Tootfinder

ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo, Stefano Cavuoti, Massimo Brescia, Giuseppe Longo
https://arxiv.org/abs/2507.20365

ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by employing automatic machine and deep learning techniques. We propose an implementation of the ULISSE algorithm aimed at determining physical parameters of galaxies, in particular star-formation rates (SFR) and stellar masses ($M_{\ast}$), using only composite-color images. ULI…

CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
Md Hasibul Amin, Mohammadreza Mohammadi, Jason D. Bakos, Ramtin Zand
https://arxiv.org/abs/2505.22868 …

CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, architecture, and systems-to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS leverages the single-path one-shot weight-sharing strategy combined with the evolutionary search for the first time in the context of PIM system mapping and optimization. CrossNAS se…

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Xiang Xu, Lingdong Kong, Song Wang, Chuanwei Zhou, Qingshan Liu
https://arxiv.org/abs/2507.05260

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR r…

One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
Han Wu, Junyao Li, Kangbo Zhao, Sen Zhang, Yukai Shi, Liang Lin
https://arxiv.org/abs/2506.15312

One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
Face sketch synthesis is a technique aimed at converting face photos into sketches. Existing face sketch synthesis research mainly relies on training with numerous photo-sketch sample pairs from existing datasets. However, these large-scale discriminative learning methods will have to face problems such as data scarcity and high human labor costs. Once the training data becomes scarce, their generative performance significantly degrades. In this paper, we propose a one-shot face sketch synthesi…

Few-shot transfer of tool-use skills using human demonstrations with proximity and tactile sensing
Marina Y. Aoyama, Sethu Vijayakumar, Tetsuya Narita
https://arxiv.org/abs/2507.13200

Few-shot transfer of tool-use skills using human demonstrations with proximity and tactile sensing
Tools extend the manipulation abilities of robots, much like they do for humans. Despite human expertise in tool manipulation, teaching robots these skills faces challenges. The complexity arises from the interplay of two simultaneous points of contact: one between the robot and the tool, and another between the tool and the environment. Tactile and proximity sensors play a crucial role in identifying these complex contacts. However, learning tool manipulation using these sensors remains challe…

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
Henri A\"idasso, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2507.04173

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
One of the main challenges developers face in the use of continuous integration (CI) and deployment pipelines is the occurrence of intermittent job failures, which result from unexpected non-deterministic issues (e.g., flaky tests or infrastructure problems) rather than regular code-related errors such as bugs. Prior studies developed machine-learning (ML) models trained on large datasets of job logs to classify job failures as either intermittent or regular. As an alternative to costly manual …

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Daniel D'souza, Julia Kreutzer, Adrien Morisot, Ahmet \"Ust\"un, Sara Hooker
https://arxiv.org/abs/2506.14702

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
One of the most profound challenges of modern machine learning is performing well on the long-tail of rare and underrepresented features. Large general-purpose models are trained for many tasks, but work best on high-frequency use cases. After training, it is hard to adapt a model to perform well on specific use cases underrepresented in the training corpus. Relying on prompt engineering or few-shot examples to maximize the output quality on a particular test case can be frustrating, as models …

This https://arxiv.org/abs/2409.03685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, environments, and observational modalities remain elusive. In this work, we investigate how knowledge from large-scale visual data of the world may be used to address one axis of variation for generalizable manipulation: observational viewpoint. Specifically, we study single-image novel view synthesis models, which learn …

PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
Junyu Liu, R. Kenny Jones, Daniel Ritchie
https://arxiv.org/abs/2506.03004

PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between…

This https://arxiv.org/abs/2505.18780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion
Humanoid locomotion faces a critical scalability challenge: traditional reinforcement learning (RL) methods require task-specific rewards and struggle to leverage growing datasets, even as more training terrains are introduced. We propose DreamPolicy, a unified framework that enables a single policy to master diverse terrains and generalize zero-shot to unseen scenarios by systematically integrating offline data and diffusion-driven motion synthesis. At its core, DreamPolicy introduces Humanoid…