Tootfinder

Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization
SaiKrishna Saketh Yellapragada, Esa Ollila, Mario Costa
https://arxiv.org/abs/2509.13786 https:/…

Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization
As wireless communication systems advance toward Sixth Generation (6G) Radio Access Networks (RAN), Deep Learning (DL)-based neural receivers are emerging as transformative solutions for Physical Layer (PHY) processing, delivering superior Block Error Rate (BLER) performance compared to traditional model-based approaches. Practical deployment on resource-constrained hardware, however, requires efficient quantization to reduce latency, energy, and memory without sacrificing reliability. We exten…

How do people record images for Mapillary while on a bicycle? GoPro stuck to a helmet? Is that stable enough? Does something in the pipeline do post-processing to correct for camera tilt?
#biketooter #mapillary #camera

Recovering the Coupled Treatment of Redshift-Space Distortions and the Lightcone Effect after Diffuse Foreground Removal
Jennifer Feron, Emma Chapman
https://arxiv.org/abs/2509.13920

Recovering the Coupled Treatment of Redshift-Space Distortions and the Lightcone Effect after Diffuse Foreground Removal
The 21 cm brightness temperature during the Epoch of Reionisation is widely modelled using semi-numeric simulations, used for their computational speed and flexibility in testing astrophysical and cosmological parameters. However, it is common practice to simulate coeval brightness temperature boxes, and then apply post-processing algorithms that treat the lightcone effect and redshift-space distortions separately, assuming they can be added in sequence. We instead model them together, allowing…

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

Low-Discrepancy Set Post-Processing via Gradient Descent
Fran\c{c}ois Cl\'ement, Linhang Huang, Woorim Lee, Cole Smidt, Braeden Sodt, Xuan Zhang
https://arxiv.org/abs/2511.10496 https://arxiv.org/pdf/2511.10496 https://arxiv.org/html/2511.10496
arXiv:2511.10496v1 Announce Type: new
Abstract: The construction of low-discrepancy sets, used for uniform sampling and numerical integration, has recently seen great improvements based on optimization and machine learning techniques. However, these methods are computationally expensive, often requiring days of computation or access to GPU clusters. We show that simple gradient descent-based techniques allow for comparable results when starting with a reasonably uniform point set. Not only is this method much more efficient and accessible, but it can be applied as post-processing to any low-discrepancy set generation method for a variety of standard discrepancy measures.
toXiv_bot_toot

PRNet: Original Information Is All You Have
PeiHuang Zheng, Yunlong Zhao, Zheng Cui, Yang Li
https://arxiv.org/abs/2510.09531 https://arxiv.org/pdf/2510.09…

PRNet: Original Information Is All You Have
Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic conte…

Accuracy-First R\'enyi Differential Privacy and Post-Processing Immunity
Ossi R\"ais\"a, Antti Koskela, Antti Honkela
https://arxiv.org/abs/2509.22213 https://

Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
The accuracy-first perspective of differential privacy addresses an important shortcoming by allowing a data analyst to adaptively adjust the quantitative privacy bound instead of sticking to a predetermined bound. Existing works on the accuracy-first perspective have neglected an important property of differential privacy known as post-processing immunity, which ensures that an adversary is not able to weaken the privacy guarantee by post-processing. We address this gap by determining which ex…

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Shobhit Gupta, Nikolay Sheshko, Daniel J. Dilley, Alvin Gonzales, Manish K. Singh, Zain H. Saleem
https://arxiv.org/abs/2510.08894

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Distributing circuits across quantum processor modules will enable the execution of circuits larger than the qubit count limitations of monolithic processors. While distributed quantum computation has primarily utilized circuit cutting, it incurs an exponential growth of sub-circuit sampling and classical post-processing overhead with an increasing number of cuts. The entanglement-based gate teleportation approach does not inherently incur exponential sampling overhead, provided that quantum in…

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
https://arxiv.org/abs/2510.06165

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but becomes less direct when the predictive model involves interactions such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrate…

SeFEF: A Seizure Forecasting Evaluation Framework
Ana Sofia Carmo, Louren\c{c}o Abrunhosa Rodrigues, Ana Rita Peralta, Ana Fred, Carla Bentes, Hugo Pl\'acido da Silva
https://arxiv.org/abs/2510.11275

SeFEF: A Seizure Forecasting Evaluation Framework
The lack of standardization in seizure forecasting slows progress in the field and limits the clinical translation of forecasting models. In this work, we introduce a Python-based framework aimed at streamlining the development, assessment, and documentation of individualized seizure forecasting algorithms. The framework automates data labeling, cross-validation splitting, forecast post-processing, performance evaluation, and reporting. It supports various forecasting horizons and includes a …

Habitable World Discovery and Characterization: Coronagraph Concept of Operations and Data Post-Processing
Michael W. McElwain, Dimitri Mawet, Jean-Baptiste Ruffio, Roser Juanola Parramon, Kellen Lawson, Herv\'e Le Coroller, Christian Marois, Max Millar-Blanchaer, Bijan Nemati, Susan Redmond, Bin Ren, Laurent Pueyo, Christopher Stark, Scott Will
https://

Habitable World Discovery and Characterization: Coronagraph Concept of Operations and Data Post-Processing
The discovery and characterization of habitable worlds was the top scientific recommendation of the Astro2020 decadal survey and is a key objective of the Habitable Worlds Observatory. Biosignature identification drives exceedingly challenging observations, which require raw contrasts of roughly 10$^{-10}$ contrast and ultimately, 1$σ$ photometric precision of roughly 3$\times 10^{-12}$ contrast. Despite significant advances for the Nancy Grace Roman Space Telescope's Coronagraph Instrument, t…

Axial resolution post-processing engineering in Fresnel incoherent correlation holography
Shivasubramanian Gopinath, Joseph Rosen, Vijayakumar Anand
https://arxiv.org/abs/2509.26179

Axial resolution post-processing engineering in Fresnel incoherent correlation holography
Fresnel incoherent correlation holography (FINCH) is a self-interference-based incoherent digital holography method. In FINCH, light from an object point is split into two beams, modulated differently using two lenses with different focal distances, and creates a self-interference hologram. At least three phase-shifted holograms are recorded and synthesized into a complex hologram, which reconstructs the object image without twin image and bias noises. Compared with conventional imaging, FINCH …

HTTP has a new method: QUERY. Tl;dr: GET with a body.
https://www.ietf.org/archive/id/draft-ietf-httpbis-safe-method-w-body-14.html
(Doesn’t have an RFC number yet but has been approved, will get one in a few weeks.)

Audio Forensics Evaluation (SAFE) Challenge
Kirill Trapeznikov, Paul Cummer, Pranay Pherwani, Jai Aslam, Michael S. Davinroy, Peter Bautista, Laura Cassani, Matthew Stamm, Jill Crisman
https://arxiv.org/abs/2510.03387

Audio Forensics Evaluation (SAFE) Challenge
The increasing realism of synthetic speech generated by advanced text-to-speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to benchmark detection models across progressively harder scenarios: raw synthetic speech, processed audio (e.g., compression, resampling), and laundered a…

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
Xinyu Ma, Chengxin Wang, Meng Wang, Xu Guo, Liu Yang, Huajian Gao
https://arxiv.org/abs/2510.05572

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated o…

Fingerprinting LLMs via Prompt Injection
Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Gong
https://arxiv.org/abs/2509.25448 https://

Fingerprinting LLMs via Prompt Injection
Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it challenging to determine whether one model is derived from another. Existing provenance detection methods have two main limitations: (1) they embed signals into the base model before release, which is infeasible for already published models, or (2) they compare outputs across models using hand-crafted or random prompts, which are not robust to post-processi…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
Anirban Chatterjee, Sayantan Choudhury, Rohan Hore
https://arxiv.org/abs/2509.25507 https://arxi…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference, and conditional distribution modeling in complex data settings. In such settings, compared with unconditional sampling, additional feature information can be leveraged to enable mo…

Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing
Yuya Kawamata, Kosuke Mitarai, Keisuke Fujii
https://arxiv.org/abs/2509.14451 https://

Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing
We propose the quasi-Monte Carlo method for linear combination of unitaries via classical post-processing (LCU-CPP) on quantum applications. The LCU-CPP framework has been proposed as an approach to reduce hardware resources, expressing a general target operator $F(A)$ as $F(A) = \int_V f(t) G(A, t)dt$, where each $G(A, t)$ is proportional to a unitary operator. On a quantum device, $Re[Tr(G(A, t)ρ)]$ can be estimated using the Hadamard test and then combined through classical integration, all…

StateX: Enhancing RNN Recall via Post-training State Expansion
Xingyu Shen, Yingfa Chen, Zhen Leng Thai, Xu Han, Zhiyuan Liu, Maosong Sun
https://arxiv.org/abs/2509.22630 https:…

StateX: Enhancing RNN Recall via Post-training State Expansion
While Transformer-based models have demonstrated remarkable language modeling performance, their high complexities result in high costs when processing long contexts. In contrast, recurrent neural networks (RNNs) such as linear attention and state space models have gained popularity due to their constant per-token complexities. However, these recurrent models struggle with tasks that require accurate recall of contextual information from long contexts, because all contextual information is comp…

Unlocking ceramic 3D printing for next-generation chemical reactors
#materials #chemistry #ceramic #manufacturing

Unlocking ceramic 3D printing for next-generation chemical reactors
Scientists at the U.S. Department of Energy's Oak Ridge National Laboratory have integrated binder jet additive manufacturing with an advanced post-processing method to fabricate leak‐tight ceramic components, overcoming a key challenge of ceramic additive manufacturing.

BGRem: A background noise remover for astronomical images based on a diffusion model
R. Nicolaas, S. Caron, F. Stoppa, S. Bhattacharyya, R. Ruiz de Austri, P. J. Groot, A. J. Levan
https://arxiv.org/abs/2510.04718

BGRem: A background noise remover for astronomical images based on a diffusion model
Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading to extensive research in advanced post-processing techniques. Aims: Removing background noise from images is a valuable pre-processing step catalog-building tasks. We introduce BGRem, a machine learning (ML) based tool to remove background noise from astronomical images. Methods: BGRem uses a diffusion-based model…

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

The EBLM project XVI. Spin-orbit alignment of the low mass eclipsing binary EBLM J0021-16
Becca Spejcher, David V. Martin, Jake Pandina, Andy Zhang, Max Ammons, Wata Thubthong, Amaury Triaud, Ritika Sethi, Noah Vowell, Adrian Barker, Pierre Maxted, Alison Duck, Shelby Summers, Fran\c{c}ois Bouchy, Monika Lendl, Maxime Marmier, Malte Tewes, St\'ephane Udry
h…

The EBLM project XVI. Spin-orbit alignment of the low mass eclipsing binary EBLM J0021-16
Thousands of tight ($<1$ AU) main sequence binaries have been discovered, but it is uncertain how they formed. There is likely too much angular momentum in a collapsing, fragmenting protostellar cloud to form such binaries in situ, suggesting some post processing. One probe of a binary's dynamical history is the angle between the stellar spin and orbital axes -- its obliquity. The classical method for determining stellar obliquity is the Rossiter-McLaughlin effect. It has been applied to over 1…

Wow, une petite révolution en train de se discuter entre experts très investis.
https://www.ietf.org/archive/id/draft-ietf-httpbis-safe-method-w-body-14.html
On n'a plus qu'Š attendre 3-5 ans pour pouvoir utiliser des QUERY au lieu de PO…

Interdisciplinary Digital Twin Engine InterTwin for calorimeter simulation
Corentin Allaire, Vera Maiboroda, David Rousseau
https://arxiv.org/abs/2509.26527 https://

Interdisciplinary Digital Twin Engine InterTwin for calorimeter simulation
Calorimeter shower simulations are computationally expensive, and generative models offer an efficient alternative. However, achieving a balance between accuracy and speed remains a challenge, with distribution tail modeling being a key limitation. Invertible generative network CaloINN provides a trade-off between simulation quality and efficiency. The ongoing study targets introducing a set of post-processing modifications of analysis-level observables aimed at improving the accuracy of distri…

Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising
Ali Dadsetan, Frank Rudzicz
https://arxiv.org/abs/2510.01137 https://arxi…

Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising
We address the challenge of sample efficiency in differentially private fine-tuning of large language models (LLMs) using DP-SGD. While DP-SGD provides strong privacy guarantees, the added noise significantly increases the entropy of gradient matrices, disrupting their low-rank structure and slowing optimization. We propose a post-processing algorithm that leverages random matrix theory to denoise gradients, restore low-rank structure, and improve alignment with the original signal. Applied to …

The discretizations of the derivative by the continuous Galerkin and the discontinuous Galerkin methods are exactly the same
Bernardo Cockburn
https://arxiv.org/abs/2509.22587 h…

The discretizations of the derivative by the continuous Galerkin and the discontinuous Galerkin methods are exactly the same
In the framework of ODEs, we uncover a new link between the continuous Galerkin method (see Math. Comp. (1972), 26 (118 and 120), 415-426 and 881-891) and the discontinuous Galerkin method (see Mathematical Aspects of Finite elements in PDEs, (1974), 89-123), namely, that the discretizations of the derivative by these two methods are the same. A direct consequence of this result is the construction of a new elementwise post-processing of the approximate solution provided by the Discontinuous Ga…

Replaced article(s) found for q-bio.TO. https://arxiv.org/list/q-bio.TO/new
[1/1]:
- Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing
Banks, Thengane, Guerrero, Garc\'ia-Madue\~no, Li, Tang, Chaurasia

Correcting quantum errors using a classical code and one additional qubit
Tenzan Araki, Joseph F. Goodwin, Zhenyu Cai
https://arxiv.org/abs/2510.05008 https://

Correcting quantum errors using a classical code and one additional qubit
Classical error-correcting codes are powerful but incompatible with quantum noise, which includes both bit-flips and phase-flips. We introduce Hadamard-based Virtual Error Correction (H-VEC), a protocol that empowers any classical bit-flip code to correct arbitrary Pauli noise with the addition of only a single ancilla qubit and two layers of controlled-Hadamard gates. Through classical post-processing, H-VEC virtually filters the error channel, projecting the noise into pure Y-type errors that…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value ...
Philipp Ertz, Petra Friederichs

Generating Differentially Private Networks with a Modified Erd\H{o}s-R\'{e}nyi Model
Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti, Matthew Hale
https://arxiv.org/abs/2509.25431

Generating Differentially Private Networks with a Modified Erdős-Rényi Model
Differential privacy has been used to privately calculate numerous network properties, but existing approaches often require the development of a new privacy mechanism for each property of interest. Therefore, we present a framework for generating entire networks in a differentially private way. Differential privacy is immune to post-processing, which allows for any network property to be computed and analyzed for a private output network, without weakening its protections. We consider undirect…

StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models
Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He
https://arxiv.org/abs/2509.17993

StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models
The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion gen…

Confidence-Guided Error Correction for Disordered Speech Recognition
Abner Hernandez, Tom\'as Arias Vergara, Andreas Maier, Paula Andrea P\'erez-Toro
https://arxiv.org/abs/2509.25048

Confidence-Guided Error Correction for Disordered Speech Recognition
We investigate the use of large language models (LLMs) as post-processing modules for automatic speech recognition (ASR), focusing on their ability to perform error correction for disordered speech. In particular, we propose confidence-informed prompting, where word-level uncertainty estimates are embedded directly into LLM training to improve robustness and generalization across speakers and datasets. This approach directs the model to uncertain ASR regions and reduces overcorrection. We fine-…

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Context-Enhanced Granular Edit Representation for Efficient and Accurate ASR Post-editing
Luan Vejsiu, Qianyu Zheng, Haoxuan Chen, Yizhou Han

Advantage for Discrete Variational Quantum Algorithms in Circuit Recompilation
Oleksandr Kyriienko, Chukwudubem Umeano, Zo\"e Holmes
https://arxiv.org/abs/2510.01154 https:…

Advantage for Discrete Variational Quantum Algorithms in Circuit Recompilation
The relative power of quantum algorithms, using an adaptive access to quantum devices, versus classical post-processing methods that rely only on an initial quantum data set, remains the subject of active debate. Here, we present evidence for an exponential separation between adaptive and non-adaptive strategies in a quantum circuit recompilation task. Our construction features compilation problems with loss landscapes for discrete optimization that are unimodal yet non-separable, a structure k…

3D Macro Physics and Light Odd-Z Element Production in O-C Shell Mergers: Implications for $^{40}\mathrm{K}$ production and radiogenic heating inventories of rocky exoplanets
Joshua Issa, Falk Herwig, Stephen J. Mojzsis, Marco Pignatari
https://arxiv.org/abs/2509.19240

This is interesting—a maturing replacement to LaTeX! It's called Typst.
I've been using LaTeX for all kinds of word processing since I left the academic world behind—I'm writing the manual for my Gnome for seniors in it, at the moment. Might be interesting to switch to Typst, to try it out. (#latex #typst #linux #opensource #oss #typesetting

Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs
Parker Glenn, Alfy Samuel, Daben Liu
https://arxiv.org/abs/2509.20208 https://

Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs
Integrating LLM powered operators in declarative query languages allows for the combination of cheap and interpretable functions with powerful, generalizable language model reasoning. However, in order to benefit from the optimized execution of a database query language like SQL, generated outputs must align with the rules enforced by both type checkers and database contents. Current approaches address this challenge with orchestrations consisting of many LLM-based post-processing calls to ensu…

Superellipsoid modeling of wire-laser additive manufacturing defects measured by digital image correlation
Khalil Hachem (LURPA), Yann Quinsat (LURPA), Christophe Tournier (LURPA), Nicolas Beraud (G-SCOP)
https://arxiv.org/abs/2509.17545

Superellipsoid modeling of wire-laser additive manufacturing defects measured by digital image correlation
In wire-laser additive manufacturing (WLAM), producing parts with multiple entities requires optimizing both additive and subtractive processes. This involves adjusting positions and integrating machining operations. Geometric defects that occur during the components' deposition can affect subsequent entity production, making real-time measurement crucial. A contactless measuring system is essential to assess geometric deviations without complex post-processing. A comprehensive image stereocorr…

SiDiaC: Sinhala Diachronic Corpus
Nevidu Jayatilleke, Nisansa de Silva
https://arxiv.org/abs/2509.17912 https://arxiv.org/pdf/2509.17912

SiDiaC: Sinhala Diachronic Corpus
SiDiaC, the first comprehensive Sinhala Diachronic Corpus, covers a historical span from the 5th to the 20th century CE. SiDiaC comprises 58k words across 46 literary works, annotated carefully based on the written date, after filtering based on availability, authorship, copyright compliance, and data attribution. Texts from the National Library of Sri Lanka were digitised using Google Document AI OCR, followed by post-processing to correct formatting and modernise the orthography. The construc…

Discrete optimal transport is a strong audio adversarial attack
Anton Selitskiy, Akib Shahriyar, Jishnuraj Prakasan
https://arxiv.org/abs/2509.14959 https://

Discrete optimal transport is a strong audio adversarial attack
In this paper, we show that discrete optimal transport (DOT) is an effective black-box adversarial attack against modern audio anti-spoofing countermeasures (CMs). Our attack operates as a post-processing, distribution-alignment step: frame-level WavLM embeddings of generated speech are aligned to an unpaired bona fide pool via entropic OT and a top-$k$ barycentric projection, then decoded with a neural vocoder. Evaluated on ASVspoof2019 and ASVspoof5 with AASIST baselines, DOT yields consisten…