Tootfinder

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…

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…

"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"

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…

State Department tells consulates to resume processing Harvard visas - The Washington Post
https://www.washingtonpost.com/education/2025/06/06/state-department-tells-consulates-reject-harvard-visas/

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

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
ChangJae Lee, Heecheol Yang, Byeonggwon Kim
https://arxiv.org/abs/2506.03842

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Accurate quantitative precipitation forecasting (QPF) remains one of the main challenges in numerical weather prediction (NWP), primarily due to the difficulty of representing the full complexity of atmospheric microphysics through parameterization schemes. This study introduces a deep learning-based post-processing model, DL-QPF, which diagnoses precipitation fields from meteorological forecasts by learning directly from high-resolution radar estimates precipitation. The DL-QPF model is constr…

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.

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…

This https://arxiv.org/abs/2407.11523 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Improved Belief Propagation Decoding Algorithms for Surface Codes
Quantum error correction is crucial for universal fault-tolerant quantum computing. Highly accurate and low-time-complexity decoding algorithms play an indispensable role in ensuring quantum error correction works effectively. Among existing decoding algorithms, belief propagation (BP) is notable for its nearly linear time complexity and general applicability to stabilizer codes. However, BP's decoding accuracy without post-processing is unsatisfactory in most situations. This article focuses o…

Utilizing AI for Aviation Post-Accident Analysis Classification
Aziida Nanyonga, Graham Wild
https://arxiv.org/abs/2506.00169 https://

Utilizing AI for Aviation Post-Accident Analysis Classification
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage …

This https://arxiv.org/abs/2410.00023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Self-Tuning Spectral Clustering for Speaker Diarization
Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity matrix called spectral clustering on p-neighborhood retained affinity matrix (SC-pNA). Our method improves on node-specific fixed neighbor selection by allowing a variable number …

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Jiwen Yu, Jianhong Bai, Yiran Qin, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, Xihui Liu
https://arxiv.org/abs/2506.03141

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Recent advances in interactive video generation have shown promising results, yet existing approaches struggle with scene-consistent memory capabilities in long video generation due to limited use of historical context. In this work, we propose Context-as-Memory, which utilizes historical context as memory for video generation. It includes two simple yet effective designs: (1) storing context in frame format without additional post-processing; (2) conditioning by concatenating context and frame…

This https://arxiv.org/abs/2505.18458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

A Survey of LLM $\times$ DATA
The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisit…

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…

Sometimes I feel like a broken record when #writing about #neurodivergence
But re-processing my past through the lens of #Autism

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…

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…

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
Philipp Ertz, Petra Friederichs
https://arxiv.org/abs/2505.22182

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
The aim of this study is to provide a probabilistic gust analysis for the region of Germany that is calibrated with station observations and with an interpolation to unobserved locations. To this end, we develop a spatial Bayesian hierarchical model (BHM) for the post-processing of surface maximum wind gusts from the COSMO-REA6 reanalysis. Our approach uses a non-stationary extreme value distribution for the gust observations, with parameters that vary according to a linear model using COSMO-RE…

A seasonal to decadal calibration of 1990-2100 eastern Canadian freshwater discharge simulations by observations, data models, and neural networks
Richard E. Danielson, Minghong Zhang, Jo\"el Chass\'e, Will Perrie
https://arxiv.org/abs/2506.05261

A seasonal to decadal calibration of 1990-2100 eastern Canadian freshwater discharge simulations by observations, data models, and neural networks
A configuration of the NCAR WRF-Hydro model was sought using well established data models to guide the initial hydrologic model setup, as well as a seasonal streamflow post-processing by neural networks. Discharge was simulated using an eastern Canadian river network at two-km resolution. The river network was taken from a digital elevation model that was made to conform to observed catchment boundaries. Perturbations of a subset of model parameters were examined with reference to streamflow fr…

Getting #AI to write good #SQL: Text-to-SQL techniques explained
https://cloud.google.com/blo…

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…

Sample-optimal learning of quantum states using gentle measurements
Cristina Butucea, Jan Johannes, Henning Stein
https://arxiv.org/abs/2505.24587 https://…

Sample-optimal learning of quantum states using gentle measurements
Gentle measurements of quantum states do not entirely collapse the initial state. Instead, they provide a post-measurement state at a prescribed trace distance $α$ from the initial state together with a random variable used for quantum learning of the initial state. We introduce here the class of $α-$locally-gentle measurements ($α-$LGM) on a finite dimensional quantum system which are product measurements on product states and prove a strong quantum Data-Processing Inequality (qDPI) on this…

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…

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…

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…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
Athena Vakali, Ilias Dimitriadis
https://arxiv.org/abs/2506.09107 https:…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
AI models have become active decision makers, often acting without human supervision. The rapid advancement of AI technology has already caused harmful incidents that have hurt individuals and societies and AI unfairness in heavily criticized. It is urgent to disrupt AI pipelines which largely neglect human principles and focus on computational biases exploration at the data (pre), model(in), and deployment (post) processing stages. We claim that by exploiting the advances of agents technology,…

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…

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…

This https://arxiv.org/abs/2505.08640 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Exact quantum noise deconvolution with partial knowledge of noise
We introduce a new quantum noise deconvolution technique that does not rely on the complete knowledge of noise and does not require partial noise tomography. In this new method, we construct a set of observables with completely correctable expectation values despite our incomplete knowledge of noise. This task is achieved just by classical post-processing without extra quantum resources. We show that the number of parameters in the subset of observables with correctable expectation values is th…

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Tingzhi Li, Xuefeng Liu
https://arxiv.org/abs/2506.08602 https://

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model similarity, which is computationally expensive and prone to ownership collisions after model post-processing such as model pruning or fine-tuning. The latter embeds backdoors, exposing watermarked model…

Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
\v{S}imon Sedl\'a\v{c}ek, Bolaji Yusuf, J\'an \v{S}vec, Pradyoth Hegde, Santosh Kesiraju, Old\v{r}ich Plchot, Jan \v{C}ernock\'y
https://arxiv.org/abs/2506.08633

Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully open-sourced and open-data components (WavLM-large, OLMo). We focus on ablating different aspects of such systems including full/LoRA adapter fine-tuning, the effect of agent turns in the dialogue history, as well as fuzzy matching-based output post-processing, which greatly improves performance of our systems on nam…