Tootfinder

Fast and length-independnt transport time supported by topological edge states in finite-size Su-Schieffer-Heeger chains
Yu-Han Chang, Nadia Daniela Rivera Torres, Santiago Figueroa Manrique, Raul A. Robles Robles, Vanna Chrismas Silalahi, Cen-Shawn Wu, Gang Wang, Giulia Marcucci, Laura Pilozzi, Claudio Conti, Ray-Kuang Lee, Watson Kuo
https://arxiv.org/abs/2511.19237 https://arxiv.org/pdf/2511.19237 https://arxiv.org/html/2511.19237
arXiv:2511.19237v1 Announce Type: new
Abstract: In order to transport information with topological protection, we explore experimentally the fast transport time using edge states in one-dimensional Su-Schrieffer-Heeger (SSH) chains. The transport time is investigated in both one- and two-dimensional models with topological non-trivial band structures. The fast transport is inherited with the wavefunction localization, giving a stronger effective coupling strength between the mode and the measurement leads. Also the transport time in one-dimension is independent of the system size. To verify the asertion, we implement a chain of split-ring resonators and their complementary ones with controllable hopping strengths. By performing the measurements on the group delay of non-trivially topological edge states with pulse excitations, the transport time between two edge states is directly observed with the chain length up to $20$. Along the route to harness topology to protect optical information, our experimental demonstrations provide a crucial guideline for utilizing photonic topological devices.
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Continuous Inverse Ambiguous Functions on Lie Groups
David Schmitz, Sadman Rahman, Anthony Kindness
https://arxiv.org/abs/2510.09958 https://arxiv.org/pdf/…

Continuous Inverse Ambiguous Functions on Lie Groups
In a previous study, the first author defines an inverse ambiguous function on a group $G$ to be a bijective function $f : G \to G$ satisfying the functional equation $f^{-1}(x) = f(x^{-1})$ for all $x \in G$. In this paper, we investigate the existence of continuous inverse ambiguous functions on classical Lie groups. In particular, we look at tori, elliptic curves over various fields, vector spaces, additive matrix groups, and multiplicative matrix groups.

Re: discourse about #FediSoWhite
I'm a white man. Was on Twitter throughout #BLM and gained an awful lot of free education from Black folks on there. That was the start of me consciously following diverse folks which is a strategy that's improved my life immensely.
Back on Twitter before the Muskening, there was a lot of diversity. Black Twitter was a thing, and not just first-world (anyone else remember "O jewa ke eng?"). When I went looking for people to follow to diversify my feed, I found them in abundance.
That's why it's so clearly false to me when people claim that the fediverse is secretly diverse, and why anyone making that claim sounds suspect to me. Sure there are a ton of great Black and other POC folks you can find on here, if you look hard. But it's nowhere near the levels of diversity and community that were on Twitter. Which you would know had you been following those people before, so now I have to assume you weren't, and wonder why you feel qualified to make statements about diversity even though you haven't made an effort to engage with diverse voices before?
Also, if you were actually following some of the excellent POC voices on here, you'd know that across different servers and interest groups, almost every group has had a discussion of #FediSoWhite at some point. If all the Black people you follow are independently talking about the lack of community and diversity here, you've either got to believe them or start putting on your clown makeup, and the later is absolutely a choice.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach
Xinyu Guan, Shaohua Zhang
https://arxiv.org/abs/2512.16927 https://mastoxiv.page/@arXiv_csDS_bot/115762062326187898
- SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
https://arxiv.org/abs/2512.16956 https://mastoxiv.page/@arXiv_csSE_bot/115762248476963893
- MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Saksham Sahai Srivastava, Haoyu He
https://arxiv.org/abs/2512.16962 https://mastoxiv.page/@arXiv_csCR_bot/115762140339109012
- Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Clas...
Faisal Ahmed
https://arxiv.org/abs/2512.16964 https://mastoxiv.page/@arXiv_eessIV_bot/115762196702065869
- Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, et al.
https://arxiv.org/abs/2512.16969 https://mastoxiv.page/@arXiv_csAI_bot/115762050529328276
- PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Kamer Ali Yuksel
https://arxiv.org/abs/2512.16970 https://mastoxiv.page/@arXiv_csAI_bot/115762054461584205
- A Women's Health Benchmark for Large Language Models
Elisabeth Gruber, et al.
https://arxiv.org/abs/2512.17028 https://mastoxiv.page/@arXiv_csCL_bot/115762049873946945
- Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
https://arxiv.org/abs/2512.17075 https://mastoxiv.page/@arXiv_csCL_bot/115762077400293945
- Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
https://arxiv.org/abs/2512.17127 https://mastoxiv.page/@arXiv_statML_bot/115762251753966702
- Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Huixin Zhan
https://arxiv.org/abs/2512.17146 https://mastoxiv.page/@arXiv_csCR_bot/115762318582013305
- Application of machine learning to predict food processing level using Open Food Facts
Arora, Chauhan, Rana, Aditya, Bhagat, Kumar, Kumar, Semar, Singh, Bagler
https://arxiv.org/abs/2512.17169 https://mastoxiv.page/@arXiv_qbioBM_bot/115762302873829397
- Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
Sandeep Neela
https://arxiv.org/abs/2512.17185 https://mastoxiv.page/@arXiv_qfinRM_bot/115762275982224870
- Do Foundational Audio Encoders Understand Music Structure?
Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
https://arxiv.org/abs/2512.17209 https://mastoxiv.page/@arXiv_csSD_bot/115762341541572505
- CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu, Zhicheng Jiao, Bin Jing, Quan Wang
https://arxiv.org/abs/2512.17213 https://mastoxiv.page/@arXiv_csCV_bot/115762574180736975
- Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Fun...
Deriyan Senjaya, Stephen Ekaputra Limantoro
https://arxiv.org/abs/2512.17245 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115762447037143855
- AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
https://arxiv.org/abs/2512.17251 https://mastoxiv.page/@arXiv_csCR_bot/115762396593872943
- Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, Zheli Liu
https://arxiv.org/abs/2512.17254 https://mastoxiv.page/@arXiv_csCR_bot/115762402470985707
- Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling A...
Abhivansh Gupta
https://arxiv.org/abs/2512.17259 https://mastoxiv.page/@arXiv_csMA_bot/115762225538364939
- Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan
https://arxiv.org/abs/2512.17277 https://mastoxiv.page/@arXiv_csIR_bot/115762214396869930
- LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass, Zheng-Hua Tan
https://arxiv.org/abs/2512.17281 https://mastoxiv.page/@arXiv_csSD_bot/115762361858560703
- Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose
https://arxiv.org/abs/2512.17340 https://mastoxiv.page/@arXiv_statME_bot/115762446402738033
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Scalar-tensor theories in the Lyra geometry: Invariance under local transformations of length units and the Jordan-Einstein frame conundrum
E. C. Valad\~ao, Felipe Sobrero, Santiago Esteban Perez Bergliaffa
https://arxiv.org/abs/2510.08433

Scalar-tensor theories in the Lyra geometry: Invariance under local transformations of length units and the Jordan-Einstein frame conundrum
The Lyra geometry provides an interesting approach to develop purely geometrical scalar-tensor theories due to the natural presence of the Lyra scale function. This paper explores further the scale function source term to construct a theory on Lyra manifolds which contains proper generalizations of both Brans-Dicke gravity and the Einstein-Gauss-Bonnet scalar-tensor theory. It is shown that the symmetry group of gravitational theories on the Lyra geometry comprises not only coordinate transform…

ILD-VIT: A Unified Vision Transformer Architecture for Detection of Interstitial Lung Disease from Respiratory Sounds
Soubhagya Ranjan Hota, Arka Roy, Udit Satija
https://arxiv.org/abs/2510.11458

ILD-VIT: A Unified Vision Transformer Architecture for Detection of Interstitial Lung Disease from Respiratory Sounds
Interstitial lung disease (ILD) represents a group of restrictive chronic pulmonary diseases that impair oxygen acquisition by causing irreversible changes in the lungs such as fibrosis, scarring of parenchyma, etc. ILD conditions are often diagnosed by various clinical modalities such as spirometry, high-resolution lung imaging techniques, crackling respiratory sounds (RSs), etc. In this letter, we develop a novel vision transformer (VIT)-based deep learning framework namely, ILD-VIT, to detec…