Tootfinder

Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Data
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein, Ofer Hadar
https://arxiv.org/abs/2509.22850 h…

Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Data
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a novel black-box, decision-based adversarial attack tailored for tabular data. Our approach combines gradient-free direction estimation with an iterative boundary search, enabling efficient navigation of discrete and continuous feature spaces under minimal oracle access. Extensive experiments demonstrate that our method successfully compromises nearly …

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Robert Graf, Tanja Lerchl, Kati Nispel, Hendrik M\"oller, Matan Atad, Julian McGinnis, Julius Maria Watrinet, Johannes Paetzold, Daniel Rueckert, Jan S. Kirschke
https://arxiv.org/abs/2508.14708

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical modeling. In this work, we present a rule-based approach for subpixel-accurate key-point extraction from MRI, adapted from prior CT-based methods. Our approach incorporates robust image alignment and vertebra-specific orientation estimation to generate anatomically meaningful landmarks that serve as boundary conditions …

Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
Xiaohan Wang, Yang Ning
https://arxiv.org/abs/2508.20942

Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric transformation of the Bayes decision boundary, our method reformulates the problem as a low-dimensional empirical risk minimization problem. Under mild regularity conditions, we establish the consistency of our estimators and derive the risk bounds. M…

Comparative Explanations via Counterfactual Reasoning in Recommendations
Yi Yu, Zhenxing Hu
https://arxiv.org/abs/2510.10920 https://arxiv.org/pdf/2510.109…

Comparative Explanations via Counterfactual Reasoning in Recommendations
Explainable recommendation through counterfactual reasoning seeks to identify the influential aspects of items in recommendations, which can then be used as explanations. However, state-of-the-art approaches, which aim to minimize changes in product aspects while reversing their recommended decisions according to an aggregated decision boundary score, often lead to factual inaccuracies in explanations. To solve this problem, in this work we propose a novel method of Comparative Counterfactual E…

Good Morning #Canada
It's a holiday across most of our nation today, so why not take 14 minutes to find out why Alaska isn't part of Canada and discover why Lord Alverstone is the villain you've likely never heard of.
Lord Alverstone, whose full name was Richard Everard Webster, 1st Viscount Alverstone, played a key role in the Alaska Boundary Dispute of 1903. As the British representative on the arbitration tribunal, he ultimately sided with the United States, leading to a decision that favored the US claim over Canada's claims regarding the Alaska panhandle. At the time, the British government was trying to rebuild relationships with the U.S. and likely instructed Lord Alverstone to rule against Canadian border claims.
#CanadaIsAwesome #History
https://youtu.be/woXBk3OAtSM?si=-pTwI7OKidfK2g2L

Topological Signatures of ReLU Neural Network Activation Patterns
Vicente Bosca, Tatum Rask, Sunia Tanweer, Andrew R. Tawfeek, Branden Stone
https://arxiv.org/abs/2510.12700 htt…

Topological Signatures of ReLU Neural Network Activation Patterns
This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary -- in the case of binary classification. Additionally, we compute the homology of the cellular decomposition -- in a regr…

The Geodesics Less Traveled: Nonminimal RT Surfaces and Holographic Scattering
Jacqueline Caminiti, Caroline Lima, Robert C. Myers
https://arxiv.org/abs/2509.03597 https://

The Geodesics Less Traveled: Nonminimal RT Surfaces and Holographic Scattering
The connected wedge theorem states that in order to have a scattering process in the bulk, it is necessary to have O(1/G) mutual information between certain "decision" regions in the boundary theory. While this large mutual information is not generally sufficient to imply scattering, Caminiti et al. (2024) showed that for a certain class of geometries, bulk scattering is implied by a certain relation between two (possibly non-minimal) Ryu-Takayanagi surfaces. Here, we show that the 2-to-2 versi…

Replaced article(s) found for cs.CG. https://arxiv.org/list/cs.CG/new
[1/1]:
- Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Func...
Goutham, DalferroNucci, Stockar, Menon, Nayak, Zade, Patel, Santillo

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Zhen Yang, Yansong Ma, Lei Chen
https://arxiv.org/abs/2510.08949 https://

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Trustworthy medical image segmentation aims at deliver accurate and reliable results for clinical decision-making. Most existing methods adopt the evidence deep learning (EDL) paradigm due to its computational efficiency and theoretical robustness. However, the EDL-based methods often neglect leveraging uncertainty maps rich in attention cues to refine ambiguous boundary segmentation. To address this, we propose a progressive evidence uncertainty guided attention (PEUA) mechanism to guide the m…

A proof of Generalized Connected Wedge Theorem
Bowen Zhao
https://arxiv.org/abs/2509.23119 https://arxiv.org/pdf/2509.23119

A proof of Generalized Connected Wedge Theorem
In the context of asymptotic $2$-to-$2$ scattering process in AdS/CFT, the Connected Wedge Theorem identifies the existence of $O(1/G_N)$ mutual information between suitable boundary subregions, referred to as decision regions, as a necessary but not sufficient condition for bulk-only scattering processes, i.e., nonempty bulk scattering region $S_0$. Recently, Liu and Leutheusser proposed an enlarged bulk scattering region $S_E$ and conjectured that the non-emptiness of $S_E$ fully characterize…