Tootfinder

Enhancing Optical Imaging via Quantum Computation
Aleksandr Mokeev, Babak Saif, Mikhail D. Lukin, Johannes Borregaard
https://arxiv.org/abs/2509.09465 https://

Enhancing Optical Imaging via Quantum Computation
Extracting information from weak optical signals is a critical challenge across a broad range of technologies. Conventional imaging techniques, constrained to integrating over detected signals and classical post-processing, are limited in signal-to-noise ratio (SNR) from shot noise accumulation in the post-processing algorithms. We show that these limitations can be circumvented by coherently encoding photonic amplitude information into qubit registers and applying quantum algorithms to process…

CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data
Chongke Bi, Xin Gao, Jiangkang Deng, Guan
https://arxiv.org/abs/2508.08173 h…

CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data
Large-scale scientific simulations require significant resources to generate high-resolution time-varying data (TVD). While super-resolution is an efficient post-processing strategy to reduce costs, existing methods rely on a large amount of HR training data, limiting their applicability to diverse simulation scenarios. To address this constraint, we proposed CD-TVD, a novel framework that combines contrastive learning and an improved diffusion-based super-resolution model to achieve accurate 3…

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Alireza Khodaie, Berkay Kemal Balioglu, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05875

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Local differential privacy (LDP) has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent perturbation added by LDP protocols reduces the utility of the collected data. To mitigate this issue, several post-processing (PP) methods have been developed. Yet, the comparative performance of PP methods under diverse settings remains underexplored. In this paper, we present an extensive benchmark comprising 6 popular L…

Learned Regularization for Microwave Tomography
Bowen Tong, Hao Chen, Shaorui Guo, Dong Liu
https://arxiv.org/abs/2508.08114 https://arxiv.org/pdf/2508.081…

Learned Regularization for Microwave Tomography
Microwave Tomography (MWT) aims to reconstruct the dielectric properties of tissues from measured scattered electromagnetic fields. This inverse problem is highly nonlinear and ill-posed, posing significant challenges for conventional optimization-based methods, which, despite being grounded in physical models, often fail to recover fine structural details. Recent deep learning strategies, including end-to-end and post-processing networks, have improved reconstruction quality but typically requ…

MESAlab: a Pipeline for Mapping the Blue Loop with MESA runs
D. Tarczay-Neh\'ez
https://arxiv.org/abs/2509.08946 https://arxiv.org/pdf/2509.08946

MESAlab: a Pipeline for Mapping the Blue Loop with MESA runs
Modules for Experiments in Stellar Astrophysics (MESA; (Paxton2011, 2013, 2015, 2018, 2019, Jermyn 2023) is a widely used open-source software for modelling stellar evolution. In many studies, the computational grids covers thousands of models (e.g., Joyce et al 2024), which requires a tremendous amount of time and computational effort to process. To streamline data analysis, the mesalab package was developed. This Python-based pipeline is designed to simplify the post-processing of MESA outp…

StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation
Shuyuan Tu, Yueming Pan, Yinming Huang, Xintong Han, Zhen Xing, Qi Dai, Chong Luo, Zuxuan Wu, Yu-Gang Jiang
https://arxiv.org/abs/2508.08248

StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation
Current diffusion models for audio-driven avatar video generation struggle to synthesize long videos with natural audio synchronization and identity consistency. This paper presents StableAvatar, the first end-to-end video diffusion transformer that synthesizes infinite-length high-quality videos without post-processing. Conditioned on a reference image and audio, StableAvatar integrates tailored training and inference modules to enable infinite-length video generation. We observe that the main…

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Berkay Kemal Balioglu, Alireza Khodaie, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05872

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing (PP) methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the lack of a comprehensive and extensible LDP benchmarking toolkit raises difficulties in evaluating new protocols and PP methods. To address these concerns, this paper presents LDP$^3$ (pronounced LDP-C…

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Chen Feng, Yicheng Lin, Shaojie Zhuo, Chenzheng Su, Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Xiaopeng Zhang
https://arxiv.org/abs/2507.07877

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Recent advances in Automatic Speech Recognition (ASR) have demonstrated remarkable accuracy and robustness in diverse audio applications, such as live transcription and voice command processing. However, deploying these models on resource constrained edge devices (e.g., IoT device, wearables) still presents substantial challenges due to strict limits on memory, compute and power. Quantization, particularly Post-Training Quantization (PTQ), offers an effective way to reduce model size and infere…

"Two months ago, we decided that [AI] is finally mature enough to take center stage in rsyslog development. Since then, we’ve:
Upgraded the repository for AI agent compatibility (automated review and more)
Restructured key parts of the codebase
Built new functionality with heavy AI assistance
Developed a full base strategy for AI integration
Identified high-impact areas where AI will support log processing and observability"

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Yan Sun, Faming Liang
https://arxiv.org/abs/2508.01217 https://arxiv.org/…

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output from the last hidden layer of a pre-trained large-scale DNN model into a stochastic neural network (StoNet), then trains the StoNet with a sparse penalty on a validation dataset and constructs prediction…

Tel Aviv-based Qedma, which is developing an error mitigation software called QESEM for quantum computers, raised a $26M Series A led by Glilot Capital Partners (Anna Heim/TechCrunch)
https://techcrunch.com/2025/07/03/isra

Israeli quantum startup Qedma just raised $26 million, with IBM joining in | TechCrunch
Qedma specializes in error mitigation software. Its main piece of software, QESEM, standing for Quantum Error Suppression and Error Mitigation, analyzes noise patterns to suppress some classes of errors while the algorithm is running and mitigate others in post-processing.

Simultaneous Triggering and Synchronization of Sensors and Onboard Computers
Morten Nissov, Nikhil Khedekar, Kostas Alexis
https://arxiv.org/abs/2507.05717

Simultaneous Triggering and Synchronization of Sensors and Onboard Computers
High fidelity estimation algorithms for robotics require accurate data. However, timestamping of sensor data is a key issue that rarely receives the attention it deserves. Inaccurate timestamping can be compensated for in post-processing but is imperative for online estimation. Simultaneously, even online mitigation of timing issues can be achieved through a relaxation of the tuning parameters from their otherwise more performative optimal values, but at a detriment to performance. To address t…

An Open-Sourced, Community-Driven Volumetric Additive Manufacturing Printer and Post-Processor
Taylor Waddell, Erik Broude, Tristan Bourgade, Natalia Fabiana De La Torre, Erfan Kohyarnejadfard, Tavleen Kaur, Scarlett Hao, Dylan Motley, Daniel Oslund, Evan Percival, Connor Vidmar, Hayden Taylor
https://arxiv.org/abs/2509.02865

An Open-Sourced, Community-Driven Volumetric Additive Manufacturing Printer and Post-Processor
OpenCAL is a low cost CAL based printing and post processing platform developed using commercial off the shelf (COTS) components and standard rapid prototyping tools. The system incorporates recent advancements in VAM, including Optical Scattering Tomography (OST), and is designed with modularity to support future technological integration. The post processing systems developed include standardized procedures for solvent based removal of uncured resin and a centrifugal cleaning module for enhan…

THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling
Soroush Shahi, Farzad Shahabi, Rama Nabulsi, Glenn Fernandes, Aggelos Katsaggelos, Nabil Alshurafa
https://arxiv.org/abs/2507.06442

THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling
Wearable cameras are increasingly used as an observational and interventional tool for human behaviors by providing detailed visual data of hand-related activities. This data can be leveraged to facilitate memory recall for logging of behavior or timely interventions aimed at improving health. However, continuous processing of RGB images from these cameras consumes significant power impacting battery lifetime, generates a large volume of unnecessary video data for post-processing, raises privac…

GPI SPHERE detection of a 6.1MJup circumbinary planet around HD 143811
Vito Squicciarini, Johan Mazoyer, Christian Wilkinson, Anne-Marie Lagrange, Philippe Delorme, Alice Radcliffe, Olivier Flasseur, Flavien Kiefer, Evelyne Alecian
https://arxiv.org/abs/2509.06009

GPI+SPHERE detection of a 6.1MJup circumbinary planet around HD 143811
Context. Owing to its sensitivity to wide-orbit giant exoplanets, direct imaging is uniquely positioned to shed light on the interplay between proto-planetary disks and stellar hosts. Besides constraining formation models, new detections are natural benchmarks for atmospheric characterization. Aims. The COBREX project performed an extensive reanalysis of archival observations from SPHERE and GPI using advanced post-processing techniques, enhancing detection sensitivity at close separation. Newl…

An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction
Ali Hamdi, Malak Mohamed, Rokaia Emad, Khaled Shaban
https://arxiv.org/abs/2509.02446

An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction
Social telehealth has made remarkable progress in healthcare by allowing patients to post symptoms and participate in medical consultations remotely. Users frequently post symptoms on social media and online health platforms, creating a huge repository of medical data that can be leveraged for disease classification. Large language models (LLMs) such as LLAMA3 and GPT-3.5, along with transformer-based models like BERT, have demonstrated strong capabilities in processing complex medical text. In…

Replaced article(s) found for astro-ph.GA. https://arxiv.org/list/astro-ph.GA/new
[2/2]:
- HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for f...
Schuldt, Ca\~nameras, Shu, Andika, Bag, Grillo, Melo, Suyu, Taubenberger

Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Nicolas Heintz, Tom Francart, Alexander Bertrand
https://arxiv.org/abs/2506.24024

Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker environment. While state-of-the-art AAD algorithms can identify the attended speaker on short time windows, their predictions are often too inaccurate for practical use. In this work, we propose augmenting AAD with a hidden Markov model (HMM) that models the temporal structure of attention. More specifically, the HMM rel…

Enhancing Fault-Tolerant Surface Code Decoding with Iterative Lattice Reweighting
Yi Tian, Yi-Cong Zheng, Xiaoting Wang, Ching-Yi Lai
https://arxiv.org/abs/2509.06756 https://…

Enhancing Fault-Tolerant Surface Code Decoding with Iterative Lattice Reweighting
Efficient and realistic error decoding is crucial for fault-tolerant quantum computation (FTQC) on near-term devices. While decoding is a classical post-processing task, its effectiveness depends on accurately modeling quantum noise, which is hardware-dependent. In particular, correlated bit-flip ($X$) and phase-flip ($Z$) errors often arise under circuit-level noise. We introduce the Iterative Reweighting Minimum-Weight Perfect Matching (IRMWPM) decoder, which systematically incorporates such …

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu, Huaizu Jiang, Shrikanth Yadav, Qi Wan, Lei Qin, Geoffrey S Young
https://arxiv.org/abs/2507.00832

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CN…

Wonderfully geeky look into capturing awesome train photographs with a linear slit camera setup. The best part is all the details about the post processing steps.
https://daniel.lawrence.lu/blog/y2025m09d21/

Line scan camera image processing
I use my line scan camera to take cool pictures of trains and other stuff.

The formation and evolution of dust in the colliding-wind binary Apep revealed by JWST: #binary Apep [...] reveal an intricate series of nested concentric dust shells that are abundant in detailed substructure. The striking regularity in these substructures between successive shells suggests an exactly repeating formation mechanism combined with a highly stable outflow that maintains a consistent morphology even after reaching 0.6 pc (assuming a distance of 2.4 kpc) into the interstellar medium." See also https://bsky.app/profile/landru79.bsky.social/post/3luqbnb2jmk25 for other processing experiments with the image, https://arxiv.org/abs/2507.14610 for another paper and https://theconversation.com/swirling-nebula-of-two-dying-stars-revealed-in-spectacular-detail-in-new-webb-telescope-image-258314 for an article!

A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts
M\'aria Lakatos
https://arxiv.org/abs/2509.02784 https://

A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts
Ensemble forecasting systems have advanced meteorology by providing probabilistic estimates of future states, supporting applications from renewable energy production to transportation safety. Nonetheless, systematic biases often persist, making statistical post-processing essential. Traditional parametric post-processing techniques and machine learning-based methods can produce calibrated predictive distributions at specific locations and lead times, yet often struggle to capture dependencies …

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Ruicheng Xian, Yuxuan Wan, Han Zhao
https://arxiv.org/abs/2508.11258 https://

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Instruction fine-tuned large language models (LLMs) enable a simple zero-shot or few-shot prompting paradigm, also known as in-context learning, for building prediction models. This convenience, combined with continued advances in LLM capability, has the potential to drive their adoption across a broad range of domains, including high-stakes applications where group fairness -- preventing disparate impacts across demographic groups -- is essential. The majority of existing approaches to enforci…

Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning
Philip Huang, Yorai Shaoul, Jiaoyang Li
https://arxiv.org/abs/2508.05027 https://a…

Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning
Generating high-quality motion plans for multiple robot arms is challenging due to the high dimensionality of the system and the potential for inter-arm collisions. Traditional motion planning methods often produce motions that are suboptimal in terms of smoothness and execution time for multi-arm systems. Post-processing via shortcutting is a common approach to improve motion quality for efficient and smooth execution. However, in multi-arm scenarios, optimizing one arm's motion must not intro…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
Parshva Dhilankumar Patel
https://arxiv.org/abs/2507.07029 https://…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extr…

ah of course the hero image on this blog post about big data streaming should feature a processing pipeline feeding a data lake
but before i worked out what they intended i thought it was a corroded waste outflow pouring into a reservoir

#SIGBOVIK is at it again: "Factoring up to 255 “with” a Quantum Computer"
"[…] in this paper, I will appear to succeed at factoring by failing with style"
htt…

Elemental and Isotopic Yields from T Coronae Borealis: Predictions and Uncertainties
Emma Wallace, Christian Iliadis, Sumner Starrfield
https://arxiv.org/abs/2509.03395 https://…

Elemental and Isotopic Yields from T Coronae Borealis: Predictions and Uncertainties
T Coronae Borealis (T CrB) is a symbiotic recurrent nova system expected to undergo its next outburst within the next two years. Recent hydrodynamic simulations have predicted the nucleosynthetic yields for both carbon-oxygen (CO) and oxygen-neon (ONe) white-dwarf models, but without accounting for thermonuclear reaction-rate uncertainties. We perform detailed Monte Carlo post-processing nucleosynthesis calculations based on updated thermonuclear reaction rates and uncertainties from the 2025 e…

From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform
Zihan Ning, Yannick Brackenier, Sarah McElroy, Sara Neves Silva, Lucilio Cordero-Grande, Sam Rot, Liane S. Canas, Rebecca E Thornley, David Leit\~ao, Davide Poccecai, Andrew Cantell, Rene Kerosi, Anthony N Price, Jon Cleary, Donald J Tournier, Jana Hutter, Philippa Bridgen, Pierluigi Di Cio, Michela Cleri, Inka Granlund, Lucy Billimoria…

From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform
Purpose: To develop and validate a practical framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) delay from lengthy reconstructions, (2) limited support for multi-scan input reconstructions, (3) the need to adapt scripts for different raw formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner-based post-processing to custom outputs. Methods: The framework builds upon the Gadgetron pl…

The Efficiency Frontier: Classical Shadows versus Quantum Footage
Shuowei Ma, Junyu Liu
https://arxiv.org/abs/2509.06218 https://arxiv.org/pdf/2509.06218…

The Efficiency Frontier: Classical Shadows versus Quantum Footage
Interfacing quantum and classical processors is an important subroutine in full-stack quantum algorithms. The so-called "classical shadow" method efficiently extracts essential classical information from quantum states, enabling the prediction of many properties of a quantum system from only a few measurements. However, for a small number of highly non-local observables, or when classical post-processing power is limited, the classical shadow method is not always the most efficient choice. Here…

Looks indeed well made. Nice Website, too. https://www.threads.com/@luokai/post/DMNYb9hIunA

luokai (@luokai) on Threads
DJI just dropped the latest version of Terra, now supercharged with 3D Gaussian Splatting. 🚁✨ This isn’t just another update—it’s a leap for 3D reconstruction pros, blending photorealistic visuals, pinpoint mapping, and lightning-fast processing. Imagine digital twins so real, you’ll want to reach out and touch them. Key stats: Photorealistic 3D, trusted accuracy, LiDAR support, multispectral insights, and high-efficiency cluster processing—all in one end-to-end aerial survey s…

On the Optimization of Methods for Establishing Well-Connected Communities
Mohammad Dindoost, Oliver Alvarado Rodriguez, Bartosz Bryg, Minhyuk Park, George Chacko, Tandy Warnow, David A. Bader
https://arxiv.org/abs/2509.02590

On the Optimization of Methods for Establishing Well-Connected Communities
Community detection plays a central role in uncovering meso scale structures in networks. However, existing methods often suffer from disconnected or weakly connected clusters, undermining interpretability and robustness. Well-Connected Clusters (WCC) and Connectivity Modifier (CM) algorithms are post-processing techniques that improve the accuracy of many clustering methods. However, they are computationally prohibitive on massive graphs. In this work, we present optimized parallel implementat…

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Wonung Kim, Yubin Lee, Yoonsung Kim, Jinwoo Hwang, Seongryong Oh, Jiyong Jung, Aziz Huseynov, Woong Gyu Park, Chang Hyun Park, Divya Mahajan, Jongse Park
https://arxiv.org/abs/2507.10178

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Transformers are the driving force behind today's Large Language Models (LLMs), serving as the foundation for their performance and versatility. Yet, their compute and memory costs grow with sequence length, posing scalability challenges for long-context inferencing. In response, the algorithm community is exploring alternative architectures, such as state space models (SSMs), linear attention, and recurrent neural networks (RNNs), which we refer to as post-transformers. This shift presents a k…

Optimising reference library selection for reference-star differential imaging of discs with SPHERE/IRDIS
S. Stasevic, J. Milli, J. Mazoyer, A. -M. Lagrange, S. Bergeon
https://arxiv.org/abs/2509.03325

Optimising reference library selection for reference-star differential imaging of discs with SPHERE/IRDIS
The direct detection of circumstellar discs through high-contrast imaging provides key insights into the history and dynamics of planetary systems. Pole-on discs, especially faint debris discs, are difficult to detect and require careful consideration during post-processing to remove stellar residuals from the data while preserving the disc signal. Reference-star differential imaging (RDI) serves as one of the primary post-processing methods for disc observations. We aim to develop a method of …

A second-order and unconditionally stable time filtered scheme for the Cahn-Hilliard-Navier-Stokes system
Xi Li, Haijun Gao, Chunmei Xie, Minfu Feng
https://arxiv.org/abs/2507.02402

A second-order and unconditionally stable time filtered scheme for the Cahn-Hilliard-Navier-Stokes system
In this work, we propose, analyze, and test a novel computational low-complexity, linear, second-order, and unconditional energy-stable semi-discrete time-stepping scheme for the Cahn-Hilliard-Navier-Stokes (CHNS) system by employing the time filter technique. Firstly, the first-order semi-implicit backward Euler (BE) method is utilized to discretize the CHNS model; Secondly, the time filter, as a post-processing strategy, is incorporated into the BE scheme, requiring only minimal modifications…

A Multi-stage Low-latency Enhancement System for Hearing Aids
Chengwei Ouyang, Kexin Fei, Haoshuai Zhou, Congxi Lu, Linkai Li
https://arxiv.org/abs/2508.04283 https://

A Multi-stage Low-latency Enhancement System for Hearing Aids
This paper proposes an end-to-end system for the ICASSP 2023 Clarity Challenge. In this work, we introduce four major novelties: (1) a novel multi-stage system in both the magnitude and complex domains to better utilize phase information; (2) an asymmetric window pair to achieve higher frequency resolution with the 5ms latency constraint; (3) the integration of head rotation information and the mixture signals to achieve better enhancement; (4) a post-processing module that achieves higher hear…

Sequential Change Detection with Differential Privacy
Liyan Xie, Ruizhi Zhang
https://arxiv.org/abs/2509.02768 https://arxiv.org/pdf/2509.02768

Sequential Change Detection with Differential Privacy
Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false-alarm rate when pre- and post-change distributions are fully known. However, releasing CUSUM statistics and the corresponding stopping time directly can compromise individual data privacy. We therefore introduce a differentially private (DP) variant, called DP-CUSUM, that injects calibrated Laplace noise into b…

WaveMixings.jl: a Julia package for performing on-the-fly time-resolved nonlinear electronic spectra from quasi-classical trajectories
Luis Vasquez, Sebastian Pios, Lipeng Chen, Zhenggang Lan, Wolfgang Domcke, Maxim Gelin
https://arxiv.org/abs/2509.03917

WaveMixings.jl: a Julia package for performing on-the-fly time-resolved nonlinear electronic spectra from quasi-classical trajectories
We present an efficient numerical implementation of the quasi-classical doorway-window approximation, specifically designed for on-the-fly simulations of time-resolved nonlinear spectroscopic signals in the Julia package WaveMixings.jl. The package contains modules that facilitate standard tasks such as input/output data handling, data filtering, and post-processing, among others. WaveMixings.jl includes implementations of various methods developed by the authors, including integral and dispers…

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
Jordan Brook, Hamish McGowan, Matt Mason, Joshua Soderholm, Alain Protat
https://arxiv.org/abs/2509.02929

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
We present the Australian Identification, Nowcasting and Tracking (AINT) algorithm, a radar-based system for detecting, characterising, and archiving thunderstorm activity across the Australian continent. AINT employs a two-step hysteresis-based identification scheme to separate embedded convective cells, followed by a phase-correlation and optimisation framework for storm tracking and cell matching. Post-processing routines address challenges of ground clutter using clustering and topography-a…

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Lucien Heitz, Runze Li, Oana Inel, Abraham Bernstein
https://arxiv.org/abs/2508.13019

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Norm-aware recommender systems have gained increased attention, especially for diversity optimization. The recommender systems community has well-established experimentation pipelines that support reproducible evaluations by facilitating models' benchmarking and comparisons against state-of-the-art methods. However, to the best of our knowledge, there is currently no reproducibility framework to support thorough norm-driven experimentation at the pre-processing, in-processing, post-processing, …

Electrical Impedance Tomography with an Integrated Picoliter-Volume Subtractive Microfluidic Chamber in 65 nm CMOS
Antonio Victor Machado de Oliveira, Debjit Sarkar, Ali Hajimiri
https://arxiv.org/abs/2508.20431

Electrical Impedance Tomography with an Integrated Picoliter-Volume Subtractive Microfluidic Chamber in 65 nm CMOS
Electrical impedance tomography with fully integrated microfluidics and electronics is presented for the first time in a CMOS chip. Chambers and electrodes are fabricated in the interconnect layers of a 65 nm CMOS chip through post-processing, enabling picoliter-volumes to be processed and imaged. Tomography maps are reconstructed by reading out voltages from a 16-element electrode array and processing the data off-chip, and sources of variation in reconstruction are discussed. The EIT system p…

LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation
I-Chao Shen, Ariel Shamir, Takeo Igarashi
https://arxiv.org/abs/2508.11177 https://…

LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation
Recent deep learning methods can generate diverse graphic design layouts efficiently. However, these methods often create layouts with flaws, such as misalignment, unwanted overlaps, and unsatisfied containment. To tackle this issue, we propose an optimization-based method called LayoutRectifier, which gracefully rectifies auto-generated graphic design layouts to reduce these flaws while minimizing deviation from the generated layout. The core of our method is a two-stage optimization. First, w…

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
Mahdi Dhaini, Stephen Meisenbacher, Ege Erdogan, Florian Matthes, Gjergji Kasneci
https://arxiv.org/abs/2508.10482

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of \textit{explainability} and \textit{privacy}. While research interest in both explainable and privacy-preserving NLP has increased considerably in recent years, there remains a lack of investigation at the intersection of the two. This leaves a considerable gap in understanding of whether achieving \textit{both} explainability and privacy is possible, or whether t…

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition
Yanyan Liu, Minqiang Xu, Yihao Chen, Liang He, Lei Fang, Sian Fang, Lin Liu
https://arxiv.org/abs/2509.04392

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition
In recent years, large language models (LLM) have made significant progress in the task of generation error correction (GER) for automatic speech recognition (ASR) post-processing. However, in complex noisy environments, they still face challenges such as poor adaptability and low information utilization, resulting in limited effectiveness of GER. To address these issues, this paper proposes a noise-robust multi-modal GER framework (Denoising GER). The framework enhances the model's adaptabilit…

Granulation on a quiet K dwarf: HD 166620 I. Spectral signatures as a function of line-formation temperature
Ancy Anna John, Khaled Al Moulla, Niamh K. O Sullivan, Jay Fitzpatrick, Andrew Collier Cameron, Ben S. Lakeland, Michael Cretignier, Annelies Mortier, Tim Naylor, Joe Llama, Suzanne Aigrain, Christian Hartogh, Shweta Dalal, Heather M. Cegla, Christopher A. Watson, Xavier Dumusque, Aldo F. Martinez Fiorenzano

Granulation on a quiet K dwarf: HD 166620 I. Spectral signatures as a function of line-formation temperature
As Radial velocity (RV) spectrographs reach unprecedented precision and stability below 1 m/s, the challenge of granulation in the context of exoplanet detection has intensified. Despite promising advancements in post-processing tools, granulation remains a significant concern for the EPRV community. We present a pilot study to detect and characterise granulation using the High-Accuracy Radial-velocity Planet Searcher for the Northern hemisphere (HARPS-N) spectrograph. We observed HD166620, a K…

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Kazuto Fukuchi
https://arxiv.org/abs/2506.13947 https://

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algori…

Emissive perovskite quantum wires in robust nanocontainers
Bea Botka, Erzs\'ebet Dodony, Ildik\'o Hars\'anyi, Michael Stratton, J\'anos M\'ozer, \'Eva Kov\'ats, Katalin Kamar\'as
https://arxiv.org/abs/2508.19833

Emissive perovskite quantum wires in robust nanocontainers
Light emissive nanostructures were prepared from boron nitride nanotubes (BNNTs) filled with inorganic lead halide perovskites. BNNTs provide a platform for facile synthesis of high aspect ratio perovskite quantum wires having color-tunable, highly polarized emission. BNNTs form a flexible and robust protective shell around individual nanowires, that mitigate degradation during post-processing for practical applications, while allowing to exploit the emission of the perovskite nanowires due to …

A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Arjo Chakravarty, Michael X. Grey, M. A. Viraj J. Muthugala, Mohan Rajesh Elara
https://arxiv.org/abs/2506.16748

A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively appl…

A Visualization Framework for Exploring Multi-Agent-Based Simulations Case Study of an Electric Vehicle Home Charging Ecosystem
Kristoffer Christensen, Bo N{\o}rregaard J{\o}rgensen, Zheng Grace Ma
https://arxiv.org/abs/2506.20400

A Visualization Framework for Exploring Multi-Agent-Based Simulations Case Study of an Electric Vehicle Home Charging Ecosystem
Multi-agent-based simulations (MABS) of electric vehicle (EV) home charging ecosystems generate large, complex, and stochastic time-series datasets that capture interactions between households, grid infrastructure, and energy markets. These interactions can lead to unexpected system-level events, such as transformer overloads or consumer dissatisfaction, that are difficult to detect and explain through static post-processing. This paper presents a modular, Python-based dashboard framework, buil…

Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model
Julien Hauret, Thomas Joubaud, \'Eric Bavu
https://arxiv.org/abs/2508.02974

Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model
We present a real-time speech enhancement demo using speech captured with a throat microphone. This demo aims to showcase the complete pipeline, from recording to deep learning-based post-processing, for speech captured in noisy environments with a body-conducted microphone. The throat microphone records skin vibrations, which naturally attenuate external noise, but this robustness comes at the cost of reduced audio bandwidth. To address this challenge, we fine-tune Kyutai's Mimi--a neural audi…

Quasi-van Hove singularities informed approach improving DOS/pDOS predictions in GNN
Grigory Koroteev
https://arxiv.org/abs/2509.02818 https://arxiv.org/pd…

Quasi-van Hove singularities informed approach improving DOS/pDOS predictions in GNN
In this paper, we introduce an extension of the concept of Van Hoff singularities in order to explicitly use them in machine learning methods of models as a priori information. The claimed method becomes possible when, instead of directly calculating the density of quantum states, we operate with estimates obtained by machine learning methods. Then, determining additional information allows us to organize post-processing, which significantly improves the quality of model prediction. Analyticall…

SOAP: A Python Package for Calculating the Properties of Galaxies and Halos Formed in Cosmological Simulations
Robert McGibbon, John C. Helly, Joop Schaye, Matthieu Schaller, Bert Vandenbroucke
https://arxiv.org/abs/2507.22669

SOAP: A Python Package for Calculating the Properties of Galaxies and Halos Formed in Cosmological Simulations
Modern large scale cosmological hydrodynamic simulations require robust tools capable of analysing their data outputs in a parallel and efficient manner. We introduce SOAP (Spherical Overdensity and Aperture Processor), a Python package designed to compute halo and galaxy properties from SWIFT simulations after being post-processed with a subhalo finder. SOAP takes a subhalo catalogue as input and calculates a wide array of properties for each object. SOAP offers parallel processing capabilitie…

Momentum Equation-Based Regularization and Image Registration for Two-Dimensional Ultrasound Elasticity Imaging
Olalekan A. Babaniyi, Rebecca Rodrigues, Michael S. Richards
https://arxiv.org/abs/2508.19086

Momentum Equation-Based Regularization and Image Registration for Two-Dimensional Ultrasound Elasticity Imaging
Objective: Evaluate and compare multiple mechanics-based and traditional regularization strategies within a variational image registration framework for quasi-static ultrasound elastography. Methods:We reformulate a previously proposed momentum-equation-based post-processing method (SPREME) as a regularization term directly integrated into an image registration energy functional. Four regularization types are implemented and compared: a strain magnitude ($\R_ε$), a strain magnitude with incomp…

Floquet-informed Learning of Periodically Driven Hamiltonians
Keren Li
https://arxiv.org/abs/2509.02331 https://arxiv.org/pdf/2509.02331

Floquet-informed Learning of Periodically Driven Hamiltonians
Characterizing time-periodic Hamiltonians is pivotal for validating and controlling driven quantum platforms, yet prevailing and unadjusted reconstruction methods demand dense time-domain sampling and heavy post-processing. We introduce a scalable Floquet-informed learning algorithm that represents the Hamiltonian as a truncated Fourier series and recasts parameter estimation as a compact linear inverse problem in the Floquet band picture. The algorithm is well-suited to problems satisfying mil…

Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, a Low-Resource Language
Md Obyedullahil Mamun, Md Adyelullahil Mamun, Arif Ahmad, Md. Imran Hossain Emu
https://arxiv.org/abs/2507.18448

Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, a Low-Resource Language
Punctuation restoration enhances the readability of text and is critical for post-processing tasks in Automatic Speech Recognition (ASR), especially for low-resource languages like Bangla. In this study, we explore the application of transformer-based models, specifically XLM-RoBERTa-large, to automatically restore punctuation in unpunctuated Bangla text. We focus on predicting four punctuation marks: period, comma, question mark, and exclamation mark across diverse text domains. To address the…

BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming
Midhila Madhusoodanan, Mahesh Raveendranatha Panicker, Pisharody Harikrishnan Gopalakrishnan, Abhilash Rakkunedeth Hareendranathan
https://arxiv.org/abs/2507.15306

BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming
Pocket-sized, low-cost point-of-care ultrasound (POCUS) devices are increasingly used in musculoskeletal (MSK) applications for structural examination of bone tissue. However, the image quality in MSK ultrasound is often limited by speckle noise, low resolution, poor contrast, and anisotropic reflections, making bone images difficult to interpret without additional post-processing. Typically, medical ultrasound systems use delay and sum beamforming (DASB) for image reconstruction, which is not …

Uncoupled high-latitude wave models in COAMPS
W. E. Rogers, T. J. Campbell, J. Yu, R. A. Allard
https://arxiv.org/abs/2508.16755 https://arxiv.org/pdf/2508…

Uncoupled high-latitude wave models in COAMPS
This report describes six demonstration cases of two numerical ocean wave models in high latitude regions where waves interact with sea ice. The two wave models are SWAN (Simulating WAves Nearshore) and WW3 (WAVEWATCH III), run in uncoupled mode within the Navy's coupled regional modeling system, COAMPS(R) (Coupled Ocean Atmosphere Mesoscale Prediction System). The COAMPS software handles a large majority of the tasks associated with the setup, running, and post-processing of the wave models. A…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing
Hanfang Cui, Longfei Song, Li Li, Dongxing Xu, Yanhua Long

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Yifan Chen, Dong Wang, Yibo Zhao, Liang Cheng, Yi Zhang, Yang Zhang
https://arxiv.org/abs/2508.21498

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Current prevailing designs of quantum random number generators (QRNGs) designs typically employ post-processing techniques to distill raw random data, followed by statistical verification with suites like NIST SP 800-22. This paper demonstrates that this widely adopted practice harbors a critical flaw. We show that the powerful extraction process can create a false sense of security by perfectly concealing physical-layer attacks, rendering the subsequent statistical tests blind to a compromised…

Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
Hang Xu, Alexandre Bousse, Alessandro Perelli
https://arxiv.org/abs/2507.18012 https://

Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
Dual-energy X-ray Computed Tomography (DECT) constitutes an advanced technology which enables automatic decomposition of materials in clinical images without manual segmentation using the dependency of the X-ray linear attenuation with energy. However, most methods perform material decomposition in the image domain as a post-processing step after reconstruction but this procedure does not account for the beam-hardening effect and it results in sub-optimal results. In this work, we propose a dee…

Statistical post-processing of operational dual-resolution wind-speed ensemble forecasts
S\'andor Baran, M\'aria Lakatos
https://arxiv.org/abs/2506.15578

Statistical post-processing of operational dual-resolution wind-speed ensemble forecasts
Weather forecasting presents several challenges, including the chaotic nature of the atmosphere and the high computational demands of numerical weather prediction models. To achieve the most accurate predictions, the ideal scenario involves the lowest possible horizontal resolution and the largest ensemble size. This study provides a detailed comparative analysis of the forecast skill of the raw and post-processed medium- and extended-range wind-speed ensemble forecasts of the European Centre f…

Follow My Hold: Hand-Object Interaction Reconstruction through Geometric Guidance
Ayce Idil Aytekin, Helge Rhodin, Rishabh Dabral, Christian Theobalt
https://arxiv.org/abs/2508.18213

Follow My Hold: Hand-Object Interaction Reconstruction through Geometric Guidance
We propose a novel diffusion-based framework for reconstructing 3D geometry of hand-held objects from monocular RGB images by leveraging hand-object interaction as geometric guidance. Our method conditions a latent diffusion model on an inpainted object appearance and uses inference-time guidance to optimize the object reconstruction, while simultaneously ensuring plausible hand-object interactions. Unlike prior methods that rely on extensive post-processing or produce low-quality reconstructio…

gsaot: an R package for Optimal Transport-based sensitivity analysis
Leonardo Chiani, Emanuele Borgonovo, Elmar Plischke, Massimo Tavoni
https://arxiv.org/abs/2507.18588 https:/…

gsaot: an R package for Optimal Transport-based sensitivity analysis
gsaot is an R package for Optimal Transport-based global sensitivity analysis. It provides a simple interface for indices estimation using a variety of state-of-the-art Optimal Transport solvers such as the network simplex and Sinkhorn-Knopp. The package is model-agnostic, allowing analysts to perform the sensitivity analysis as a post-processing step. Moreover, gsaot provides functions for indices and statistics visualization. In this work, we provide an overview of the theoretical grounds, of…

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Sirui Li, Shuai Wang, Zhijun Liu, Zhongjie Jiang, Yannan Wang, Haizhou Li
https://arxiv.org/abs/2506.13709

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, these methods can sometimes be inadequate, resulting in residual noise or the introduction of new artifacts. Such deficiencies are typically not captured by metrics like SI-SNR but are noticeable to human listeners. To address this, we introduce SpeechRefiner, a post-processing tool that utilizes Conditional Flow Match…

Generative AI for Cybersecurity of Energy Management Systems: Methods, Challenges, and Future Directions
Aydin Zaboli, Junho Hong
https://arxiv.org/abs/2508.10044 https://

Generative AI for Cybersecurity of Energy Management Systems: Methods, Challenges, and Future Directions
This paper elaborates on an extensive security framework specifically designed for energy management systems (EMSs), which effectively tackles the dynamic environment of cybersecurity vulnerabilities and/or system problems (SPs), accomplished through the incorporation of novel methodologies. A comprehensive multi-point attack/error model is initially proposed to systematically identify vulnerabilities throughout the entire EMS data processing pipeline, including post state estimation (SE) steal…

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Yang Luo, Arunprakash Jayaprakash, Gaojie Chen, Chong Huang, Qu Luo, De Mi, Pei Xiao
https://arxiv.org/abs/2506.14557

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Althoug…

Arbitrary-Scale 3D Gaussian Super-Resolution
Huimin Zeng, Yue Bai, Yun Fu
https://arxiv.org/abs/2508.16467 https://arxiv.org/pdf/2508.16467

Arbitrary-Scale 3D Gaussian Super-Resolution
Existing 3D Gaussian Splatting (3DGS) super-resolution methods typically perform high-resolution (HR) rendering of fixed scale factors, making them impractical for resource-limited scenarios. Directly rendering arbitrary-scale HR views with vanilla 3DGS introduces aliasing artifacts due to the lack of scale-aware rendering ability, while adding a post-processing upsampler for 3DGS complicates the framework and reduces rendering efficiency. To tackle these issues, we build an integrated framewor…

Pitfalls when tackling the exponential concentration of parameterized quantum models
Reyhaneh Aghaei Saem, Behrang Tafreshi, Zo\"e Holmes, Supanut Thanasilp
https://arxiv.org/abs/2507.22054

Pitfalls when tackling the exponential concentration of parameterized quantum models
Identifying scalable circuit architectures remains a central challenge in variational quantum computing and quantum machine learning. Many approaches have been proposed to mitigate or avoid the barren plateau phenomenon or, more broadly, exponential concentration. However, due to the intricate interplay between quantum measurements and classical post-processing, we argue these techniques often fail to circumvent concentration effects in practice. Here, by analyzing concentration at the level of…

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Jiale Liu, Dandan Peng, Huan Wang, Chenyu Liu, Yan-Fu Li, Min Xie
https://arxiv.org/abs/2506.16225

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Aerospace engines, as critical components in aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures. While deep learning techniques have been extensively studied in this context, they output logits or confidence scores, necessitating post-processing to derive actionable insights. Furthermore, the potential of large-scale audio models in this domain remains largely untapped. To address these limitations, t…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neura...
Antoine Leclerc, Erwan Koch, Monika Feldmann, Daniele Nerini, Tom Beucler
…

Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods
Martin J\'anos Mayer, \'Agnes Baran, Sebastian Lerch, Nina Horat, Dazhi Yang, S\'andor Baran
https://arxiv.org/abs/2508.15508

Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods
Accurate and reliable forecasting of photovoltaic (PV) power generation is crucial for grid operations, electricity markets, and energy planning, as solar systems now contribute a significant share of the electricity supply in many countries. PV power forecasts are often generated by converting forecasts of relevant weather variables to power predictions via a model chain. The use of ensemble simulations from numerical weather prediction models results in probabilistic PV forecasts in the form …

Machine learning revolution for exoplanet direct imaging detection: transformer architectures
Yu-Chia Lin
https://arxiv.org/abs/2508.14508 https://arxiv.or…

Machine learning revolution for exoplanet direct imaging detection: transformer architectures
Directly imaging exoplanets is a formidable challenge due to extreme contrast ratios and quasi-static speckle noise, motivating the exploration of advanced post-processing methods. While Convolutional Neural Networks (CNNs) have shown promise, their inherent limitations in capturing long-range dependencies in image sequences hinder their effectiveness. This study introduces a novel hybrid deep learning architecture that combines a CNN feature extractor with a Transformer encoder to leverage tem…

Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
Kyriaki-Margarita Bintsi, Ya\"el Balbastre, Jingjing Wu, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki
https://arxiv.org/abs/2508.12942

Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
Anatomic tracer studies are critical for validating and improving diffusion MRI (dMRI) tractography. However, large-scale analysis of data from such studies is hampered by the labor-intensive process of annotating fiber bundles manually on histological slides. Existing automated methods often miss sparse bundles or require complex post-processing across consecutive sections, limiting their flexibility and generalizability. We present a streamlined, fully automated framework for fiber bundle seg…

Alternating Approach-Putt Models for Multi-Stage Speech Enhancement
Iksoon Jeong, Kyung-Joong Kim, Kang-Hun Ahn
https://arxiv.org/abs/2508.10436 https://ar…

Alternating Approach-Putt Models for Multi-Stage Speech Enhancement
Speech enhancement using artificial neural networks aims to remove noise from noisy speech signals while preserving the speech content. However, speech enhancement networks often introduce distortions to the speech signal, referred to as artifacts, which can degrade audio quality. In this work, we propose a post-processing neural network designed to mitigate artifacts introduced by speech enhancement models. Inspired by the analogy of making a `Putt' after an `Approach' in golf, we name our mod…

Portfolio construction using a sampling-based variational quantum scheme
Gabriele Agliardi, Dimitris Alevras, Vaibhaw Kumar, Roberto Lo Nardo, Gabriele Compostella, Sumit Kumar, Manuel Proissl, Bimal Mehta
https://arxiv.org/abs/2508.13557

Portfolio construction using a sampling-based variational quantum scheme
The efficient and effective construction of portfolios that adhere to real-world constraints is a challenging optimization task in finance. We investigate a concrete representation of the problem with a focus on design proposals of an Exchange Traded Fund. We evaluate the sampling-based CVaR Variational Quantum Algorithm (VQA), combined with a local-search post-processing, for solving problem instances that beyond a certain size become classically hard. We also propose a problem formulation tha…