Tootfinder

Bombshell interview sees Trump witness reveal he loaded boxes of documents onto plane
#Brian #Butler, an employee at Mar-a-Lago serving as a witness in the #classified

Bombshell interview sees Trump witness reveal he loaded boxes of documents onto plane
Brian Butler, an employee at Mar-a-Lago serving as a witness in the classified documents case against former President Donald Trump, told his side of the story to CNN's Kaitlan Collins in an interview released on Monday.Butler, identified in court records only as "Trump Employee 5," said he was aske...

Classifying point clouds at the facade-level using geometric features and deep learning networks
Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
https://arxiv.org/abs/2402.06506

Classifying point clouds at the facade-level using geometric features and deep learning networks
3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods' per…

Robust performance metrics for imbalanced classification problems
Hajo Holzmann, Bernhard Klar
https://arxiv.org/abs/2404.07661 https://

Robust performance metrics for imbalanced classification problems
We show that established performance metrics in binary classification, such as the F-score, the Jaccard similarity coefficient or Matthews' correlation coefficient (MCC), are not robust to class imbalance in the sense that if the proportion of the minority class tends to $0$, the true positive rate (TPR) of the Bayes classifier under these metrics tends to $0$ as well. Thus, in imbalanced classification problems, these metrics favour classifiers which ignore the minority class. To alleviate thi…

Coalgebraic Modal Logic for Dynamic Systems with Uncertainty
Andr\'es Gallardo, Ignacio Viglizzo
https://arxiv.org/abs/2403.06177 https://

Coalgebraic Modal Logic for Dynamic Systems with Uncertainty
In this paper we define a class of polynomial functors suited for constructing coalgebras representing processes in which uncertainty plays an important role. In these polynomial functors we include upper and lower probability measures, finitely additive probability measures, plausibilty measures (and their duals, belief functions), and possibility measures. We give axioms and inference rules for the associated system of coalgebraic modal logic, and construct the canonical coalgebras to prove a…

#CactusFlowers
Apparently it’s National #Cactus Day. Here are two prickly pear flowers. June 2017

"Corcoran took potentially incriminating notes about the classified documents stashed at the Mar-a-Lago country club — and federal prosecutors have obtained them."
Trump's loss of 'critical' lawyer 'could pose a significant issue for' ex-president: CNN - Raw Story
https://www.

Robust automated calcification meshing for biomechanical cardiac digital twins
Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk, Raymond McKay, Ellen T. Roche, Rudolph Gleason, James S. Duncan
https://arxiv.org/abs/2403.04998

Robust automated calcification meshing for biomechanical cardiac digital twins
Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcified heart meshes for physics-driven simulations are still often reconstructed using manual operations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated meshing algorithm that enables robust incorporat…

Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea\textendash Land Clutter Classification
Xiaoxuan Zhang, Quan Pan, Salvador Garc\'ia
https://arxiv.org/abs/2402.06315

Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea\textendash Land Clutter Classification
Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervised Adversarial Domain Generalization Network (MSADGN) for cross-scene sea\textendash land clutter classification. MSADGN can extract domain-…

Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization
has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean.
Notably, these signals were detected at distances ranging from two to five thousand kilometres,
highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments.
In this study, we investigate the plausibil…

Underwater acoustic analysis reveals unique pressure signals associated with aircraft crashes in the sea: revisiting MH370 - Scientific Reports
Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean. Notably, these signals were detected at distances ranging from two to five thousand kilometres, highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments. In this study, we investigate the plausibility of an aircraft, such as M…

This https://arxiv.org/abs/2212.04382 has been replaced.
link: https://scholar.google.com/scholar?q=a

Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier
Whether based on models, training data or a combination, classifiers place (possibly complex) input data into one of a relatively small number of output categories. In this paper, we study the structure of the boundary--those points for which a neighbor is classified differently--in the context of an input space that is a graph, so that there is a concept of neighboring inputs, The scientific setting is a model-based naive Bayes classifier for DNA reads produced by Next Generation Sequencers. W…