Tootfinder

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

Spike in steel tariffs could imperil Trump promise of lower grocery prices | Financial Post
https://financialpost.com/pmn/spike-in-steel-tariffs-could-imperil-trump-promise-of-lower-grocery-prices

Quantum Acoustics with Tunable Nonlinearity in the Superstrong Coupling Regime
Marco Scigliuzzo, L\'eo Peyruchat, Riccardo Maria Marabini, Carla Becker, Vincent Jouanny, Per Delsing, Pasquale Scarlino
https://arxiv.org/abs/2505.24865

Quantum Acoustics with Tunable Nonlinearity in the Superstrong Coupling Regime
Precise control of mechanical modes in the quantum regime is a key resource for quantum technologies, offering promising pathways for quantum sensing with macroscopic systems and scalable architectures for quantum simulation. In this work, we realise a multimode mechanical cavity coupled to a superconducting Kerr resonator, which induces nonlinearity in the mechanical modes. The Kerr mode is realised by a flux-tunable SQUID array resonator, while the mechanical modes are implemented by a surfac…

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan, Xiang Li, Yixuan Yuan
https://arxiv.org/abs/2505.24687

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks that fail to capture the gradual transitions characteristic of tumor boundaries. We present TumorGen, a novel Boundary-Aware Tumor-Mask Sy…

The effects of the spin and quadrupole moment of SgrA* on the orbits of S stars
K. Abd El Dayem, F. H. Vincent, G. Heissel, T. Paumard, G. Perrin
https://arxiv.org/abs/2505.24789 …

The effects of the spin and quadrupole moment of SgrA* on the orbits of S stars
Measuring the astrometric and spectroscopic data of stars orbiting the central black hole in our galaxy (Sgr A*) offers a promising way to measure relativistic effects. In principle, the "no-hair" theorem can be tested at the Galactic Center by monitoring the orbital precession of S-stars due to the angular momentum (spin) and quadrupole moment of Sgr A*. Closer-in stars, more strongly affected by the black hole's rotation, may be required. GRAVITY+ could detect such stars that are currently to…

Energy-Oriented Computing Architecture Simulator for SNN Training
Yunhao Ma (Pengcheng Laboratory, Southern University of Science and Technology), Wanyi Jia (Pengcheng Laboratory, Southern University of Science and Technology), Yanyu Lin (Pengcheng Laboratory), Wenjie Lin (Pengcheng Laboratory), Xueke Zhu (Pengcheng Laboratory), Huihui Zhou (Pengcheng Laboratory), Fengwei An (Southern University of Science and Technology)

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

This https://arxiv.org/abs/2504.07477 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Analog Computing for Signal Processing and Communications -- Part II: Toward Gigantic MIMO Beamforming
Analog-domain operations offer a promising solution to accelerating signal processing and enabling future multiple-input multiple-output (MIMO) communications with thousands of antennas. In Part I of this paper, we have introduced a microwave linear analog computer (MiLAC) as an analog computer that processes microwave signals linearly, demonstrating its potential to reduce the computational complexity of specific signal processing tasks. In Part II of this paper, we extend these benefits to wi…

Uniqueness of free 2-periodicities of links
Ken'ichi Yoshida
https://arxiv.org/abs/2505.22282 https://arxiv.org/pdf/2505.22282

Uniqueness of free 2-periodicities of links
We show that if two links in the real projective 3-space $\mathbb{RP}^{3}$ have isotopic preimages in the 3-sphere $S^{3}$ by the double covering map, then they are themselves isotopic in $\mathbb{RP}^{3}$.

Discrete and Continuous Muttalib--Borodin process: Large deviations and Riemann--Hilbert analysis
Jonathan Husson, Guido Mazzuca, Alessandra Occelli
https://arxiv.org/abs/2505.23164

Discrete and Continuous Muttalib--Borodin process: Large deviations and Riemann--Hilbert analysis
In this paper, we study the asymptotic behaviour of plane partitions distributed according to a weighted $q^{Volume}$ Muttalib--Borodin ensemble. Specifically, we establish a Large Deviation principle for the discrete Muttalib--Borodin process, characterising the rate function. Furthermore, through Riemann--Hilbert analysis, we give an explicit expression for the asymptotic shape of the partition.

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…