets are available at https://github.com/Chrisshen12/LHGEL.
Scalable Ground Station Selection for Large LEO Constellations
Venue: arXiv cs.NI 2025
Year: 2025

Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce communication gaps between access windows. Traditional ground station selection typically begins by choosing from a fixed set of locations offered by Ground Station-as-a-Service (GSaaS) providers, which helps reduce the problem scope to optimizing locations over existing infrastructure. However, finding a globally optimal solution for stations using existing mixed-integer programming methods quickly becomes intractable at scale, especially when considering multiple providers and large satellite constellations. To address this issue, we introduce a scalable, hierarchical framework that decomposes the global selection problem into single-satellite, short time-window subproblems. Optimal station choices from each subproblem are clustered to identify consistently high-value locations across all decomposed cases. Cluster-level sets are then matched back to the closest GSaaS candidate sites to produce a globally feasible solution. This approach enables scalable coordination while maintaining near-optimal performance. We evaluate our method's performance on synthetic Walker-Star test cases (1-10 satellites, 1-10 stations), achieving solutions within 95% of the global IP optimum for all test cases. Real-world evaluations on Capella Space (5 satellites), ICEYE (40), and Planet's Flock (96) show that while exact IP solutions fail to scale, our framework continues to deliver high-quality site selections.