Tootfinder

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
Michail Kavousanakis, Gianluca Fabiani, Anastasia Georgiou, Constantinos Siettos, Panagiotis Kevrekidis, Ioannis Kevrekidis
https://arxiv.org/abs/2509.15963

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
In the past, we have presented a systematic computational framework for analyzing self-similar and traveling wave dynamics in nonlinear partial differential equations (PDEs) by dynamically factoring out continuous symmetries such as translation and scaling. This is achieved through the use of time-dependent transformations -- what can be viewed as dynamic pinning conditions -- that render the symmetry-invariant solution stationary or slowly varying in rescaled coordinates. The transformation pr…

Inference Time Debiasing Concepts in Diffusion Models
Lucas S. Kupssinsk\"u, Marco N. Bochernitsan, Jordan Kopper, Ot\'avio Parraga, Rodrigo C. Barros
https://arxiv.org/abs/2508.14933

Inference Time Debiasing Concepts in Diffusion Models
We propose DeCoDi, a debiasing procedure for text-to-image diffusion-based models that changes the inference procedure, does not significantly change image quality, has negligible compute overhead, and can be applied in any diffusion-based image generation model. DeCoDi changes the diffusion process to avoid latent dimension regions of biased concepts. While most deep learning debiasing methods require complex or compute-intensive interventions, our method is designed to change only the inferen…

Dynamic polarization of nuclear spins by optically-oriented electrons and holes in lead halide perovskite semiconductors
Mladen Kotur, Pavel S. Bazhin, Kirill V. Kavokin, Nataliia E. Kopteva, Dmitri R. Yakovlev, Dennis Kudlacik, Manfred Bayer
https://arxiv.org/abs/2509.15530

Dynamic polarization of nuclear spins by optically-oriented electrons and holes in lead halide perovskite semiconductors
A theory of dynamic polarization of the nuclear spin system via optically-oriented charge carriers in lead halide perovskites is developed and compared with the experiments performed on a FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ crystal. The spin Hamiltonians of the electron and hole hyperfine interaction with the nuclear spins of lead and halogen are derived. The hyperfine interaction of the halogen spins with charge carriers is shown to be anisotropic and depending on the position of the hal…

Use of a genetic algorithm to find solutions to introductory physics problems
Tom Bensky, Justin Kopcinski
https://arxiv.org/abs/2508.10920 https://arxiv.o…

Use of a genetic algorithm to find solutions to introductory physics problems
In this work, we show how a genetic algorithm (GA) can be used to find step-by-step solutions to introductory physics problems. Our perspective is that the underlying task for this is one of finding a sequence of equations that will lead to the needed answer. Here a GA is used to find an appropriate equation sequence by minimizing a fitness function that measures the difference between the number of unknowns versus knowns in a set of equations. Information about knowns comes from the GA posing …

Quantized topological transport mediated by the long-range couplings
Ekaterina S. Lebedeva, Maxim Mazanov, Alexey V. Kavokin, Maxim A. Gorlach
https://arxiv.org/abs/2509.13553 h…

Quantized topological transport mediated by the long-range couplings
Certain topological systems with time-varying Hamiltonian enable quantized and disorder-robust transport of excitations. Here, we introduce the modification of the celebrated Thouless pump when the on-site energies remain fixed, while the nearest and next-nearest neighbor couplings vary in time. We demonstrate quantized transport of excitations and propose an experimental implementation using an array of evanescently coupled optical waveguides.

KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
Dmitry Akulov, Mohamed Sana, Antonio De Domenico, Tareq Si Salem, Nicola Piovesan, Fadhel Ayed
https://arxiv.org/abs/2509.05165

KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
Large language models (LLMs) rely on key-value (KV) caches for efficient autoregressive decoding; however, cache size grows linearly with context length and model depth, becoming a major bottleneck in long-context inference. Prior KV cache compression methods either enforce rigid heuristics, disrupt tensor layouts with per-attention-head variability, or require specialized compute kernels. We propose a simple, yet effective, KV cache compression framework based on attention-guided, layer-adap…

Reply to "Comment(s) to "Consequences of the single-pair measurement of the Bell parameter""
Marco Genovese, Fabrizio Piacentini
https://arxiv.org/abs/2509.07922

Reply to "Comment(s) to "Consequences of the single-pair measurement of the Bell parameter""
We reply to the comments made by M. Kupczynski and J. P. Lambare on our recent paper titled ''Consequences of the single-pair measurement of the Bell parameter'' [Phys. Rev. A 111, 022204 (2025)], questioning our claims on the implications on Quantum Mechanics interpretations (and alternative theories) generated by the results of a recent experiment [Quantum Sci. Technol. 9, 045027 (2024)] demonstrating the possibility of measuring the entire Bell parameter on a single entangled pair.

"Tutkimus kattoi koko
Kanadan alueelta satoja metsiä ja mil-
joonia neliökilometrejä. Läpileikkaava
johtopäätös: kun metsässä on enem-
män monimuotoisuutta, se kerryttää
enemmän hiiltä maaperään.
Toisessa tuoreessa tutkimuksessa
laskettiin koko maapallon mittakaa-
vassa, mitä monimuotoisuuden pois-
leikkautuminen ekosysteemeistä aiheuttataa. Se jättää hiiltä ilmakehään."

Niin kaunis on Maa
Metsä, maailma ja minä -podcastin ensimmäisessä jaksossa vieraana on toimittaja ja tietokirjailija Juha Kauppinen. Toimittajana Kauppinen on tullut tunnetuksi muun muassa seurattuaan Talvivaaran kaivoksen vaiheita. Hänen ehkä tunnetuin teoksensa on Monimuotoisuus – Kertomuksia katoamisista.

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
Alexander Nikitas Dimopoulos, Joseph Grasso
https://arxiv.org/abs/2508.00822

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
This study analyzes semantic segmentation performance across heterogeneously labeled point-cloud datasets relevant to public safety applications, including pre-incident planning systems derived from lidar scans. Using NIST's Point Cloud City dataset (Enfield and Memphis collections), we investigate challenges in unifying differently labeled 3D data. Our methodology employs a graded schema with the KPConv architecture, evaluating performance through IoU metrics on safety-relevant features. Resul…

Near optimal quantum algorithm for estimating Shannon entropy
Myeongjin Shin, Kabgyun Jeong
https://arxiv.org/abs/2509.07452 https://arxiv.org/pdf/2509.074…

Near optimal quantum algorithm for estimating Shannon entropy
We present a near-optimal quantum algorithm, up to logarithmic factors, for estimating the Shannon entropy in the quantum probability oracle model. Our approach combines the singular value separation algorithm with quantum amplitude amplification, followed by the application of quantum singular value transformation. On the lower bound side, we construct probability distributions encoded via Hamming weights in the oracle, establishing a tight query lower bound up to logarithmic factors. Conseque…