OpenRaiser/Intern-Atlas huggingface dataset:
Hyperbolic Diffusion Recommender Model
Venue: arXiv cs.IR 2025
Year: 2025

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities between images and items. Consequently, items often exhibit distinct anisotropic and directional structures that are less prevalent in images. However, the traditional forward diffusion process continuously adds isotropic Gaussian noise, causing anisotropic signals to degrade into noise, which impairs the semantically meaningful representations in recommender systems. Inspired by the advancements in hyperbolic spaces, we propose a novel \textit{\textbf{H}yperbolic} \textit{\textbf{D}iffusion} \textit{\textbf{R}ecommender} \textit{\textbf{M}odel} (named HDRM). Unlike existing directional diffusion methods based on Euclidean space, the intrinsic non-Euclidean structure of hyperbolic space makes it particularly well-adapted for handling anisotropic diffusion processes. In particular, we begin by formulating concepts to characterize latent directed diffusion processes within a geometrically grounded hyperbolic space. Subsequently, we propose a novel hyperbolic latent diffusion process specifically tailored for users and items. Drawing upon the natural geometric attributes of hyperbolic spaces, we impose structural restrictions on the space to enhance hyperbolic diffusion propagation, thereby ensuring the preservation of the intrinsic topology of user-item graphs. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HDRM.
8-DoFs Cable Driven Parallel Robots for Bimanual Teleportation
Venue: arXiv cs.RO 2025
Year: 2025

Teleoperation plays a critical role in intuitive robot control and imitation learning, particularly for complex tasks involving mobile manipulators with redundant degrees of freedom (DoFs). However, most existing master controllers are limited to 6-DoF spatial control and basic gripper control, making them insufficient for controlling high-DoF robots and restricting the operator to a small workspace. In this work, we present a novel, low-cost, high-DoF master controller based on Cable-Driven Parallel Robots (CDPRs), designed to overcome these limitations. The system decouples translation and orientation control, following a scalable 3 + 3 + n DoF structure: 3 DoFs for large-range translation using a CDPR, 3 DoFs for orientation using a gimbal mechanism, and n additional DoFs for gripper and redundant joint control. Its lightweight cable-driven design enables a large and adaptable workspace while minimizing actuator load. The end-effector remains stable without requiring continuous high-torque input, unlike most serial robot arms. We developed the first dual-arm CDPR-based master controller using cost-effective actuators and a simple mechanical structure. In demonstrations, the system successfully controlled an 8-DoF robotic arm with a 2-DoF pan-tilt camera, performing tasks such as pick-and-place, knot tying, object sorting, and tape application. The results show precise, versatile, and practical high-DoF teleoperation.
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
Venue: arXiv cs.IR 2025
Year: 2025

Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially from unstructured sources like news, presenting a need for innovative tools. Methodology: The study introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, offering functionalities such as de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging AI to reorder textual data based on relevancy, employing neural networks for semantic similarity, and subsequently prioritizing likely relevant entries for human review. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 62% reduction in manual review efforts at 95% recall. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. A smaller case-study on our news datasets shows the potential of ensembling large language models within the active-learning process for faster detection of relevant articles across news datasets. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.
Proposition of Affordance-Driven Environment Recognition Framework Using Symbol Networks in Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

In the quest to enable robots to coexist with humans, understanding dynamic situations and selecting appropriate actions based on common sense and affordances are essential. Conventional AI systems face challenges in applying affordance, as it represents implicit knowledge derived from common sense. However, large language models (LLMs) offer new opportunities due to their ability to process extensive human knowledge. This study proposes a method for automatic affordance acquisition by leveraging LLM outputs. The process involves generating text using LLMs, reconstructing the output into a symbol network using morphological and dependency analysis, and calculating affordances based on network distances. Experiments using ``apple'' as an example demonstrated the method's ability to extract context-dependent affordances with high explainability. The results suggest that the proposed symbol network, reconstructed from LLM outputs, enables robots to interpret affordances effectively, bridging the gap between symbolized data and human-like situational understanding.
A flexible framework for early power and timing comparison of time-multiplexed CGRA kernel executions
Venue: arXiv cs.AR 2025
Year: 2025

At the intersection between traditional CPU architectures and more specialized options such as FPGAs or ASICs lies the family of reconfigurable hardware architectures, termed Coarse-Grained Reconfigurable Arrays (CGRAs). CGRAs are composed of a 2-dimensional array of processing elements (PE), tightly integrated with each other, each capable of performing arithmetic and logic operations. The vast design space of CGRA implementations poses a challenge, which calls for fast exploration tools to prune it in advance of time-consuming syntheses. The proposed tool aims to simplify this process by simulating kernel execution and providing a characterization framework. The estimator returns energy and latency values otherwise only available through a time-consuming post-synthesis simulation, allowing for instantaneous comparative analysis between different kernels and hardware configurations.
Construction of MDS Euclidean Self-Dual Codes via Multiple Subsets
Venue: arXiv cs.IT 2025
Year: 2025

MDS self-dual codes have good algebraic structure, and their parameters are completely determined by the code length. In recent years, the construction of MDS Euclidean self-dual codes with new lengths has become an important issue in coding theory. In this paper, we are committed to constructing new MDS Euclidean self-dual codes via generalized Reed-Solomon (GRS) codes and their extended (EGRS) codes. The main effort of our constructions is to find suitable subsets of finite fields as the evaluation sets, ensuring that the corresponding (extended) GRS codes are Euclidean self-dual. Firstly, we present a method for selecting evaluation sets from multiple intersecting subsets and provide a theorem to guarantee that the chosen evaluation sets meet the desired criteria. Secondly, based on this theorem, we construct six new classes of MDS Euclidean self-dual codes using the norm function, as well as the union of three multiplicity subgroups and their cosets respectively. Finally, in our constructions, the proportion of possible MDS Euclidean self-dual codes exceeds 85\%, which is much higher than previously reported results.
Linear Time Iterative Decoders for Hypergraph-Product and Lifted-Product Codes
Venue: arXiv cs.IT 2025
Year: 2025

Quantum low-density parity-check (QLDPC) codes with asymptotically non-zero rates are prominent candidates for achieving fault-tolerant quantum computation, primarily due to their syndrome-measurement circuit's low operational depth. Numerous studies advocate for the necessity of fast decoders to fully harness the capabilities of QLDPC codes, thus driving the focus towards designing low-complexity iterative decoders. However, empirical investigations indicate that such iterative decoders are susceptible to having a high error floor while decoding QLDPC codes. The main objective of this paper is to analyze the decoding failures of the \emph{hypergraph-product} and \emph{lifted-product} codes and to design decoders that mitigate these failures, thus achieving a reduced error floor. The suboptimal performance of these codes can predominantly be ascribed to two structural phenomena: (1) stabilizer-induced trapping sets, which are subgraphs formed by stabilizers, and (2) classical trapping sets, which originate from the classical codes utilized in the construction of hypergraph-product and lifted-product codes. The dynamics of stabilizer-induced trapping sets is examined and a straightforward modification of iterative decoders is proposed to circumvent these trapping sets. Moreover, this work proposes a systematic methodology for designing decoders that can circumvent classical trapping sets in both hypergraph product and lifted product codes, from decoders capable of avoiding their trapping set in the parent classical LDPC code. When decoders that can avoid stabilizer-induced trapping sets are run in parallel with those that can mitigate the effect of classical TS, the logical error rate improves significantly in the error-floor region.
Enlightenment Period Improving DNN Performance
Venue: arXiv cs.LG 2025
Year: 2025

The start of deep neural network training is characterized by a brief yet critical phase that lasts from the beginning of the training until the accuracy reaches approximately 50\%. During this phase, disordered representations rapidly transition toward ordered structure, and we term this phase the Enlightenment Period. Through theoretical modeling based on phase transition theory and experimental validation, we reveal that applying Mixup data augmentation during this phase has a dual effect: it introduces a Gradient Interference Effect that hinders performance, while also providing a beneficial Activation Revival Effect to restore gradient updates for saturated neurons. We further demonstrate that this negative interference diminishes as the sample set size or the model parameter size increases, thereby shifting the balance between these two effects. Based on these findings, we propose three strategies that improve performance by solely adjusting the training data distribution within this brief period: the Mixup Pause Strategy for small-scale scenarios, the Alpha Boost Strategy for large-scale scenarios with underfitting, and the High-Loss Removal Strategy for tasks where Mixup is inapplicable (e.g., time series and large language models). Extensive experiments show that these strategies achieve superior performance across diverse architectures such as ViT and ResNet on datasets including CIFAR and ImageNet-1K. Ultimately, this work offers a novel perspective on enhancing model performance by strategically capitalizing on the dynamics of the brief and crucial early stages of training. Code is available at https://anonymous.4open.science/r/code-A5F1/.
Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers
Venue: arXiv cs.CV 2025
Year: 2025

Understanding the perceptual invariances of artificial neural networks is essential for improving explainability and aligning models with human vision. Metamers - stimuli that are physically distinct yet produce identical neural activations - serve as a valuable tool for investigating these invariances. We introduce a novel approach to metamer generation by leveraging ensembles of artificial neural networks, capturing shared representational subspaces across diverse architectures, including convolutional neural networks and vision transformers. To characterize the properties of the generated metamers, we employ a suite of image-based metrics that assess factors such as semantic fidelity and naturalness. Our findings show that convolutional neural networks generate more recognizable and human-like metamers, while vision transformers produce realistic but less transferable metamers, highlighting the impact of architectural biases on representational invariances.
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) enables distributed devices to train a shared machine learning (ML) model collaboratively while protecting their data privacy. However, the resource-limited mobile devices suffer from intensive computation-and-communication costs of model parameters. In this paper, we observe the phenomenon that the model parameters tend to be stabilized long before convergence during training process. Based on this observation, we propose a two-timescale FL framework by joint optimization of freezing stabilized parameters and controlling transmit power for the unstable parameters to balance the energy consumption and convergence. First, we analyze the impact of model parameter freezing and unreliable transmission on the convergence rate. Next, we formulate a two-timescale optimization problem of parameter freezing percentage and transmit power to minimize the model convergence error subject to the energy budget. To solve this problem, we decompose it into parallel sub-problems and decompose each sub-problem into two different timescales problems using the Lyapunov optimization method. The optimal parameter freezing and power control strategies are derived in an online fashion. Experimental results demonstrate the superiority of the proposed scheme compared with the benchmark schemes.
SOLAQUA: SINTEF Ocean Large Aquaculture Robotics Dataset
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a dataset gathered with an underwater robot in a sea-based aquaculture setting. Data was gathered from an operational fish farm and includes data from sensors such as the Waterlinked A50 DVL, the Nortek Nucleus 1000 DVL, Sonardyne Micro Ranger 2 USBL, Sonoptix Mulitbeam Sonar, mono and stereo cameras, and vehicle sensor data such as power usage, IMU, pressure, temperature, and more. Data acquisition is performed during both manual and autonomous traversal of the net pen structure. The collected vision data is of undamaged nets with some fish and marine growth presence, and it is expected that both the research community and the aquaculture industry will benefit greatly from the utilization of the proposed SOLAQUA dataset.
Virtual Target Trajectory Prediction for Stochastic Targets
Venue: arXiv cs.RO 2025
Year: 2025

Trajectory prediction of aerial vehicles is a key requirement in applications ranging from missile guidance to UAV collision avoidance. While most prediction methods assume deterministic target motion, real-world targets often exhibit stochastic behaviors such as evasive maneuvers or random gliding patterns. This paper introduces a probabilistic framework based on Conditional Normalizing Flows (CNFs) to model and predict such stochastic dynamics directly from trajectory data. The learned model generates probability distributions of future target positions conditioned on initial states and dynamic parameters, enabling efficient sampling and exact density evaluation. To provide deterministic surrogates compatible with existing guidance and planning algorithms, sampled trajectories are clustered using a time series k-means approach, yielding a set of representative "virtual target" trajectories. The method is target-agnostic, computationally efficient, and requires only trajectory data for training, making it suitable as a drop-in replacement for deterministic predictors. Simulated scenarios with maneuvering and ballistic targets demonstrate that the proposed approach bridges the gap between deterministic assumptions and stochastic reality, advancing guidance and control algorithms for autonomous vehicles.
Improved Bounds for Coin Flipping, Leader Election, and Random Selection
Venue: arXiv cs.CC 2025
Year: 2025

Random selection, leader election, and collective coin flipping are fundamental tasks in fault-tolerant distributed computing. We study these problems in the full-information model where despite decades of study, key gaps remain in our understanding of the trade-offs between round complexity, communication per player in each round, and adversarial resilience. We make progress by proving improved bounds for these problems. We first show that any $k$-round coin flipping protocol over $\ell$ players, each player sending one bit per round, can be biased by $O(\ell/\log^{(k)}(\ell))$ bad players. We obtain a similar lower bound for leader election. This strengthens prior best bounds [RSZ, SICOMP 2002] of $O(\ell/\log^{(2k-1)}(\ell))$ for coin flipping protocols and $O(\ell/\log^{(2k+1)}(\ell))$ for leader election protocols. Our result implies that any (1-bit per player) protocol tolerating linear fraction of bad players requires at least $\log^* \ell$ rounds, showing existing protocols [RZ, JCSS 2001; F, FOCS 1999] are near-optimal. We next initiate the study of one-round, (1-bit per player) random selection. We construct a protocol resilient to $\ell / (\log \ell)^2$ bad players that outputs $(\log \ell)^2 / (\log \log \ell)^2$ uniform random bits. This implies a one-round leader election protocol resilient to $\ell / (\log \ell)^2$ bad players, improving the prior best protocol [RZ, JCSS 2001] which was resilient to $\ell / (\log \ell)^3$ bad players. Our resilience matches that of the best one-round coin flipping protocol by Ajtai & Linial. We also obtain an almost matching lower bound: any protocol outputting $(\log \ell)^2 / (\log \log \ell)^2$ bits can be corrupted by $\ell (\log \log \ell)^2 / (\log \ell)^2$ bad players. To obtain our lower bound, we introduce multi-output influence, an extension of influence of boolean functions to the multi-output setting.
Extending MovieLens-32M to Provide New Evaluation Objectives
Venue: arXiv cs.IR 2025
Year: 2025

Offline evaluation of recommender systems has traditionally treated the problem as a machine learning problem. In the classic case of recommending movies, where the user has provided explicit ratings of which movies they like and don't like, each user's ratings are split into test and train sets, and the evaluation task becomes to predict the held out test data using the training data. This machine learning style of evaluation makes the objective to recommend the movies that a user has watched and rated highly, which is not the same task as helping the user find movies that they would enjoy if they watched them. This mismatch in objective between evaluation and task is a compromise to avoid the cost of asking a user to evaluate recommendations by watching each movie. We offer an extension to the MovieLens-32M dataset that provides for new evaluation objectives. Our primary objective is to predict the movies that a user would be interested in watching, i.e. predict their watchlist. To construct this extension, we recruited MovieLens users, collected their profiles, made recommendations with a diverse set of algorithms, pooled the recommendations, and had the users assess the pools. This paper demonstrates the feasibility of using pooling to construct a test collection for recommender systems. Notably, we found that the traditional machine learning style of evaluation ranks the Popular algorithm, which recommends movies based on total number of ratings in the system, in the middle of the twenty-two recommendation runs we used to build the pools. In contrast, when we rank the runs by users' interest in watching movies, we find that recommending popular movies as a recommendation algorithm becomes one of the worst performing runs. It appears that by asking users to assess their personal recommendations, we can alleviate the issue of popularity bias in the evaluation of top-n recommendation.
Focal Mechanism Uncertainty Quantification In Ground Motion Simulations Of Le Teil Earthquake
Venue: arXiv cs.CE 2025
Year: 2025

Ensuring the seismic safety of nuclear power plants (NPPs) is essential, especially for facilities that rely on base isolation to reduce earthquake impacts. For understanding the seismic response, accurate models are key to predict the ground motions, which are generally sensitive to various factors, including earthquake source parameters like the focal mechanism, i.e., strike, dip, and rake angles. This study examines how uncertainties in these parameters affect ground motion predictions. The analysis is based on the SMATCH benchmark, which provides a standardized approach for evaluating the seismic response of the Cruas-Meysse NPP in France during the Mw 4.9 Le-Teil earthquake of 2019. A set of 27 3D high-fidelity numerical simulations was performed using a spectral-element method, each incorporating different focal mechanism variations. These simulations provide an effective approach for investigating the factors behind the exceptional ground motion observed during this event. To quantify uncertainty, the simulated ground motions were compared to recorded data using two well-established goodness-of-fit criteria: one assessing time-frequency domain characteristics and another focusing on the characterization of the ground motion signals by intensity measures. Results highlight the significant influence of focal mechanism variability on ground motion predictions, especially on the rake angle, which showed the strongest correlation with wave and intensity measures.