Tootfinder

aus der reihe "die wissenschaft hat festgestellt" heute: "#Katzen schlafen am liebsten auf der linken Seite" https://ne…

Had a really nice time today at Gay Day at the Yuba, an informal Northern California thing. Some 500-1000 guys come to swim at my favorite skinny dipping swimming hole. It was really nice, a fun and good vibes event.

Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning
Lloyd Pellatt, Fotios Drakopoulos, Shievanie Sabesan, Nicholas A. Lesica
https://arxiv.org/abs/2506.03088

Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning
The mapping from sound to neural activity that underlies hearing is highly non-linear. The first few stages of this mapping in the cochlea have been modelled successfully, with biophysical models built by hand and, more recently, with DNN models trained on datasets simulated by biophysical models. Modelling the auditory brain has been a challenge because central auditory processing is too complex for models to be built by hand, and datasets for training DNN models directly have not been availab…

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

Biobjective optimization with M-convex functions
Ellen H. Fukuda, Satoru Iwata, Itsuki Nakagawa
https://arxiv.org/abs/2507.23423 https://arxiv.org/pdf/2507…

Biobjective optimization with M-convex functions
In this paper, we deal with two ingredients that, as far as we know, have not been combined until now: multiobjective optimization and discrete convex analysis. First, we show that the entire Pareto optimal value set can be obtained in polynomial time for biobjective optimization problems with discrete convex functions, in particular, involving an M$^\natural$-convex function and a linear function with binary coefficients. We also observe that a more efficient algorithm can be obtained in the s…

I'm lucky in libraries, because of where I live.
My local library "system": Pikes Peak Library District is made up of 14 library locations and 3 mobile libraries that serve 500,000 people living in 2,070 square miles of El Paso County, CO, USA.
But ONLY if you live in this county.
A neighboring county has BUPKIS.
Queens Public Library (New York, USA) $50/year. Accepts non-US residents.

CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search
Zhenxin Li, Shuibing He, Jiahao Guo, Xuechen Zhang, Xian-He Sun, Gang Chen
https://arxiv.org/abs/2509.00365

CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) is a crucial problem in information retrieval and AI applications. Recently, there has been a surge of interest in graph-based ANNS algorithms due to their superior efficiency and accuracy. However, the repeated computation of distances in high-dimensional spaces constitutes the primary time cost of graph-based methods. To accelerate the search, we propose a novel routing strategy named CRouting, which bypasses unnecessary distance computations by expl…

Testing for large-dimensional covariance matrix under differential privacy
Shiwei Sang, Yicheng Zeng, Xuehu Zhu, Shurong Zheng
https://arxiv.org/abs/2506.02410

Testing for large-dimensional covariance matrix under differential privacy
The increasing prevalence of high-dimensional data across various applications has raised significant privacy concerns in statistical inference. In this paper, we propose a differentially private integrated statistic for testing large-dimensional covariance structures, enabling accurate statistical insights while safeguarding privacy. First, we analyze the global sensitivity of sample eigenvalues for sub-Gaussian populations, where our method bypasses the commonly assumed boundedness of data co…