Tootfinder

Maximally Useful and Minimally Redundant: The Key to Self Supervised Learning for Imbalanced Data
Yash Kumar Sharma, Vineet Nair, Wilson Naik
https://arxiv.org/abs/2509.08469 ht…

Maximally Useful and Minimally Redundant: The Key to Self Supervised Learning for Imbalanced Data
The robustness of contrastive self-supervised learning (CSSL) for imbalanced datasets is largely unexplored. CSSL usually makes use of \emph{multi-view} assumptions to learn discriminatory features via similar and dissimilar data samples. CSSL works well on balanced datasets, but does not generalize well for imbalanced datasets. In a very recent paper, as part of future work, Yann LeCun pointed out that the self-supervised multiview framework can be extended to cases involving \emph{more than t…

Boom Aerospace, which is building a supersonic plane, announces Superpower, a 42MW natural gas turbine using Boom's supersonic engine, to power AI data centers (Boom)
https://boomsupersonic.com/flyby/ai-needs-more-power-than-the…

Boom - FlyBy - AI Needs More Power Than the Grid Can Deliver. Supersonic Tech Can Fix That.
Today, we’re announcing Superpower, our new 42 MW natural gas turbine, along with a $300M funding round and Crusoe as our launch customer.

Houston suburb says some online services taken down by cyberattack https://therecord.media/houston-suburb-cyberattack-services

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Fam\'a, Roberto Pereira, Charalampos Kalalas, Paolo Dini, Lorena Qendro, Fahim Kawsar, Mohammad Malekzadeh
https://arxiv.org/abs/2510.08374

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored. The substantial computational demands required for training conventional CL frameworks pose a set of challenges, particularly in terms of energy consumption, data availability, and memory usage. We conduct an evaluation of four widely used CL frameworks: SimCLR, MoCo, SimSiam, and Barlow Twins. We focus on the practi…

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras
https://arxiv.org/abs/2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data…

A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG
Emilio Estevan, Mar\'ia Sierra-Torralba, Eduardo L\'opez-Larraz, Luis Montesano
https://arxiv.org/abs/2510.07960

A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG
Wearable EEG devices have emerged as a promising alternative to polysomnography (PSG). As affordable and scalable solutions, their widespread adoption results in the collection of massive volumes of unlabeled data that cannot be analyzed by clinicians at scale. Meanwhile, the recent success of deep learning for sleep scoring has relied on large annotated datasets. Self-supervised learning (SSL) offers an opportunity to bridge this gap, leveraging unlabeled signals to address label scarcity and …

On a class of (non)local superposition operators of arbitrary order
Serena Dipierro, Sven Jarohs, Enrico Valdinoci
https://arxiv.org/abs/2510.08345 https://

On a class of (non)local superposition operators of arbitrary order
In this paper we introduce a very general setting dealing with the superposition of operators of any positive order and provide a systematic study of them. We also provide examples and counterexamples, as well as characterizing properties of the measures and the functional spaces under consideration. Moreover, we present some applications regarding the existence theory for a class of nonlinear problems involving superposition operators of arbitrary (possibly fractional) order.

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Yuelin Guo, Haoyu He, Zhiyuan Chen, Zitong Huang, Renhao Lu, Lu Shi, Zejun Wang, Weizhe Zhang
https://arxiv.org/abs/2509.08289

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) has attracted significant attention in recent years, as it does not require box-level annotations. State-of-the-art methods generally adopt a multi-module network, which employs WSDDN as the multiple instance detection network module and multiple instance refinement modules to refine performance. However, these approaches suffer from three key limitations. First, existing methods tend to generate pseudo GT boxes that either focus only on discriminative …

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers
https://arxiv.org/abs/2509.08628

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source…

EVDI : Event-based Video Deblurring and Interpolation via Self-Supervised Learning
Chi Zhang, Xiang Zhang, Chenxu Jiang, Gui-Song Xia, Lei Yu
https://arxiv.org/abs/2509.08260 h…

EVDI++: Event-based Video Deblurring and Interpolation via Self-Supervised Learning
Frame-based cameras with extended exposure times often produce perceptible visual blurring and information loss between frames, significantly degrading video quality. To address this challenge, we introduce EVDI++, a unified self-supervised framework for Event-based Video Deblurring and Interpolation that leverages the high temporal resolution of event cameras to mitigate motion blur and enable intermediate frame prediction. Specifically, the Learnable Double Integral (LDI) network is designed …