OpenRaiser/Intern-Atlas huggingface dataset:
Discretely Beyond $1/e$: Guided Combinatorial Algorithms for Submodular Maximization
Venue: arXiv cs.DS 2025
Year: 2025

For constrained, not necessarily monotone submodular maximization, all known approximation algorithms with ratio greater than $1/e$ require continuous ideas, such as queries to the multilinear extension of a submodular function and its gradient, which are typically expensive to simulate with the original set function. For combinatorial algorithms, the best known approximation ratios for both size and matroid constraint are obtained by a simple randomized greedy algorithm of Buchbinder et al. [9]: $1/e \approx 0.367$ for size constraint and $0.281$ for the matroid constraint in $\mathcal O (kn)$ queries, where $k$ is the rank of the matroid. In this work, we develop the first combinatorial algorithms to break the $1/e$ barrier: we obtain approximation ratio of $0.385$ in $\mathcal O (kn)$ queries to the submodular set function for size constraint, and $0.305$ for a general matroid constraint. These are achieved by guiding the randomized greedy algorithm with a fast local search algorithm. Further, we develop deterministic versions of these algorithms, maintaining the same ratio and asymptotic time complexity. Finally, we develop a deterministic, nearly linear time algorithm with ratio $0.377$.
SoMeR: Multi-View User Representation Learning for Social Media
Venue: arXiv cs.SI 2025
Year: 2025

Social media user representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations are critical to a range of social problems, including predicting user behaviors and detecting inauthentic accounts. However, existing methods are either designed for commercial applications, or rely on specific features like text contents, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address these limitations, we propose SoMeR, a Social Media user Representation learning framework that incorporates temporal activities, text contents, profile information, and network interactions to learn comprehensive user portraits. SoMeR encodes user post streams as sequences of time-stamped textual features, uses transformers to embed this along with profile data, and jointly trains with link prediction and contrastive learning objectives to capture user similarity. We demonstrate SoMeR's versatility through three applications: 1) Identifying information operation driver accounts, 2) Measuring online polarization after major events, and 3) Predicting future user participation in Reddit hate communities. SoMeR provides new solutions to better understand user behavior in the socio-political domains, enabling more informed decisions and interventions.
From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences
Venue: arXiv cs.CL 2025
Year: 2025

Current computational approaches for analysing or generating code-mixed sentences do not explicitly model ``naturalness'' or ``acceptability'' of code-mixed sentences, but rely on training corpora to reflect distribution of acceptable code-mixed sentences. Modelling human judgement for the acceptability of code-mixed text can help in distinguishing natural code-mixed text and enable quality-controlled generation of code-mixed text. To this end, we construct Cline - a dataset containing human acceptability judgements for English-Hindi~(en-hi) code-mixed text. Cline is the largest of its kind with 16,642 sentences, consisting of samples sourced from two sources: synthetically generated code-mixed text and samples collected from online social media. Our analysis establishes that popular code-mixing metrics such as CMI, Number of Switch Points, Burstines, which are used to filter/curate/compare code-mixed corpora have low correlation with human acceptability judgements, underlining the necessity of our dataset. Experiments using Cline demonstrate that simple Multilayer Perceptron (MLP) models when trained solely using code-mixing metrics as features are outperformed by fine-tuned pre-trained Multilingual Large Language Models (MLLMs). Specifically, among Encoder models XLM-Roberta and Bernice outperform IndicBERT across different configurations. Among Encoder-Decoder models, mBART performs better than mT5, however Encoder-Decoder models are not able to outperform Encoder-only models. Decoder-only models perform the best when compared to all other MLLMS, with Llama 3.2 - 3B models outperforming similarly sized Qwen, Phi models. Comparison with zero and fewshot capabilitites of ChatGPT show that MLLMs fine-tuned on larger data outperform ChatGPT, providing scope for improvement in code-mixed tasks. Zero-shot transfer from En-Hi to En-Te acceptability judgments are better than random baselines.
Towards a Novel Measure of User Trust in XAI Systems
Venue: arXiv cs.CV 2025
Year: 2025

The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel trust measure in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted three case studies showing an improvement respect the state-of-the-art, with an increased sensitiviy to different scenarios.
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness
Venue: arXiv cs.CR 2025
Year: 2025

AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an ideal platform for providing powerful AIGC network services. At the same time, advanced AIGC techniques can also make future network services more intelligent, especially various online content generation services. However, the significant untrustworthiness concerns of current AIGC models, such as robustness, security, and fairness, greatly affect the credibility of intelligent network services, especially in ensuring secure AIGC services. This paper proposes TrustGAIN, a trustworthy AIGC framework that incorporates robust, secure, and fair network services. We first discuss the robustness to adversarial attacks faced by AIGC models in network systems and the corresponding protection issues. Subsequently, we emphasize the importance of avoiding unsafe and illegal services and ensuring the fairness of the AIGC network services. Then as a case study, we propose a novel sentiment analysis-based detection method to guide the robust detection of unsafe content in network services. We conduct our experiments on fake news, malicious code, and unsafe review datasets to represent LLM application scenarios. Our results indicate that TrustGAIN is an exploration of future networks that can support trustworthy AIGC network services.
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
Venue: arXiv cs.LG 2025
Year: 2025

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba
Venue: arXiv cs.CV 2025
Year: 2025

Event cameras draw inspiration from biological systems, boasting low latency and high dynamic range while consuming minimal power. The most current approach to processing Event Cloud often involves converting it into frame-based representations, which neglects the sparsity of events, loses fine-grained temporal information, and increases the computational burden. In contrast, Point Cloud is a popular representation for processing 3-dimensional data and serves as an alternative method to exploit local and global spatial features. Nevertheless, previous point-based methods show an unsatisfactory performance compared to the frame-based method in dealing with spatio-temporal event streams. In order to bridge the gap, we propose EventMamba, an efficient and effective framework based on Point Cloud representation by rethinking the distinction between Event Cloud and Point Cloud, emphasizing vital temporal information. The Event Cloud is subsequently fed into a hierarchical structure with staged modules to process both implicit and explicit temporal features. Specifically, we redesign the global extractor to enhance explicit temporal extraction among a long sequence of events with temporal aggregation and State Space Model (SSM) based Mamba. Our model consumes minimal computational resources in the experiments and still exhibits SOTA point-based performance on six different scales of action recognition datasets. It even outperformed all frame-based methods on both Camera Pose Relocalization (CPR) and eye-tracking regression tasks. Our code is available at: https://github.com/rhwxmx/EventMamba.
Fast and Slow Mixing of the Kawasaki Dynamics on Bounded-Degree Graphs
Venue: arXiv cs.DS 2025
Year: 2025

We study the worst-case mixing time of the global Kawasaki dynamics for the fixed-magnetization Ising model on the class of graphs of maximum degree $Δ$. Proving a conjecture of Carlson, Davies, Kolla, and Perkins, we show that below the tree uniqueness threshold, the Kawasaki dynamics mix rapidly for all magnetizations. Disproving a conjecture of Carlson, Davies, Kolla, and Perkins, we show that the regime of fast mixing does not extend throughout the regime of tractability for this model: there is a range of parameters for which there exist efficient sampling algorithms for the fixed-magnetization Ising model on max-degree $Δ$ graphs, but the Kawasaki dynamics can take exponential time to mix. Our techniques involve showing spectral independence in the fixed-magnetization Ising model and proving a sharp threshold for the existence of multiple metastable states in the Ising model with external field on random regular graphs.
XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare
Venue: arXiv cs.LG 2025
Year: 2025

Clinical decision support systems require models that are not only highly accurate but also equitable and sensitive to the implications of missed diagnoses. In this study, we introduce a knowledge-guided in-context learning (ICL) framework designed to enable large language models (LLMs) to effectively process structured clinical data. Our approach integrates domain-specific feature groupings, carefully balanced few-shot examples, and task-specific prompting strategies. We systematically evaluate this method across seventy distinct ICL designs by various prompt variations and two different communication styles-natural-language narrative and numeric conversational-and compare its performance to robust classical machine learning (ML) benchmarks on tasks involving heart disease and diabetes prediction. Our findings indicate that while traditional ML models maintain superior performance in balanced precision-recall scenarios, LLMs employing narrative prompts with integrated domain knowledge achieve higher recall and significantly reduce gender bias, effectively narrowing fairness disparities by an order of magnitude. Despite the current limitation of increased inference latency, LLMs provide notable advantages, including the capacity for zero-shot deployment and enhanced equity. This research offers the first comprehensive analysis of ICL design considerations for applying LLMs to tabular clinical tasks and highlights distillation and multimodal extensions as promising directions for future research.
Implementation Study of Cost-Effective Verification for Pietrzak's Verifiable Delay Function in Ethereum Smart Contracts
Venue: arXiv cs.CR 2025
Year: 2025

Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
Towards Browser Controls to Protect Cookies from Malicious Extensions
Venue: arXiv cs.CR 2025
Year: 2025

Cookies maintain state across related web traffic. As such, cookies are commonly used for authentication by storing a user's session ID and replacing the need to re-enter credentials in subsequent traffic. These so-called ``session cookies'' are prime targets for attacks that aim to steal them to gain unauthorized access to user accounts. To mitigate these attacks, the Secure and HttpOnly cookie attributes limit a cookie's accessibility from malicious networks and websites. However, these controls overlook browser extensions: third-party HTML/JavaScript add-ons with access to privileged browser APIs and the ability to operate across multiple websites. Thus malicious or compromised extensions can provide unrestricted access to a user's session cookies. In this work, we first analyze the prevalence of extensions with access to ``risky'' APIs (those that enable cookie modification and theft) and find that they have hundreds of millions of users. Motivated by this, we propose a mechanism to protect cookies from malicious extensions by introducing two new cookie attributes: BrowserOnly and Monitored. The BrowserOnly attribute prevents extension access to cookies altogether. While effective, not all cookies can be made inaccessible. Thus cookies with the Monitored attribute remain accessible but are tied to a single browser and any changes made to the cookie are logged. As a result, stolen Monitored cookies are unusable outside their original browser and servers can validate the modifications performed. To demonstrate the proposed functionalities, we design and implement CREAM (Cookie Restrictions for Extension Abuse Mitigation) a modified version of the open-source Chromium browser realizing these controls. Our evaluation indicates that CREAM effectively protects cookies from malicious extensions while incurring little run-time overheads.
Direct Learning of Mesh and Appearance via 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Accurately reconstructing a 3D scene including explicit geometry information is both attractive and challenging. Geometry reconstruction can benefit from incorporating differentiable appearance models, such as Neural Radiance Fields and 3D Gaussian Splatting (3DGS). However, existing methods encounter efficiency issues due to indirect geometry learning and the paradigm of separately modeling geometry and surface appearance. In this work, we propose a learnable scene model that incorporates 3DGS with an explicit geometry representation, namely a mesh. Our model learns the mesh and appearance in an end-to-end manner, where we bind 3D Gaussians to the mesh faces and perform differentiable rendering of 3DGS to obtain photometric supervision. The model creates an effective information pathway to supervise the learning of both 3DGS and mesh. Experimental results demonstrate that the learned scene model not only improves efficiency and rendering quality but also enables manipulation via the explicit mesh. In addition, our model has a unique advantage in adapting to scene updates, thanks to the end-to-end learning of both mesh and appearance.
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
Venue: arXiv cs.CV 2025
Year: 2025

X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coefficients (LACs) of metals vary significantly with X-ray energy, causing a nonlinear beam hardening effect (BHE) in CT measurements. Reconstructing CT images from metal-corrupted measurements consequently becomes a challenging nonlinear inverse problem. Existing state-of-the-art (SOTA) metal artifact reduction (MAR) algorithms rely on supervised learning with numerous paired CT samples. While promising, these supervised methods often assume that the unknown LACs are energy-independent, ignoring the energy-induced BHE, which results in limited generalization. Moreover, the requirement for large datasets also limits their applications in real-world scenarios. In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method. Our key innovation lies in formulating MAR as an energy-independent density reconstruction problem that strictly adheres to the photon-tissue absorption physical model. This model is inherently nonlinear and complex, making it a rarely considered approach in inverse imaging problems. By introducing the water-equivalent tissues approximation and a new polychromatic model to characterize the nonlinear CT acquisition process, we directly learn the neural representation of the density map from raw measurements without using external training data. This energy-independent density reconstruction framework fundamentally resolves the nonlinear BHE, enabling superior MAR performance across a wide range of scanning scenarios. Extensive experiments on both simulated and real-world datasets demonstrate the superiority of our unsupervised Diner over popular supervised methods in terms of MAR performance and robustness.
LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation
Venue: arXiv cs.HC 2025
Year: 2025

Creating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method performs visual analysis, instantiates a design concept, and conducts visual checking to generate animation code. LogoMotion provides novices with code-connected AI editing widgets that help them edit the motion, grouping, and timing of their animation. In a comparison study on 276 animations, LogoMotion was found to produce more content-aware animation than an industry-leading tool. In a user evaluation (n=16) comparing against a prompt-only baseline, these code-connected widgets helped users edit animations with control, iteration, and creative expression.
Case Study of Novelty, Complexity, and Adaptation in a Multicellular System
Venue: arXiv cs.NE 2025
Year: 2025

Continuing generation of novelty, complexity, and adaptation are well-established as core aspects of open-ended evolution. However, it has yet to be firmly established to what extent these phenomena are coupled and by what means they interact. In this work, we track the co-evolution of novelty, complexity, and adaptation in a case study from the DISHTINY simulation system, which is designed to study the evolution of digital multicellularity. In this case study, we describe ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. We contextualize the evolutionary history of these morphologies with measurements of complexity and adaptation. Our case study suggests a loose -- sometimes divergent -- relationship can exist among novelty, complexity, and adaptation.
Motion Keyframe Interpolation for Any Human Skeleton via Temporally Consistent Point Cloud Sampling and Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

In the character animation field, modern supervised keyframe interpolation models have demonstrated exceptional performance in constructing natural human motions from sparse pose definitions. As supervised models, large motion datasets are necessary to facilitate the learning process; however, since motion is represented with fixed hierarchical skeletons, such datasets are incompatible for skeletons outside the datasets' native configurations. Consequently, the expected availability of a motion dataset for desired skeletons severely hinders the feasibility of learned interpolation in practice. To combat this limitation, we propose Point Cloud-based Motion Representation Learning (PC-MRL), an unsupervised approach to enabling cross-compatibility between skeletons for motion interpolation learning. PC-MRL consists of a skeleton obfuscation strategy using temporal point cloud sampling, and an unsupervised skeleton reconstruction method from point clouds. We devise a temporal point-wise K-nearest neighbors loss for unsupervised learning. Moreover, we propose First-frame Offset Quaternion (FOQ) and Rest Pose Augmentation (RPA) strategies to overcome necessary limitations of our unsupervised point cloud-to-skeletal motion process. Comprehensive experiments demonstrate the effectiveness of PC-MRL in motion interpolation for desired skeletons without supervision from native datasets.
Wild Berry image dataset collected in Finnish forests and peatlands using drones
Venue: arXiv cs.CV 2025
Year: 2025

Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands and under the canopy of Finnish forests using drones. Unlike previous and related datasets, WildBe includes new varieties of berries, such as bilberries, cloudberries, lingonberries, and crowberries, captured under severe light variations and in cluttered environments. WildBe features 3,516 images, including a total of 18,468 annotated bounding boxes. We carry out a comprehensive analysis of WildBe using six popular object detectors, assessing their effectiveness in berry detection across different forest regions and camera types. WildBe is publicly available on HuggingFace at https://huggingface.co/datasets/FBK-TeV/WildBe.