Tootfinder

Toward a Consistent Definition of Holographic Entanglement Entropy in de Sitter Space
Shan-Ming Ruan, Yu-ki Suzuki
https://arxiv.org/abs/2508.14478 https://

Toward a Consistent Definition of Holographic Entanglement Entropy in de Sitter Space
We investigate a new definition of holographic entanglement entropy in the framework of static patch holography for de Sitter space. Using the replica trick and twist operator formalism, we derive an entropy functional in three-dimensional de Sitter space expressed through de Sitter Green's functions. Since the naive Ryu-Takayanagi prescription in de Sitter spacetime fails to satisfy strong subadditivity, we demand that our proposed formula be consistent with the fundamental entropic inequaliti…

Scavenger : Revisiting Space-Time Tradeoffs in Key-Value Separated LSM-trees
Jianshun Zhang, Fang Wang, Jiaxin Ou, Yi Wang, Ming Zhao, Sheng Qiu, Junxun Huang, Baoquan Li, Peng Fang, Dan Feng
https://arxiv.org/abs/2508.13935

Scavenger+: Revisiting Space-Time Tradeoffs in Key-Value Separated LSM-trees
Key-Value Stores (KVS) based on log-structured merge-trees (LSM-trees) are widely used in storage systems but face significant challenges, such as high write amplification caused by compaction. KV-separated LSM-trees address write amplification but introduce significant space amplification, a critical concern in cost-sensitive scenarios. Garbage collection (GC) can reduce space amplification, but existing strategies are often inefficient and fail to account for workload characteristics. Moreove…

iPhone Air teardown: clever use of space by placing the logic board above the 12.26 Wh battery, the same cell used in Apple's MagSafe Battery pack, and more (Elizabeth Chamberlain/iFixit News)
https://www.ifixit.com/News/113171/iphone-air-teardown

Apple’s Thinnest iPhone Still Stands Up to Repairs
To be honest, we were holding our breath for the iPhone Air. Thinner usually means flimsier, harder to fix, and more glued-down parts. But the iPhone Air proves…

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Peiming Li, Ziyi Wang, Yulin Yuan, Hong Liu, Xiangming Meng, Junsong Yuan, Mengyuan Liu
https://arxiv.org/abs/2508.14604

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scann…

Geodesic convexity and strengthened functional inequalities in submanifolds of Wasserstein space
Louis-Pierre Chaintron (DMA), Daniel Lacker
https://arxiv.org/abs/2508.13698 htt…

Geodesic convexity and strengthened functional inequalities in submanifolds of Wasserstein space
We study the geodesic convexity of various energy and entropy functionals restricted to (non-geodesically convex) submanifolds of Wasserstein spaces with their induced geometry. We prove a variety of convexity results by means of a simple general principle, which holds in the metric space setting, and which crucially requires no knowledge of the structure of geodesics in the submanifold: If the EVI gradient flow of a functional exists and leaves the submanifold invariant, then the restriction o…

Word Meanings in Transformer Language Models
Jumbly Grindrod, Peter Grindrod
https://arxiv.org/abs/2508.12863 https://arxiv.org/pdf/2508.12863

Word Meanings in Transformer Language Models
We investigate how word meanings are represented in the transformer language models. Specifically, we focus on whether transformer models employ something analogous to a lexical store - where each word has an entry that contains semantic information. To do this, we extracted the token embedding space of RoBERTa-base and k-means clustered it into 200 clusters. In our first study, we then manually inspected the resultant clusters to consider whether they are sensitive to semantic information. In …

Generalisation and benign over-fitting for linear regression onto random functional covariates
Andrew Jones, Nick Whiteley
https://arxiv.org/abs/2508.13895 https://

Generalisation and benign over-fitting for linear regression onto random functional covariates
We study theoretical predictive performance of ridge and ridge-less least-squares regression when covariate vectors arise from evaluating $p$ random, means-square continuous functions over a latent metric space at $n$ random and unobserved locations, subject to additive noise. This leads us away from the standard assumption of i.i.d. data to a setting in which the $n$ covariate vectors are exchangeable but not independent in general. Under an assumption of independence across dimensions, $4$-th…

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Lukas Brenner, Beatriz Dias, Robert Koenig
https://arxiv.org/abs/2509.14658

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Quantifying the accuracy of logical gates is paramount in approximate error correction, where perfect implementations are often unachievable with the available set of physical operations. To this end, we introduce a single scalar quantity we call the (composable) logical gate error. It captures both the deviation of the logical action from the desired target gate as well as leakage out of the code space. It is subadditive under successive application of gates, providing a simple means for analy…

End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments
Meng-Ping Lin, Enoch Hsin-Ho Huang, Shao-Yi Chien, Yu Tsao
https://arxiv.org/abs/2508.13576

End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments
The cochlear implant (CI) is a remarkable biomedical device that successfully enables individuals with severe-to-profound hearing loss to perceive sound by converting speech into electrical stimulation signals. Despite advancements in the performance of recent CI systems, speech comprehension in noisy or reverberant conditions remains a challenge. Recent and ongoing developments in deep learning reveal promising opportunities for enhancing CI sound coding capabilities, not only through replicat…

DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu, Qiang Li, Chih-Wei Chang, Richard LJ Qiu, Xiaofeng Yang
https://arxiv.org/abs/2508.14809

DINOv3 with Test-Time Training for Medical Image Registration
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.7…