Tootfinder

AI is coming for entry-level jobs. Everybody needs to get ready.
CEOs are saying that AI is coming for a lot of jobs, and soon
— perhaps as many as half of all white-collar workers.
That’s likely to show up first in entry-level jobs, where the basic skills required are the easiest to replicate,
and in tech, where the ability to rapidly adapt the latest software tools is itself an entry-level job requirement.
Sure enough, in recent years unemployment has risen…

AI is coming for entry-level jobs. Everybody needs to get ready.
AI may replace a lot of entry-level skills. So how will workers acquire more advanced ones?

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Joon Tai Kim, Tianle Chen, Ziyu Dong, Nishanth Kunchala, Alexander Guller, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
https://arxiv.org/abs/2507.06321

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire…

From NLVO to NAO: Reactive Robot Navigation using Velocity and Acceleration Obstacles
Asher Stern, Zvi Shiller
https://arxiv.org/abs/2506.06255 https://

From NLVO to NAO: Reactive Robot Navigation using Velocity and Acceleration Obstacles
This paper introduces a novel approach for robot navigation in challenging dynamic environments. The proposed method builds upon the concept of Velocity Obstacles (VO) that was later extended to Nonlinear Velocity Obstacles (NLVO) to account for obstacles moving along nonlinear trajectories. The NLVO is extended in this paper to Acceleration Obstacles (AO) and Nonlinear Acceleration Obstacles (NAO) that account for velocity and acceleration constraints. Multi-robot navigation is achieved by usi…

Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Workloads on Kubernetes
Guilin Zhang, Srinivas Vippagunta, Raghavendra Nandagopal, Suchitra Raman, Jeff Xu, Marcus Pfeiffer, Shree Chatterjee, Ziqi Tan, Wulan Guo, Hailong Jiang
https://arxiv.org/abs/2507.05653

Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Workloads on Kubernetes
High-performance extreme computing (HPEC) platforms increasingly adopt serverless paradigms, yet face challenges in efficiently managing highly dynamic workloads while maintaining service-level objectives (SLOs). We propose **AAPA**, an archetype-aware predictive autoscaling system that leverages weak supervision to automatically classify 300\,000\,+ workload windows into four archetypes (PERIODIC, SPIKE, RAMP, STATIONARY\_NOISY) with 99.8\% accuracy. Evaluation on publicly available Azure Func…

»Tim Berners-Lee wird 70 – der Architekt des World Wide Webs:
Der Mann, der uns das WWW schenkte, feiert 70. Geburtstag und kämpft weiter für ein freies Netz. Ein Blick auf sein technisches Erbe und seine aktuellen Pläne.«
Das WWW ist einiges mehr als nur die populären Social-Media's, die deren Nutzer*innen in ihrem virtuellen Garten festhalten. Ein freies Netz (ausser Propaganda) ist u.a. auch die Freiheit der Menschen.
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Missing Link: Tim Berners-Lee wird 70 – der Architekt des World Wide Webs
Der Mann, der uns das WWW schenkte, feiert 70. Geburtstag und kämpft weiter für ein freies Netz. Ein Blick auf sein technisches Erbe und seine aktuellen Pläne.

ASKAP-EMU radio continuum detection of planetary nebula NGC 5189: the "Infinity" nebula
A. D. Asher, Z. J. Smeaton, M. D. Filipovi\'c, A. M. Hopkins, J. Th. van Loon, T. J. Galvin, L. A. Barnes
https://arxiv.org/abs/2506.06768

ASKAP-EMU radio continuum detection of planetary nebula NGC 5189: the "Infinity" nebula
We report the radio continuum detection of well known Galactic Planetary Nebula (PN) NGC 5189, observed at 943 MHz during the Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) survey. Two detections of NGC 5189 have been made during the survey, of better resolution than previous radio surveys. Both measurements of the integrated flux density are consistent with each other, at $S_{\rm 943\,MHz} = 0.33\pm0.03$ Jy, and the spectral luminosity is $L_{\rm{94…

Algorithmic Analysis of GTFS-RT vehicle position accuracy
Joshua Wong
https://arxiv.org/abs/2506.06479 https://arxiv.org/pdf/2506.064…

Algorithmic Analysis of GTFS-RT vehicle position accuracy
This paper presents three novel algorithms for calculating geodesic intersections on an ellipsoid. These algorithms are applied in a case study analyzing real-time transit data in California to assess vehicle position drift. The analysis reveals that while certain data anomalies can be corrected, large-scale discrepancies persist. The paper concludes by proposing a set of practical solutions that can be implemented by either data producers or consumers to significantly improve positional accura…

AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition
Chen Bao, Chuanbing Huo, Qinyu Chen, Chang Gao
https://arxiv.org/abs/2506.06566

AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition
This paper proposes AS-ASR, a lightweight aphasia-specific speech recognition framework based on Whisper-tiny, tailored for low-resource deployment on edge devices. Our approach introduces a hybrid training strategy that systematically combines standard and aphasic speech at varying ratios, enabling robust generalization, and a GPT-4-based reference enhancement method that refines noisy aphasic transcripts, improving supervision quality. We conduct extensive experiments across multiple data mix…

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Yangui Fang, Jing Peng, Xu Li, Yu Xi, Chengwei Zhang, Guohui Zhong, Kai Yu
https://arxiv.org/abs/2506.05671

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Recent advances in automatic speech recognition (ASR) have combined speech encoders with large language models (LLMs) through projection, forming Speech LLMs with strong performance. However, adapting them to new domains remains challenging, especially in low-resource settings where paired speech-text data is scarce. We propose a text-only fine-tuning strategy for Speech LLMs using unpaired target-domain text without requiring additional audio. To preserve speech-text alignment, we introduce a …

ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
He Wang, Linhan Ma, Dake Guo, Xiong Wang, Lei Xie, Jin Xu, Junyang Lin
https://arxiv.org/abs/2507.05727

ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
Automatic Speech Recognition (ASR) has been extensively investigated, yet prior evaluative efforts have largely been restricted to contextless paradigms. This constraint stems from the limited proficiency of conventional ASR models in context modeling and their deficiency in memory and reasoning based on world knowledge. Recent breakthroughs in the development of Large Language Models (LLMs) and corresponding Large Audio Language Models (LALMs) have markedly enhanced the visibility of general a…