OpenRaiser/Intern-Atlas huggingface dataset:
Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform
Venue: arXiv cs.CV 2025
Year: 2025

Background: There are many challenges and opportunities in the clinical deployment of AI tools in radiology. The current study describes a radiology software platform called NeoMedSys that can enable efficient deployment and refinements of AI models. We evaluated the feasibility and effectiveness of running NeoMedSys for three months in real-world clinical settings and focused on improvement performance of an in-house developed AI model (VIOLA-AI) designed for intracranial hemorrhage (ICH) detection. Methods: NeoMedSys integrates tools for deploying, testing, and optimizing AI models with a web-based medical image viewer, annotation system, and hospital-wide radiology information systems. A prospective pragmatic investigation was deployed using clinical cases of patients presenting to the largest Emergency Department in Norway (site-1) with suspected traumatic brain injury (TBI) or patients with suspected stroke (site-2). We assessed ICH classification performance as VIOLA-AI encountered new data and underwent pre-planned model retraining. Performance metrics included sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Results: NeoMedSys facilitated iterative improvements in the AI model, significantly enhancing its diagnostic accuracy. Automated bleed detection and segmentation were reviewed in near real-time to facilitate re-training VIOLA-AI. The iterative refinement process yielded a marked improvement in classification sensitivity, rising to 90.3% (from 79.2%), and specificity that reached 89.3% (from 80.7%). The bleed detection ROC analysis for the entire sample demonstrated a high area-under-the-curve (AUC) of 0.949 (from 0.873). Model refinement stages were associated with notable gains, highlighting the value of real-time radiologist feedback.
Instant AoI Optimization through Relay Location Selection in Disaster Multi-hop Communication
Venue: arXiv cs.NI 2025
Year: 2025

Meteorological disasters such as typhoons, forest fires, and floods can damage the communication infrastructures, which will further disable the communication capabilities of cellular networks. The multi-hop wireless communication based on IoT devices (e.g., rescue robots, UAVs, and mobile devices) becomes an available and rapidly deployable communication approach for search and rescue operations. However, Age of Information (AoI), an emerging network performance metric, has not been comprehensively investigated in this multi-hop model. In this paper, we first construct a UAV-relayed wireless network model and formulate the end-to-end instant AoI. Then we derive the optimal location of the relay UAV to achieve the minimum instant AoI by mathematical analysis. Simulations show that the derived relay location can always guarantee the optimal AoI and outperform other schemes.
Mitigating Configuration Differences Between Development and Production Environments: A Catalog of Strategies
Venue: arXiv cs.SE 2025
Year: 2025

Context: The Configuration Management of the development and production environments is an important aspect of IT operations. However, managing the configuration differences between these two environments can be challenging, leading to inconsistent behavior, unexpected errors, and increased downtime. Objective: In this study, we sought to investigate the strategies software companies employ to mitigate the configuration differences between the development and production environments. Our goal is to provide a comprehensive understanding of these strategies used to contribute to reducing the risk of configuration-related issues. Method: To achieve this goal, we interviewed 17 participants and leveraged the Thematic Analysis methodology to analyze the interview data. These participants shed some light on the current practices, processes, challenges, or issues they have encountered. Results: Based on the interviews, we systematically formulated and structured a catalog of eight strategies that explain how software producing companies mitigate these configuration differences. These strategies vary from 1) creating detailed configuration management plans, 2) using automation tools, and 3) developing processes to test and validate changes through containers and virtualization technologies. Conclusion: By implementing these strategies, companies can improve their ability to respond quickly and effectively to changes in the production environment. In addition, they can also ensure compliance with industry standards and regulations.
Wormhole Detection Based on Z-Score And Neighbor Table Comparison
Venue: arXiv cs.NI 2025
Year: 2025

Wormhole attacks can cause serious disruptions to the network topology in disaster rescue opportunity networks. By establishing false Wormhole(WH) links, malicious nodes can mislead legitimate paths in the network, further causing serious consequences such as traffic analysis attacks (i.e., by eavesdropping and monitoring exchanged traffic), denial of service (DoS) or selective packet loss attacks. This paper uses rescue equipment (vehicle-mounted base stations, rescue control centers, etc.) as an effective third-party auditor (TPA), and combines the commonly used Z-Score (Standard Score) data processing method to propose a new detection method based on pure mathematical statistics for detecting wormhole attacks. Finally, we perform a large number of simulations to evaluate the proposed method. Since our proposed strategy does not require auxiliary equipment such as GPS positioning and timers, as a pure data statistical analysis method, it is obviously more economically valuable, feasible, and practical than other strategies in disaster relief.
Linear Search with Probabilistic Detection and Variable Speeds
Venue: arXiv cs.DM 2025
Year: 2025

We present results on new variants of the famous linear search (or cow-path) problem that involves an agent searching for a target with unknown position on the infinite line. We consider the variant where the agent can move either at speed $1$ or at a slower speed $v \in [0, 1)$. When traveling at the slower speed $v$, the agent is guaranteed to detect the target upon passing through its location. When traveling at speed $1$, however, the agent, upon passing through the target's location, detects it with probability $p \in [0, 1]$. We present algorithms and provide upper bounds for the competitive ratios for three cases separately: when $p=0$, $v=0$, and when $p,v \in (0,1)$. We also prove that the provided algorithm for the $p=0$ case is optimal.
Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
Venue: arXiv cs.RO 2025
Year: 2025

We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In image generation, the target is high-dimensional. By contrast, in action generation, the dimensionality of the target is comparatively small, and only the image condition is high-dimensional. Our approach, \emph{Mini Diffuser}, exploits this asymmetry by introducing \emph{two-level minibatching}, which pairs multiple noised action samples with each vision-language condition, instead of the conventional one-to-one sampling strategy. To support this batching scheme, we introduce architectural adaptations to the diffusion transformer that prevent information leakage across samples while maintaining full conditioning access. In RLBench simulations, Mini-Diffuser achieves 95\% of the performance of state-of-the-art multi-task diffusion policies, while using only 5\% of the training time and 7\% of the memory. Real-world experiments further validate that Mini-Diffuser preserves the key strengths of diffusion-based policies, including the ability to model multimodal action distributions and produce behavior conditioned on diverse perceptual inputs. Code available at mini-diffuse-actor.github.io
ARM SVE Unleashed: Performance and Insights Across HPC Applications on Nvidia Grace
Venue: arXiv cs.DC 2025
Year: 2025

Vector architectures are essential for boosting computing throughput. ARM provides SVE as the next-generation length-agnostic vector extension beyond traditional fixed-length SIMD. This work provides a first study of the maturity and readiness of exploiting ARM and SVE in HPC. Using selected performance hardware events on the ARM Grace processor and analytical models, we derive new metrics to quantify the effectiveness of exploiting SVE vectorization to reduce executed instructions and improve performance speedup. We further propose an adapted roofline model that combines vector length and data elements to identify potential performance bottlenecks. Finally, we propose a decision tree for classifying the SVE-boosted performance in applications.
aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents aUToPath, a unified online framework for global path-planning and control to address the challenge of autonomous navigation in cluttered urban environments. A key component of our framework is a novel hybrid planner that combines pre-computed lattice maps with dynamic free-space sampling to efficiently generate optimal driveable corridors in cluttered scenarios. Our system also features sequential convex programming (SCP)-based model predictive control (MPC) to refine the corridors into smooth, dynamically consistent trajectories. A single optimization problem is used to both generate a trajectory and its corresponding control commands; this addresses limitations of decoupled approaches by guaranteeing a safe and feasible path. Simulation results of the novel planner on randomly generated obstacle-rich scenarios demonstrate the success rate of a free-space Adaptively Informed Trees* (AIT*)-based planner, and runtimes comparable to a lattice-based planner. Real-world experiments of the full system on a Chevrolet Bolt EUV further validate performance in dense obstacle fields, demonstrating no violations of traffic, kinematic, or vehicle constraints, and a 100% success rate across eight trials.
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection
Venue: arXiv cs.LG 2025
Year: 2025

Loss of plasticity in deep neural networks is the gradual reduction in a model's capacity to incrementally learn and has been identified as a key obstacle to learning in non-stationary problem settings. Recent work has shown that deep linear networks tend to be resilient towards loss of plasticity. Motivated by this observation, we propose Adaptive Linearization (AdaLin), a general approach that dynamically adapts each neuron's activation function to mitigate plasticity loss. Unlike prior methods that rely on regularization or periodic resets, AdaLin equips every neuron with a learnable parameter and a gating mechanism that injects linearity into the activation function based on its gradient flow. This adaptive modulation ensures sufficient gradient signal and sustains continual learning without introducing additional hyperparameters or requiring explicit task boundaries. When used with conventional activation functions like ReLU, Tanh, and GeLU, we demonstrate that AdaLin can significantly improve performance on standard benchmarks, including Random Label and Permuted MNIST, Random Label and Shuffled CIFAR-10, and Class-Split CIFAR-100. Furthermore, its efficacy is shown in more complex scenarios, such as class-incremental learning on CIFAR-100 with a ResNet-18 backbone, and in mitigating plasticity loss in off-policy reinforcement learning agents. We perform a systematic set of ablations that show that neuron-level adaptation is crucial for good performance and analyze a number of metrics in the network that might be correlated to loss of plasticity.
Radon Exposure Dataset
Venue: arXiv cs.CE 2025
Year: 2025

Exposure to elevated radon levels in the home is one of the leading causes of lung cancer in the world. The following study describes the creation of a comprehensive, state-level dataset designed to enable the modeling and prediction of household radon concentrations at Zip Code Tabulation Area (ZCTA) and sub-kilometer scales. Details include the data collection and processing involved in compiling physical and demographic factors for Pennsylvania and Utah. Attempting to mitigate this risk requires identifying the underlying geological causes and the populations that might be at risk. This work focuses on identifying at-risk populations throughout Pennsylvania and Utah, where radon levels are some of the highest in the country. The resulting dataset harmonizes geological and demographic factors from various sources and spatial resolutions, including temperature, geochemistry, and soil characteristics. Demographic variables such as the household heating fuel used, the age of building, and the housing type provide further insight into which populations could be most susceptible in areas with potentially high radon levels. This dataset also serves as a foundational resource for two other studies conducted by the authors. The resolution of the data provides a novel approach to predicting potential radon exposure, and the data processing conducted for these states can be scaled up to larger spatial resolutions (e.g., the Contiguous United States [CONUS]) and allow for a broad reclassification of radon exposure potential in the United States.
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
Venue: arXiv cs.CV 2025
Year: 2025

In imaging inverse problems, we would like to know how close the recovered image is to the true image in terms of full-reference image quality (FRIQ) metrics like PSNR, SSIM, LPIPS, etc. This is especially important in safety-critical applications like medical imaging, where knowing that, say, the SSIM was poor could potentially avoid a costly misdiagnosis. But since we don't know the true image, computing FRIQ is non-trivial. In this work, we combine conformal prediction with approximate posterior sampling to construct bounds on FRIQ that are guaranteed to hold up to a user-specified error probability. We demonstrate our approach on image denoising and accelerated magnetic resonance imaging (MRI) problems. Code is available at https://github.com/jwen307/quality_uq.
The Niche Connectivity Paradox: Multichrome Contagions Overcome Vaccine Hesitancy more effectively than Monochromacy
Venue: arXiv cs.SI 2025
Year: 2025

The rise of vaccine hesitancy has caused a resurgence of vaccine-preventable diseases such as measles and pertussis, alongside widespread skepticism and refusals of COVID-19 vaccinations. While categorizing individuals as either supportive of or opposed to vaccines provides a convenient dichotomy of vaccine attitudes, vaccine hesitancy is far more complex and dynamic. It involves wavering individuals whose attitudes fluctuate -- those who may exhibit pro-vaccine attitudes at one time and anti-vaccine attitudes at another. Here, we identify and analyze multichrome contagions as potential targets for intervention by leveraging a dataset of known pro-vax and anti-vax Twitter users ($n =135$ million) and a large COVID-19 Twitter dataset ($n = 3.5$ billion; including close analysis of $1,563,472$ unique individuals). We reconstruct an evolving multiplex sentiment landscape using top co-spreading issues, characterizing them as monochrome and multichrome contagions, based on their conceptual overlap with vaccination. We demonstrate switchers as deliberative: they are more moderate, engage with a wider range of topics, and occupy more central positions in their networks. Further examination of their information consumption shows that their discourse often engages with progressive issues such as climate change, which can serve as avenues for multichrome contagion interventions to promote pro-vaccine attitudes. Using data-driven intervention simulations, we demonstrate a paradox of niche connectivity, where multichrome contagions with fragmented, non-overlapping communities generate the highest levels of diffusion for pro-vaccine attitudes. Our work offers insights into harnessing synergistic hitchhiking effect of multichrome contagions to drive desired attitude and behavior changes in network-based interventions, particularly for overcoming vaccine hesitancy.
On Unbiased Low-Rank Approximation with Minimum Distortion
Venue: arXiv cs.DS 2025
Year: 2025

We describe an algorithm for sampling a low-rank random matrix $Q$ that best approximates a fixed target matrix $P\in\mathbb{C}^{n\times m}$ in the following sense: $Q$ is unbiased, i.e., $\mathbb{E}[Q] = P$; $\mathsf{rank}(Q)\leq r$; and $Q$ minimizes the expected Frobenius norm error $\mathbb{E}\|P-Q\|_F^2$. Our algorithm mirrors the solution to the efficient unbiased sparsification problem for vectors, except applied to the singular components of the matrix $P$. Optimality is proven by showing that our algorithm matches the error from an existing lower bound.
Next Word Suggestion using Graph Neural Network
Venue: arXiv cs.CL 2025
Year: 2025

Language Modeling is a prevalent task in Natural Language Processing. The currently existing most recent and most successful language models often tend to build a massive model with billions of parameters, feed in a tremendous amount of text data, and train with enormous computation resources which require millions of dollars. In this project, we aim to address an important sub-task in language modeling, i.e., context embedding. We propose an approach to exploit the Graph Convolution operation in GNNs to encode the context and use it in coalition with LSTMs to predict the next word given a local context of preceding words. We test this on the custom Wikipedia text corpus using a very limited amount of resources and show that this approach works fairly well to predict the next word.
LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Spiking Large Language Models (LLMs) have emerged as an energy-efficient alternative to conventional LLMs through their event-driven computation. To effectively obtain spiking LLMs, researchers develop different ANN-to-SNN conversion methods by leveraging pre-trained ANN parameters while inheriting the energy efficiency of SNN. However, existing conversion methods struggle with extreme activation outliers and incompatible nonlinear operations of ANN-based LLMs. To address this, we propose a loss-less ANN-SNN conversion for fully spike-driven LLMs, termed LAS. Specifically, LAS introduces two novel neurons to convert the activation outlier and nonlinear operation of ANN-based LLMs. Moreover, LAS tailors the spike-equivalent Transformer components for spiking LLMs, which can ensure full spiking conversion without any loss of performance. Experimental results on six language models and two vision-language models demonstrate that LAS achieves loss-less conversion. Notably, on OPT-66B, LAS even improves the accuracy of 2\% on the WSC task. In addition, the parameter and ablation studies further verify the effectiveness of LAS. The source code is available at https://github.com/lc783/LAS
Beyond Pairwise Learning-To-Rank At Airbnb
Venue: arXiv cs.IR 2025
Year: 2025

There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch-no algorithm can achieve all three at the same time. We call this limitation the SAT theorem for ranking algorithms. Given the dilemma, how can we design a practical system that meets user needs? Our current work at Airbnb provides an answer, with a working solution deployed at scale. We start with pairwise learning-to-rank (LTR) models-the bedrock of search ranking tech stacks today. They scale linearly with the number of items ranked and perform strongly on metrics like NDCG by learning from pairwise comparisons. They are at a sweet spot of performance vs. cost, making them an ideal choice for several industrial applications. However, they have a drawback-by ignoring interactions between items, they compromise on accuracy. To improve accuracy, we create a "true" pairwise LTR model-one that captures interactions between items during pairwise comparisons. But accuracy comes at the expense of scalability and total order, and we discuss strategies to counter these challenges. For greater accuracy, we take each item in the search result, and compare it against the rest of the items along two dimensions: (1) Superiority: How strongly do searchers prefer the given item over the remaining ones? (2) Similarity: How similar is the given item to all the other items? This forms the basis of our "all-pairwise" LTR framework, which factors in interactions across all items at once. Looking at items on the search result page all together-superiority and similarity combined-gives us a deeper understanding of what searchers truly want. We quantify the resulting improvements in searcher experience through offline and online experiments at Airbnb.
Incidents During Microservice Decomposition: A Case Study
Venue: arXiv cs.SE 2025
Year: 2025

Software errors and incidents are inevitable in web based applications. Scalability challenges, increasing demand, and ongoing code changes can contribute to such failures. As software architectures evolve rapidly, understanding how and why incidents occur is crucial for enhancing system reliability. In this study, we introduce Carbon Health's software stack, share our microservices journey, and analyze 107 incidents. Based on these incidents, we share insights and lessons learned on microservice decomposition. Finally, we suggest that starting with monolithic modularization as an initial step toward microservice decomposition may help reduce incidents and contribute to building more resilient software.
Learning Kronecker-Structured Graphs from Smooth Signals
Venue: arXiv cs.LG 2025
Year: 2025

Graph learning, or network inference, is a prominent problem in graph signal processing (GSP). GSP generalizes the Fourier transform to non-Euclidean domains, and graph learning is pivotal to applying GSP when these domains are unknown. With the recent prevalence of multi-way data, there has been growing interest in product graphs that naturally factorize dependencies across different ways. However, the types of graph products that can be learned are still limited for modeling diverse dependency structures. In this paper, we study the problem of learning a Kronecker-structured product graph from smooth signals. Unlike the more commonly used Cartesian product, the Kronecker product models dependencies in a more intricate, non-separable way, but posits harder constraints on the graph learning problem. To tackle this non-convex problem, we propose an alternating scheme to optimize each factor graph and provide theoretical guarantees for its asymptotic convergence. The proposed algorithm is also modified to learn factor graphs of the strong product. We conduct experiments on synthetic and real-world graphs and demonstrate our approach's efficacy and superior performance compared to existing methods.