Tootfinder

taken at the FDR Memorial in DC, a few days ago. #nokings

Verifying Correctness of Shared Channels in a Cooperatively Scheduled Process-Oriented Language
Jan Pedersen, Kevin Chalmers
https://arxiv.org/abs/2510.11751 https://

Verifying Correctness of Shared Channels in a Cooperatively Scheduled Process-Oriented Language
Correct concurrent behaviour is important in understanding how components will act within certain conditions. In this work. we analyse the behaviour of shared communicating channels within a coorporatively scheduled runtime. We use the refinement checking and modelling tool FDR to develop both specifications of how such shared channels should behave and models of the implementations of these channels in the cooperatively scheduled language ProcessJ. Our results demonstrate that although we can …

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
Kevin Chalmers, Jan B{\ae}kgaard Pedersen
https://arxiv.org/abs/2510.10818

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
We present the first spin-free, kernel-lock-free mutex that cooperates with user-mode schedulers and is formally proven FIFO-fair and linearizable using CSP/FDR. Our fairness oracle and stability-based proof method are reusable across coroutine runtime designs. We designed the claim/release protocol for a process-oriented language -- ProcessJ -- to manage the race for claiming shared inter-process communication channels. Internally, we use a lock-free queue to park waiting processes for gaining…

Trump’s praise for Mamdani upends Republican talking points
as they bond over New York, working class and FDR
President and mayor-elect refused to be drawn into talking about their differences despite repeated attempts to nudge them into conflict

Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control for the filtered rejection set
Marina Bogomolov, Shinjini Nandi
https://arxiv.org/abs/2509.15444

Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control for the filtered rejection set
Modern biological studies often involve testing many hypotheses organized in a group or a hierarchical structure, such as a directed acyclic graph (DAG). In these studies, researchers often wish to control the false discovery rate (FDR) after filtering the discoveries to obtain interpretable results. For addressing this goal, Katsevich, Sabatti, and Bogomolov (2023, Journal of the American Statistical Association, 118(541), 165-176) developed a general method, Focused BH, that guarantees FDR co…

De novo peptide sequencing rescoring and FDR estimation with Winnow
Amandla Mabona, Jemma Daniel, Henrik Servais Janssen Knudsen, Rachel Catzel, Kevin Michael Eloff, Erwin M. Schoof, Nicolas Lopez Carranza, Timothy P. Jenkins, Jeroen Van Goey, Konstantinos Kalogeropoulos
https://arxiv.org/abs/2509.24952

De novo peptide sequencing rescoring and FDR estimation with Winnow
Machine learning has markedly advanced de novo peptide sequencing (DNS) for mass spectrometry-based proteomics. DNS tools offer a reliable way to identify peptides without relying on reference databases, extending proteomic analysis and unlocking applications into less-charted regions of the proteome. However, they still face a key limitation. DNS tools lack principled methods for estimating false discovery rates (FDR) and instead rely on model-specific confidence scores that are often miscalib…

Multi-Condition Conformal Selection
Qingyang Hao, Wenbo Liao, Bingyi Jing, Hongxin Wei
https://arxiv.org/abs/2510.08075 https://arxiv.org/pdf/2510.08075

Multi-Condition Conformal Selection
Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignment of large language models. While conformal selection methods offer a rigorous solution with False Discovery Rate (FDR) control, their applicability is confined to single-threshold scenarios (i.e., y > c) and overlooks practical needs for multi-condition selection, such as conjunctive or disjunctive conditions. In th…

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
Daofu Zhang, Mehrdad Pournaderi, Hanne M. Clifford, Yu Xiang, Pramod K. Varshney
https://arxiv.org/abs/2510.00463

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior under adversarial perturbations remains unexplored. We first formulate an oracle attack setting that quantifies the worst-case degradation of FDR, deriving an upper bound that…

To my eyes, this is an act of war by the US upon the nation of Venezuela. (Not to mention the blurring of GPS and the sinking of boats by the US.)
If indeed a state of war now exists (to borrow a phrase from FDR's speech to Congress after Pearl Harbor) then Venezuela could be anticipated to wage war against the US - endangering US people (including tourists) everywhere in the world.
El Cheato and Whiskey Pete are getting the US in Vietnam Part II.
"Trump says airs…

if fdr were today what unholy abomination of a podcast would replace the fireside chats

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
Heng Ge, Qing Lu
https://arxiv.org/abs/2510.01418 https://arxiv.org/pdf/2510.0…

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
We introduce DiffKnock, a diffusion-based knockoff framework for high-dimensional feature selection with finite-sample false discovery rate (FDR) control. DiffKnock addresses two key limitations of existing knockoff methods: preserving complex feature dependencies and detecting non-linear associations. Our approach trains diffusion models to generate valid knockoffs and uses neural network--based gradient and filter statistics to construct antisymmetric feature importance measures. Through simu…

Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control ...
Marina Bogomolov, Shinjini Nandi

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- De novo peptide sequencing rescoring and FDR estimation with Winnow
Mabona, Daniel, Knudsen, Catzel, Eloff, Schoof, Carranza, Jenkins, Van Goey, Kalogeropoulos

False Discovery Rate Control via Bayesian Mirror Statistic
Marco Molinari, Magne Thoresen
https://arxiv.org/abs/2510.00875 https://arxiv.org/pdf/2510.00875…

False Discovery Rate Control via Bayesian Mirror Statistic
Simultaneously performing variable selection and inference in high-dimensional models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while being able to control some form of selection error. In this work we adapt the Mirror Statistic approach to False Discovery Rate (FDR) control into a Bayesia…