Tootfinder

Ciphertext-Only Attack on a Secure $k$-NN Computation on Cloud
Shyam Murthy, Santosh Kumar Upadhyaya, Srinivas Vivek
https://arxiv.org/abs/2403.09080 https…

Ciphertext-Only Attack on a Secure $k$-NN Computation on Cloud
The rise of cloud computing has spurred a trend of transferring data storage and computational tasks to the cloud. To protect confidential information such as customer data and business details, it is essential to encrypt this sensitive data before cloud storage. Implementing encryption can prevent unauthorized access, data breaches, and the resultant financial loss, reputation damage, and legal issues. Moreover, to facilitate the execution of data mining algorithms on the cloud-stored data, th…

Fast Assignment in Asset-Guarding Engagements using Function Approximation
Neelay Junnarkar, Emmanuel Sin, Peter Seiler, Douglas Philbrick, Murat Arcak
https://arxiv.org/abs/2404.08086

Fast Assignment in Asset-Guarding Engagements using Function Approximation
This letter considers assignment problems consisting of n pursuers attempting to intercept n targets. We consider stationary targets as well as targets maneuvering toward an asset. The assignment algorithm relies on an n x n cost matrix where entry (i, j) is the minimum time for pursuer i to intercept target j. Each entry of this matrix requires the solution of a nonlinear optimal control problem. This subproblem is computationally intensive and hence the computational cost of the assignment is…

Comment on 'Exact-corrected confidence interval for risk difference in noninferiority binomial trials'
A. Mart\'in Andr\'es, I. Herranz Tejedor
https://arxiv.org/abs/2404.08352

Comment on 'Exact-corrected confidence interval for risk difference in noninferiority binomial trials'
The article by Hawila & Berg (2023) that is going to be commented presents four relevant problems, apart from other less important ones that are also cited. First, the title is incorrect, since it leads readers to believe that the confidence interval defined is exact when in fact it is asymptotic. Second, contrary to what is assumed by the authors of the article, the statistic that they define is not monotonic in delta. But it is fundamental that this property is verified, as the authors themse…

A Stochastic GDA Method With Backtracking For Solving Nonconvex (Strongly) Concave Minimax Problems
Qiushui Xu, Xuan Zhang, Necdet Serhat Aybat, Mert G\"urb\"uzbalaban
https://arxiv.org/abs/2403.07806

A Stochastic GDA Method With Backtracking For Solving Nonconvex (Strongly) Concave Minimax Problems
We propose a stochastic GDA (gradient descent ascent) method with backtracking (SGDA-B) to solve nonconvex-(strongly) concave (NCC) minimax problems $\min_x \max_y \sum_{i=1}^N g_i(x_i)+f(x,y)-h(y)$, where $h$ and $g_i$ for $i = 1, \ldots, N$ are closed, convex functions, $f$ is $L$-smooth and $μ$-strongly concave in $y$ for some $μ\geq 0$. We consider two scenarios: (i) the deterministic setting where we assume one can compute $\nabla f$ exactly, and (ii) the stochastic setting where we have…

Comment on 'Exact-corrected confidence interval for risk difference in noninferiority binomial trials'
A. Mart\'in Andr\'es, I. Herranz Tejedor
https://arxiv.org/abs/2404.08352

Comment on 'Exact-corrected confidence interval for risk difference in noninferiority binomial trials'
The article by Hawila & Berg (2023) that is going to be commented presents four relevant problems, apart from other less important ones that are also cited. First, the title is incorrect, since it leads readers to believe that the confidence interval defined is exact when in fact it is asymptotic. Second, contrary to what is assumed by the authors of the article, the statistic that they define is not monotonic in delta. But it is fundamental that this property is verified, as the authors themse…

Let's just say that I find it hard to square that outcome with a process that was a “free and fair competition and on the basis of merit”: «Staff at Alan Turing Institute speak out after four men given top roles»
https://www.theguardian.com/science/2024/mar/11/staff-at-alan-turing-institute-speak-out-after-four-men-given-top-roles

A Stochastic GDA Method With Backtracking For Solving Nonconvex (Strongly) Concave Minimax Problems
Qiushui Xu, Xuan Zhang, Necdet Serhat Aybat, Mert G\"urb\"uzbalaban
https://arxiv.org/abs/2403.07806

A Stochastic GDA Method With Backtracking For Solving Nonconvex (Strongly) Concave Minimax Problems
We propose a stochastic GDA (gradient descent ascent) method with backtracking (SGDA-B) to solve nonconvex-(strongly) concave (NCC) minimax problems $\min_x \max_y \sum_{i=1}^N g_i(x_i)+f(x,y)-h(y)$, where $h$ and $g_i$ for $i = 1, \ldots, N$ are closed, convex functions, $f$ is $L$-smooth and $μ$-strongly concave in $y$ for some $μ\geq 0$. We consider two scenarios: (i) the deterministic setting where we assume one can compute $\nabla f$ exactly, and (ii) the stochastic setting where we have…

An adaptive acceleration scheme for phase-field fatigue computations
Jonas Heinzmann, Pietro Carrara, Marreddy Ambati, Amir Mohammad Mirzaei, Laura De Lorenzis
https://arxiv.org/abs/2404.07003

An adaptive acceleration scheme for phase-field fatigue computations
Phase-field models of fatigue are capable of reproducing the main phenomenology of fatigue behavior. However, phase-field computations in the high-cycle fatigue regime are prohibitively expensive, due to the need to resolve spatially the small length scale inherent to phase-field models and temporally the loading history for several millions of cycles. As a remedy, we propose a fully adaptive acceleration scheme based on the cycle jump technique, where the cycle-by-cycle resolution of an approp…

Longitudinal Mammogram Risk Prediction
Batuhan K. Karaman, Katerina Dodelzon, Gozde B. Akar, Mert R. Sabuncu
https://arxiv.org/abs/2404.19083 https://arxiv.org/pdf/2404.19083
arXiv:2404.19083v1 Announce Type: new
Abstract: Breast cancer is one of the leading causes of mortality among women worldwide. Early detection and risk assessment play a crucial role in improving survival rates. Therefore, annual or biennial mammograms are often recommended for screening in high-risk groups. Mammograms are typically interpreted by expert radiologists based on the Breast Imaging Reporting and Data System (BI-RADS), which provides a uniform way to describe findings and categorizes them to indicate the level of concern for breast cancer. Recently, machine learning (ML) and computational approaches have been developed to automate and improve the interpretation of mammograms. However, both BI-RADS and the ML-based methods focus on the analysis of data from the present and sometimes the most recent prior visit. While it is clear that temporal changes in image features of the longitudinal scans should carry value for quantifying breast cancer risk, no prior work has conducted a systematic study of this. In this paper, we extend a state-of-the-art ML model to ingest an arbitrary number of longitudinal mammograms and predict future breast cancer risk. On a large-scale dataset, we demonstrate that our model, LoMaR, achieves state-of-the-art performance when presented with only the present mammogram. Furthermore, we use LoMaR to characterize the predictive value of prior visits. Our results show that longer histories (e.g., up to four prior annual mammograms) can significantly boost the accuracy of predicting future breast cancer risk, particularly beyond the short-term. Our code and model weights are available at https://github.com/batuhankmkaraman/LoMaR.

Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Omar S. M. El Nahhas, Georg W\"olflein, Marta Ligero, Tim Lenz, Marko van Treeck, Firas Khader, Daniel Truhn, Jakob Nikolas Kather
https://arxiv.org/abs/2403.03891

Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regression-based DL has seen an increasing interest. Nonetheless, clinical decision making often requires a categorical outcome. Consequently, we developed a weakly-supervised joint multi-task Transformer architecture which has been trained and evaluated on four public patient cohorts for the prediction of two key predictive…