Tootfinder

Apple unveils an updated Spotlight in macOS Tahoe, letting users quickly take actions in apps from playing music to creating notes and files, similar to Raycast (David Pierce/The Verge)
https://www.theverge.com/apple/682950/apple-spotlight-mac-shortcuts

Apple’s Spotlight upgrades look like a power-user dream
At WWDC 2025, Apple announced a big update to Spotlight that makes it a far more powerful system for launching and doing things on your Mac.

Hawking Radiation Signatures from Primordial Black Holes Transiting the Inner Solar System: Prospects for Detection
Alexandra P. Klipfel, Peter Fisher, David I. Kaiser
https://arxiv.org/abs/2506.14041

Hawking Radiation Signatures from Primordial Black Holes Transiting the Inner Solar System: Prospects for Detection
Primordial black holes (PBHs) arise from the collapse of density perturbations in the early universe and serve as a dark matter (DM) candidate and a probe of fundamental physics. There remains an unconstrained ``asteroid-mass'' window where PBHs of masses $10^{17} {\rm g} \lesssim M \lesssim 10^{23} {\rm g}$ could comprise up to $100\%$ of the dark matter. Current $e^{\pm}$ Hawking radiation constraints on the DM fraction of PBHs are set by comparing observed spatial- and time-integrated cosmic…

Beyond Decoherence: Control the Collective Quantum Dynamics of Quasi Particles in Topological Interface
Fatemeh Davoodi
https://arxiv.org/abs/2506.12805 ht…

Beyond Decoherence: Control the Collective Quantum Dynamics of Quasi Particles in Topological Interface
Long lived coherent quasiparticles are a promising foundation for novel quantum technologies, where maintaining quantum coherence is crucial. Decoherence, driven by finite emitter lifetimes, remains a central challenge in quantum computing. Here, we control the dynamics of spatially separated quantum emitters via preserving their phase information by introducing a topological waveguide as a robust chiral reservoir. Incoherent quantum emitters randomly positioned near a perturbed honeycomb plasm…

Implied Probabilities and Volatility in Credit Risk: A Merton-Based Approach with Binomial Trees
Jagdish Gnawali, Abootaleb Shirvani, Svetlozar T. Rachev
https://arxiv.org/abs/2506.12694

Implied Probabilities and Volatility in Credit Risk: A Merton-Based Approach with Binomial Trees
We explore credit risk pricing by modeling equity as a call option and debt as the difference between the firm's asset value and a put option, following the structural framework of the Merton model. Our approach proceeds in two stages: first, we calibrate the asset volatility using the Black-Scholes-Merton (BSM) formula; second, we recover implied mean return and probability surfaces under the physical measure. To achieve this, we construct a recombining binomial tree under the real-world (natu…

Boundary-Informed Sound Field Reconstruction
David Sundstr\"om, Filip Elvander, Andreas Jakobsson
https://arxiv.org/abs/2506.13279 https://

Boundary-Informed Sound Field Reconstruction
We consider the problem of reconstructing the sound field in a room using prior information of the boundary geometry, represented as a point cloud. In general, when no boundary information is available, an accurate sound field reconstruction over a large spatial region and at high frequencies requires numerous microphone measurements. On the other hand, if all geometrical and acoustical aspects of the boundaries are known, the sound field could, in theory, be simulated without any measurements.…

MEMS Switch Enabled Spatiotemporally Modulated Isolators
Connor Devitt, Yong-bok Lee, Pavitra Jain, Sunil A. Bhave, Xu Zhu, Nicholas Yost, Yabei Gu
https://arxiv.org/abs/2506.11861

MEMS Switch Enabled Spatiotemporally Modulated Isolators
This work reports the simulation, design, and implementation of a compact MEMS switch based spatiotemporally modulated (STM) bandpass filtering isolator to improve self-interference cancellation (SIC) in underwater acoustic communication networks. Conventional ferrite circulators are unavailable in ultrasonic frequency ranges limiting SIC to techniques such as spatial cancellation and adaptive digital cancellation. This study details a sub-megahertz electronic non-magnetic filtering isolator. H…

Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization
Jonathan Yang, Chuyuan Kelly Fu, Dhruv Shah, Dorsa Sadigh, Fei Xia, Tingnan Zhang
https://arxiv.org/abs/2506.06196

Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization
In this work, we investigate how spatially grounded auxiliary representations can provide both broad, high-level grounding as well as direct, actionable information to improve policy learning performance and generalization for dexterous tasks. We study these mid-level representations across three critical dimensions: object-centricity, pose-awareness, and depth-awareness. We use these interpretable mid-level representations to train specialist encoders via supervised learning, then feed them as…

“The String… will be much faster”
my brain is breaking https://mastodon.social/@Catfish_Man/114655168110161214

This https://arxiv.org/abs/2506.01933 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models
Spatial intelligence, encompassing 3D reconstruction, perception, and reasoning, is fundamental to applications such as robotics, aerial imaging, and extended reality. A key enabler is the real-time, accurate estimation of core 3D attributes (camera parameters, point clouds, depth maps, and 3D point tracks) from unstructured or streaming imagery. Inspired by the success of large foundation models in language and 2D vision, a new class of end-to-end 3D geometric foundation models (GFMs) has emer…

Improving Long-Range Navigation with Spatially-Enhanced Recurrent Memory via End-to-End Reinforcement Learning
Fan Yang, Per Frivik, David Hoeller, Chen Wang, Cesar Cadena, Marco Hutter
https://arxiv.org/abs/2506.05997

Improving Long-Range Navigation with Spatially-Enhanced Recurrent Memory via End-to-End Reinforcement Learning
Recent advancements in robot navigation, especially with end-to-end learning approaches like reinforcement learning (RL), have shown remarkable efficiency and effectiveness. Yet, successful navigation still relies on two key capabilities: mapping and planning, whether explicit or implicit. Classical approaches use explicit mapping pipelines to register ego-centric observations into a coherent map frame for the planner. In contrast, end-to-end learning achieves this implicitly, often through rec…