Tootfinder

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
https://arxiv.org/abs/2506.09496

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability prot…

Quanta Magazine's Stephen Ornes describes a novel algorithm that can factor large numbers using only one qubit. The technique relies on quantum oscillation, and although it has a catch (the larger the number being factored the more energy the oscillators expend), the work points to new ways to perform quantum computing.
"New Quantum Algorithm Factors Numbers With One Qubit
-The catch: It would require the energy of a few medium-size stars."

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Enrique Barba Roque, Luis Cruz
https://arxiv.org/abs/2506.09599 https://

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary explora…

The Emergence of Abstract Thought in Large Language Models Beyond Any Language
Yuxin Chen, Yiran Zhao, Yang Zhang, An Zhang, Kenji Kawaguchi, Shafiq Joty, Junnan Li, Tat-Seng Chua, Michael Qizhe Shieh, Wenxuan Zhang
https://arxiv.org/abs/2506.09890

The Emergence of Abstract Thought in Large Language Models Beyond Any Language
As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts. This has led to the widespread assumption that LLMs may "think" in English. However, more recent results showing strong multilingual performance, even surpassing English performance on specific tasks in other…

Optimal quantum metrology under energy constraints
Longyun Chen, Yuxiang Yang
https://arxiv.org/abs/2506.09436 https://arxiv.org/pdf/…

Optimal quantum metrology under energy constraints
Quantum metrology promises enhanced precision in parameter estimation by exploiting quantum effects. The traditional framework of metrology commonly assumes unlimited access to resources, overlooking resource constraints in realistic scenarios. As such, the optimal strategies therein are often infeasible in practice. Here, we investigate quantum metrology where the total energy consumption of the probe state preparation, intermediate control operations, and the final measurement is subject to a…

Catalog of very-high-energy emitting active galactic nuclei at high Galactic latitudes
A. Neronov, D. Semikoz
https://arxiv.org/abs/2506.08497 https://

Catalog of very-high-energy emitting active galactic nuclei at high Galactic latitudes
Large number of Active Galactic Nuclei (AGN) producing Very-High-Energy (VHE) gamma-rays (energies above 100~GeV) has been revealed using observations with Imaging Atmospheric Cherenkov Telescopes (IACTs). However, our knowledge of the VHE emitting AGN population is limited in the absence of an unbiased sky survey. We use long exposure of Fermi Large Area Telescope (LAT) to perform a survey of VHE emitting AGN at Galactic latitudes |b|>10 degrees. We consider clustering of gamma-ray e…

Distributed component-level modeling and control of energy dynamics in electric power systems
Hiya Gada, Rupamathi Jaddivada, Marija Ilic
https://arxiv.org/abs/2506.08861

Distributed component-level modeling and control of energy dynamics in electric power systems
The widespread deployment of power electronic-based technologies is transforming modern power systems into fast, nonlinear, and heterogeneous systems. Conventional modeling and control approaches, rooted in quasi-static analysis and centralized control, are inadequate for these converter-dominated systems, which operate on fast timescales and involve proprietary models of diverse components. This paper adopts and extends a previously introduced energy space modeling framework grounded in energy…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
Ryan Barker, Fatemeh Afghah
https://arxiv.org/abs/2506.09418 https://…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
The advent of Open Radio Access Networks (O-RAN) introduces modularity and flexibility into 5G deployments but also surfaces novel security challenges across disaggregated interfaces. This literature review synthesizes recent research across thirteen academic and industry sources, examining vulnerabilities such as cipher bidding-down attacks, partial encryption exposure on control/user planes, and performance trade-offs in securing O-RAN interfaces like E2 and O1. The paper surveys key cryptogr…

Ultrafast interband transitions in nanoporous gold metamaterial
Tlek Tapani, Jonas M. Pettersson, Nils Henriksson, Erik Z\"all, Nils V. Hauff, Lakshmi Das, Gianluca Balestra, Massimo Cuscun\`a, Aitor De Andr\'es, Tommaso Giovannini, Denis Garoli, Nicol\`o Maccaferri
https://arxiv.org/abs/2506.08536

Ultrafast interband transitions in nanoporous gold metamaterial
Nanoporous metals have emerged as promising functional architectures due to their tunable optical and electronic properties, high surface areas, and versatile use in real-life applications such as sensing, catalysis, and biomedicine. While the optical and morphological properties of nanoporous metals have been extensively studied, their electronic properties at ultrafast timescales remain largely unexplored. Here, we study the transient response of a nanoporous gold metamaterial and compare it …

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Xiaotian Chen, Hongyun Liu, Seyed Sahand Mohammadi Ziabari
https://arxiv.org/abs/2506.09204

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Deep Neural Networks (DNNs) have been proven to be exceptionally effective and have been applied across diverse domains within deep learning. However, as DNN models increase in complexity, the demand for reduced computational costs and memory overheads has become increasingly urgent. Sparsity has emerged as a leading approach in this area. The robustness of sparse Multi-layer Perceptrons (MLPs) for supervised feature selection, along with the application of Sparse Evolutionary Training (SET), i…