OpenRaiser/Intern-Atlas huggingface dataset:
SONIC: Sound Optimization for Noise In Crowds
Venue: arXiv cs.SD 2025
Year: 2025

This paper presents SONIC, an embedded real-time noise suppression system implemented on the ARM Cortex-M7-based STM32H753ZI microcontroller. Using adaptive filtering (LMS), the system improves speech intelligibility in noisy environments. SONIC focuses on a novel approach to noise suppression in audio signals, specifically addressing the limitations of traditional Active Noise Cancellation (ANC) systems. The paper explores various signal processing algorithms in a micro-controller point of view, highlighting various performance factors and which were considered optimal in our embedded system. Additionally we also discussed the system architecture, explaining how the MCU's efficiency was harnessed, along with an in-depth overview of how the audio signals were translated within the processor. The results demonstrate improved speech clarity and practical real-time performance, showing low-power DSP as an alternative to complex AI denoising methods.
The Transition Matrix -- A classification of navigational patterns between LMS course sections
Venue: arXiv cs.CY 2025
Year: 2025

Learning management systems (LMS) like Moodle are increasingly used to support university teaching. As Moodle courses become more complex, incorporating diverse interactive elements, it is important to understand how students navigate through course sections and whether course designs are meeting student needs. While substantial research exists on student usage of individual LMS elements, there is a lack of research on broader navigational patterns between course sections and how these patterns differ across courses. This study analyzes navigational data from 747 courses in the Moodle LMS at a technical university of applied sciences, representing (after filtering) around 4,400 students and 1.8 million logged events. Transition matrices and heat map visualizations are used to identify and quantify common navigational patterns. Findings include that the majority of the analyzed courses exhibit some kind of diagonal pattern, indicating that students typically navigate from the current to the next or previous section.
Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks
Venue: arXiv cs.NI 2025
Year: 2025

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose an Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) Algorithm, a scalable and adaptive framework that dynamically adjusts UAV placements based on real-time user locations and spatial traffic distribution. In contrast to existing methods, EMTAD jointly optimizes UAV positioning and minimizes the number of deployed UAVs, ensuring efficient UE-UAV association while satisfying the traffic demand of users. Simulation results demonstrate that EMTAD significantly improves network performance while reducing deployment overhead by minimizing the number of UAVs required in dynamic and traffic-aware environments.
Digital Transformation of Urban Planning in Australia: Influencing Factors and Key Challenges
Venue: arXiv cs.IT 2025
Year: 2025

Over the past two decades, several governments in developing and developed countries have started their journey toward digital transformation. However, the pace and maturity of digital technologies and strategies are different between public services. Current literature indicates that research on the digital transformation of urban planning is still developing. Therefore, the aim of this study is to understand the influencing factors and key challenges for the digital transformation of urban planning in Australia. The study adopts the inter-organisational theory and Planning Support Science (PSScience) under the Technological, Organisational, and External Environmental (TOE) framework. It involves a multiple case study, administered semi-structured interviews with thirteen IT and urban planning experts across Victoria and New South Wales governments and private industries. The study findings indicate that the main challenges for digital transformation of the Australian urban planning system are related to organisational and external environmental factors. Furthermore, a digital maturity model is absent in the Australian urban planning industry. This study offers important implications to research and practice related to digital transformation in urban planning.
Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders
Venue: arXiv cs.CV 2025
Year: 2025

Grapevine varieties are essential for the economies of many wine-producing countries, influencing the production of wine, juice, and the consumption of fruits and leaves. Traditional identification methods, such as ampelography and molecular analysis, have limitations: ampelography depends on expert knowledge and is inherently subjective, while molecular methods are costly and time-intensive. To address these limitations, recent studies have applied deep learning (DL) models to classify grapevine varieties using image data. However, due to the small dataset sizes, these methods often depend on transfer learning from datasets from other domains, e.g., ImageNet1K (IN1K), which can lead to performance degradation due to domain shift and supervision collapse. In this context, self-supervised learning (SSL) methods can be a good tool to avoid this performance degradation, since they can learn directly from data, without external labels. This study presents an evaluation of Masked Autoencoders (MAEs) for identifying grapevine varieties based on field-acquired images. The main contributions of this study include two benchmarks comprising 43 grapevine varieties collected across different seasons, an analysis of MAE's application in the agricultural context, and a performance comparison of trained models across seasons. Our results show that a ViT-B/16 model pre-trained with MAE and the unlabeled dataset achieved an F1 score of 0.7956, outperforming all other models. Additionally, we observed that pre-trained models benefit from long pre-training, perform well under low-data training regime, and that simple data augmentation methods are more effective than complex ones. The study also found that the mask ratio in MAE impacts performance only marginally.
LapDDPM: A Conditional Graph Diffusion Model for scRNA-seq Generation with Spectral Adversarial Perturbations
Venue: arXiv cs.LG 2025
Year: 2025

Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion model, enhanced by a novel spectral adversarial perturbation mechanism on graph edge weights. Our contributions are threefold: we leverage Laplacian Positional Encodings (LPEs) to enrich the latent space with crucial cellular relationship information; we develop a conditional score-based diffusion model for effective learning and generation from complex scRNA-seq distributions; and we employ a unique spectral adversarial training scheme on graph edge weights, boosting robustness against structural variations. Extensive experiments on diverse scRNA-seq datasets demonstrate LapDDPM's superior performance, achieving high fidelity and generating biologically-plausible, cell-type-specific samples. LapDDPM sets a new benchmark for conditional scRNA-seq data generation, offering a robust tool for various downstream biological applications.
DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Video face restoration faces a critical challenge in maintaining temporal consistency while recovering fine facial details from degraded inputs. This paper presents a novel approach that extends Vector-Quantized Variational Autoencoders (VQ-VAEs), pretrained on static high-quality portraits, into a video restoration framework through variational latent space modeling. Our key innovation lies in reformulating discrete codebook representations as Dirichlet-distributed continuous variables, enabling probabilistic transitions between facial features across frames. A spatio-temporal Transformer architecture jointly models inter-frame dependencies and predicts latent distributions, while a Laplacian-constrained reconstruction loss combined with perceptual (LPIPS) regularization enhances both pixel accuracy and visual quality. Comprehensive evaluations on blind face restoration, video inpainting, and facial colorization tasks demonstrate state-of-the-art performance. This work establishes an effective paradigm for adapting intensive image priors, pretrained on high-quality images, to video restoration while addressing the critical challenge of flicker artifacts. The source code has been open-sourced and is available at https://github.com/fudan-generative-vision/DicFace.
StoryBench: A Dynamic Benchmark for Evaluating Long-Term Memory with Multi Turns
Venue: arXiv cs.CL 2025
Year: 2025

Long-term memory (LTM) is essential for large language models (LLMs) to achieve autonomous intelligence in complex, evolving environments. Despite increasing efforts in memory-augmented and retrieval-based architectures, there remains a lack of standardized benchmarks to systematically evaluate LLMs' long-term memory abilities. Existing benchmarks still face challenges in evaluating knowledge retention and dynamic sequential reasoning, and in their own flexibility, all of which limit their effectiveness in assessing models' LTM capabilities. To address these gaps, we propose a novel benchmark framework based on interactive fiction games, featuring dynamically branching storylines with complex reasoning structures. These structures simulate real-world scenarios by requiring LLMs to navigate hierarchical decision trees, where each choice triggers cascading dependencies across multi-turn interactions. Our benchmark emphasizes two distinct settings to test reasoning complexity: one with immediate feedback upon incorrect decisions, and the other requiring models to independently trace back and revise earlier choices after failure. As part of this benchmark, we also construct a new dataset designed to test LLMs' LTM within narrative-driven environments. We further validate the effectiveness of our approach through detailed experiments. Experimental results demonstrate the benchmark's ability to robustly and reliably assess LTM in LLMs.
Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation
Venue: arXiv cs.AI 2025
Year: 2025

Current Reinforcement Learning (RL) methodologies for Large Language Models (LLMs) often rely on simplistic, outcome-based reward signals (e.g., final answer correctness), which limits the depth of learning from each interaction. This paper introduces Socratic Reinforcement Learning (Socratic-RL), a novel, process-oriented framework designed to address this limitation. Socratic-RL operates on the principle that deeper understanding is achieved by reflecting on the causal reasons for errors and successes within the reasoning process itself. The framework employs a decoupled "Teacher-Student" architecture, where a "Teacher AI" analyzes interaction histories, extracts causal insights, and formulates them into structured "viewpoints." These viewpoints, acting as distilled guidance, are then used by a "Student AI" to enhance its subsequent reasoning. A key innovation is the iterative self-improvement of the Teacher AI, enabling its reflective capabilities to evolve through a meta-learning loop. To manage the accumulation of knowledge, a distillation mechanism compresses learned viewpoints into the Student's parameters. By focusing on process rather than just outcome, Socratic-RL presents a pathway toward enhanced sample efficiency, superior interpretability, and a more scalable architecture for self-improving AI systems. This paper details the foundational concepts, formal mechanisms, synergies, challenges, and a concrete research roadmap for this proposed framework.
The Structure-Content Trade-off in Knowledge Graph Retrieval
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) increasingly rely on knowledge graphs for factual reasoning, yet how retrieval design shapes their performance remains unclear. We examine how question decomposition changes the retrieved subgraph's content and structure. Using a hybrid retrieval function that controls the importance of initial question and subquestions, we show that subquestion-based retrieval improves content precision, but yields disjoint subgraphs, while question-based retrieval maintains structure at the cost of relevance. Optimal performance arises between these extremes, revealing that balancing retrieval content and structure is key to effective LLM reasoning over structured knowledge.
Enhancing Orthopedic Surgical Training With Interactive Photorealistic 3D Visualization
Venue: arXiv cs.HC 2025
Year: 2025

Surgical training integrates several years of didactic learning, simulation, mentorship, and hands-on experience. Challenges include stress, technical demands, and new technologies. Orthopedic education often uses static materials like books, images, and videos, lacking interactivity. This study compares a new interactive photorealistic 3D visualization to 2D videos for learning total hip arthroplasty. In a randomized controlled trial, participants (students and residents) were evaluated on spatial awareness, tool placement, and task times in a simulation. Results show that interactive photorealistic 3D visualization significantly improved scores, with residents and those with prior 3D experience performing better. These results emphasize the potential of the interactive photorealistic 3D visualization to enhance orthopedic training.
Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have achieved remarkable success in imaging inverse problems owing to their powerful generative capabilities. However, existing approaches typically rely on models trained for specific degradation types, limiting their generalizability to various degradation scenarios. To address this limitation, we propose a zero-shot framework capable of handling various imaging inverse problems without model retraining. We introduce a likelihood-guided noise refinement mechanism that derives a closed-form approximation of the likelihood score, simplifying score estimation and avoiding expensive gradient computations. This estimated score is subsequently utilized to refine the model-predicted noise, thereby better aligning the restoration process with the generative framework of diffusion models. In addition, we integrate the Denoising Diffusion Implicit Models (DDIM) sampling strategy to further improve inference efficiency. The proposed mechanism can be applied to both optimization-based and sampling-based schemes, providing an effective and flexible zero-shot solution for imaging inverse problems. Extensive experiments demonstrate that our method achieves superior performance across multiple inverse problems, particularly in compressive sensing, delivering high-quality reconstructions even at an extremely low sampling rate (5%).
New characterization of full weight spectrum one-orbit cyclic subspace codes
Venue: arXiv cs.CR 2025
Year: 2025

Castello $\textit{et al}$. [J. Comb. Theory Ser. A, 212, 106005 (2025)] provided a complete classification for full weight spectrum (FWS) one-orbit cyclic subspace codes. In this paper, we determine the weight distributions of a family of FWS codes and exhibit some equivalence classes of FWS codes under certain conditions. Furthermore, we provide a complete classification for $r$-FWS codes.
VLM-SFD: VLM-Assisted Siamese Flow Diffusion Framework for Dual-Arm Cooperative Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Dual-arm cooperative manipulation holds great promise for tackling complex real-world tasks that demand seamless coordination and adaptive dynamics. Despite substantial progress in learning-based motion planning, most approaches struggle to generalize across diverse manipulation tasks and adapt to dynamic, unstructured environments, particularly in scenarios involving interactions between two objects such as assembly, tool use, and bimanual grasping. To address these challenges, we introduce a novel VLM-Assisted Siamese Flow Diffusion (VLM-SFD) framework for efficient imitation learning in dual-arm cooperative manipulation. The proposed VLM-SFD framework exhibits outstanding adaptability, significantly enhancing the ability to rapidly adapt and generalize to diverse real-world tasks from only a minimal number of human demonstrations. Specifically, we propose a Siamese Flow Diffusion Network (SFDNet) employs a dual-encoder-decoder Siamese architecture to embed two target objects into a shared latent space, while a diffusion-based conditioning process - conditioned by task instructions - generates two-stream object-centric motion flows that guide dual-arm coordination. We further design a dynamic task assignment strategy that seamlessly maps the predicted 2D motion flows into 3D space and incorporates a pre-trained vision-language model (VLM) to adaptively assign the optimal motion to each robotic arm over time. Experiments validate the effectiveness of the proposed method, demonstrating its ability to generalize to diverse manipulation tasks while maintaining high efficiency and adaptability. The code and demo videos are publicly available on our project website https://sites.google.com/view/vlm-sfd/.
Uncertainty-Aware Remaining Lifespan Prediction from Images
Venue: arXiv cs.CV 2025
Year: 2025

Predicting mortality-related outcomes from images offers the prospect of accessible, noninvasive, and scalable health screening. We present a method that leverages pretrained vision transformer foundation models to estimate remaining lifespan from facial and whole-body images, alongside robust uncertainty quantification. We show that predictive uncertainty varies systematically with the true remaining lifespan, and that this uncertainty can be effectively modeled by learning a Gaussian distribution for each sample. Our approach achieves state-of-the-art mean absolute error (MAE) of 7.41 years on an established dataset, and further achieves 4.91 and 4.99 years MAE on two new, higher-quality datasets curated and published in this work. Importantly, our models provide calibrated uncertainty estimates, as demonstrated by a bucketed expected calibration error of 0.82 years on the Faces Dataset. While not intended for clinical deployment, these results highlight the potential of extracting medically relevant signals from images. We make all code and datasets available to facilitate further research.
Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models
Venue: arXiv cs.CL 2025
Year: 2025

The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at https://github.com/JoseAdrian3/NeuroEmbed.
Multimodal Integration Challenges in Emotionally Expressive Child Avatars for Training Applications
Venue: arXiv cs.HC 2025
Year: 2025

Dynamic facial emotion is essential for believable AI-generated avatars, yet most systems remain visually static, limiting their use in simulations like virtual training for investigative interviews with abused children. We present a real-time architecture combining Unreal Engine 5 MetaHuman rendering with NVIDIA Omniverse Audio2Face to generate facial expressions from vocal prosody in photorealistic child avatars. Due to limited TTS options, both avatars were voiced using young adult female models from two systems to better fit character profiles, introducing a voice-age mismatch. This confound may affect audiovisual alignment. We used a two-PC setup to decouple speech generation from GPU-intensive rendering, enabling low-latency interaction in desktop and VR. A between-subjects study (N=70) compared audio+visual vs. visual-only conditions as participants rated emotional clarity, facial realism, and empathy for avatars expressing joy, sadness, and anger. While emotions were generally recognized - especially sadness and joy - anger was harder to detect without audio, highlighting the role of voice in high-arousal expressions. Interestingly, silencing clips improved perceived realism by removing mismatches between voice and animation, especially when tone or age felt incongruent. These results emphasize the importance of audiovisual congruence: mismatched voice undermines expression, while a good match can enhance weaker visuals - posing challenges for emotionally coherent avatars in sensitive contexts.