Tootfinder

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo
https://arxiv.org/abs/2506.01891

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose SineKAN, the NQS ansatz based on Kolmogorov-Arnold Networks (KANs), to represent quantum mechanical wave functions as nested univariate functions. We show that \sk wavefunction with learnable sinusoidal activation functions can capture the ground state energies, fidelities and various correlation functions of the 1D Transverse-F…

Non-Ferroelectric to Ferroelectric Phase Transition in epitaxial Y:HfO$_2$ via Rapid Thermal Annealing Induced Nitrogen Doping
Soumyajyoti Mondal, Asraful Haque, Binoy Krishna De, Shubham Kumar Parate, Pramod Kumar Yadav, Arup Basak, Kaushal Tiwari, Bhagwati Prasad, Pavan Nukala
https://arxiv.org/abs/2509.02229

Non-Ferroelectric to Ferroelectric Phase Transition in epitaxial Y:HfO$_2$ via Rapid Thermal Annealing Induced Nitrogen Doping
Oxygen vacancies are often essential for stabilizing the orthorhombic ferroelectric phase in HfO$_2$, with cationic doping widely employed to introduce such defects. In contrast, systematic studies on anionic doping to induce ferroelectricity remain largely in nascent stages. On epitaxial Y:HfO$_2$ grown on ITO buffered YSZ substrates that crystallize in a mixed monoclinic (non-polar) and orthorhombic (polar) phases, we introduce nitrogen doping via post deposition rapid thermal annealing (RTA)…

Immer mehr #Landwirte setzen auf #Agroforstwirtschaft, um ihre Felder widerstandsfähiger gegen #Trockenheit zu machen.
Auch Henning Rehren aus

Gegen die Trockenheit: Bäume auf dem Acker als Klimaanlage
Gegen die Trockenheit auf den Feldern scheint es eine Lösung zu geben: Bäume. Denn sie verändern das Klima auf dem Feld. Auch Landwirt Henning Rehren aus Gehrden hat seine Felder umgestellt.

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Mahin Montasir Afif, Abdullah Al Noman, K. M. Tahsin Kabir, Md. Mortuza Ahmmed, Md. Mostafizur Rahman, Mufti Mahmud, Md. Ashraful Babu
https://arxiv.org/abs/2506.17165

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains hig…

Foundations for Energy-Aware Zero-Energy Devices: From Energy Sensing to Adaptive Protocols
Onel L. A. L\'opez, Mateen Ashraf, Samer Nasser, Gabriel M. de Jesus, Ritesh Kumar Singh, Miltiadis C. Filippou, Jeroen Famaey
https://arxiv.org/abs/2507.22740

Foundations for Energy-Aware Zero-Energy Devices: From Energy Sensing to Adaptive Protocols
Zero-energy devices (ZEDs) are key enablers of sustainable Internet of Things networks by operating solely on harvested ambient energy. Their limited and dynamic energy budget calls for protocols that are energy-aware and intelligently adaptive. However, designing effective energy-aware protocols for ZEDs requires theoretical models that realistically reflect device constraints. Indeed, existing approaches often oversimplify key aspects such as energy information (EI) acquisition, task-level va…

Rams QB Matthew Stafford dealing with aggravated disc; hope is he'll be ready for Week 1 https://www.nfl.com/news/rams-qb-matthew-stafford-dealing-with-aggravated-disc-hope-is-he-ll-be-ready-for-week-1

Interface on demand: Towards AI native Control interfaces for 6G
Abhishek Dandekar, Prashiddha D. Thapa, Ashrafur Rahman, Julius Schulz-Zander
https://arxiv.org/abs/2508.15595 h…

Interface on demand: Towards AI native Control interfaces for 6G
Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necess…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric A F Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Sam Frank Kelemen, Joaqu\'in G\~oni, S\'ergio Pequito, Arian Ashourvan
https://arxiv.org/abs/2507.06136

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the physical substrate for functional connectivity (FC) -- the correlated neural activity between brain areas. While these structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways, recurrent netwo…

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Achraf Ait Laydi, Louis Cueff, Mewen Crespo, Yousef El Mourabit, H\'el\`ene Bouvrais
https://arxiv.org/abs/2507.07800

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Segmenting curvilinear structures in fluorescence microscopy remains a challenging task, particularly under noisy conditions and in dense filament networks commonly seen in vivo. To address this, we created two original datasets consisting of hundreds of synthetic images of fluorescently labelled microtubules within cells. These datasets are precisely annotated and closely mimic real microscopy images, including realistic noise. The second dataset presents an additional challenge, by simulating…