Over the past years, embedding learning on networks has shown tremendous results in link prediction tasks for complex systems, with a wide range of real-life applications. Learning a representation for each node in a knowledge graph allows us to capture topological and semantic information, which can be processed in downstream analyses later. In the link prediction task, high-dimensional network information is encoded into low-dimensional vectors, which are then fed to a predictor to infer new connections between nodes […]

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It’s been a week an just when I thought nothing could get worse than ROS ecosystem, I had to dive into whatever the f Nvidia has come up with. If you can install it, that’s 50 credits already, Want to understand how physics works in PhysX? Well too bad, the editor has it’s own physics naming convention coming from USD Physics API. Wait why would 3D scene convention have physics schema ? god knows. and why would Nvidia’s […]

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arXiv:2603.16889v1 Announce Type: new Abstract: Reliable and interpretable automated assessment of second-language (L2) speech remains a central challenge, as large speech-language models (SpeechLLMs) often struggle to align with the nuanced variability of human raters. To address this, we introduce a rubric-guided reasoning framework that explicitly encodes multi-aspect human assessment criteria: accuracy, fluency, and prosody, while calibrating model uncertainty to capture natural rating variability. We fine-tune the Qwen2-Audio-7B-Instruct model using multi-rater human judgments and develop an uncertainty-calibrated regression approach […]

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arXiv:2601.21522v1 Announce Type: cross Abstract: The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question correctly at least once in k trials. At a fixed budget, a more suitable metric is coverage@cost, the average number of unique questions answered as a function of the total number of attempts. We connect the two metrics and show that the empirically-observed power-law behavior in pass@k leads to a sublinear growth […]

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I’m working on an optimization problem with a complex environment. Environment is complex in inner working but has only one action input. The action can be either binary, or discrete, or continuous. If the environment is optimized on binary action the maximum reward will be less than when on discrete or continuous actions. PPO works when action is binary or discrete but not when it’s continuous. The input to the model needs to be a value between 0 […]

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arXiv:2603.22459v1 Announce Type: new Abstract: Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news headlines for reader engagement is often conflated with clickbait, resulting in exaggerated or misleading phrasing that undermines editorial trust. We frame clickbait not as a separate stylistic category, but as an extreme outcome of disproportionate amplification of otherwise legitimate engagement cues. Based on this view, we formulate headline rewriting as a controllable generation […]

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arXiv:2505.07272v2 Announce Type: replace Abstract: Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. However, some applications involve heterogeneous data that vary in quality due to noise characteristics associated with each data sample. Heteroscedastic methods aim to deal with such mixed data quality. This paper develops a subspace learning method, named ALPCAH, that can estimate the sample-wise noise variances and use this information to improve the estimate of the subspace basis associated […]

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arXiv:2603.26724v1 Announce Type: new Abstract: The dynamic and heterogeneous nature of agricultural fields presents significant challenges for object detection and localization, particularly for autonomous mobile robots that are tasked with surveying previously unseen unstructured environments. Concurrently, there is a growing need for real-time detection systems that do not depend on large-scale manually labeled real-world datasets. In this work, we introduce a comprehensive annotation-to-detection framework designed to train a robust multi-modal detector using limited and partially labeled training data. […]

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