Tootfinder

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
Zhaolong Wu, Pu Luo, Jason Pui Yin Cheung, Teng Zhang
https://arxiv.org/abs/2509.11645

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
This study presents the first comprehensive evaluation of Multimodal Large Language Models (MLLMs) for Adolescent Idiopathic Scoliosis (AIS) self-management. We constructed a database of approximately 3,000 anteroposterior X-rays with diagnostic texts and evaluated five MLLMs through a `Divide and Conquer' framework consisting of a visual question-answering task, a domain knowledge assessment task, and a patient education counseling assessment task. Our investigation revealed limitations of MLL…

Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Yinzheng Zhao, Zhihao Zhao, Rundong Jiang, Louisa Sackewitz, Quanmin Liang, Mathias Maier, Daniel Zapp, Peter Charbel Issa, Mohammad Ali Nasseri
https://arxiv.org/abs/2509.09227

Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Purpose: To introduce novel dynamic structural parameters and evaluate their integration within a multimodal deep learning (DL) framework for predicting postoperative visual recovery in idiopathic full-thickness macular hole (iFTMH) patients. Methods: We utilized a publicly available longitudinal OCT dataset at five stages (preoperative, 2 weeks, 3 months, 6 months, and 12 months). A stage specific segmentation model delineated related structures, and an automated pipeline extracted quantitativ…

Benchmarking Classical, Machine Learning, and Bayesian Survival Models for Clinical Prediction
Irving G\'omez-M\'endez, Sivakorn Phromsiri, Ittiphat Kijpaisansak, Settawut Chaithurdthum
https://arxiv.org/abs/2509.10073

Benchmarking Classical, Machine Learning, and Bayesian Survival Models for Clinical Prediction
Survival analysis is a statistical framework for modeling time-to-event data, particularly valuable in healthcare for predicting outcomes like patient discharge or recurrence. This study implements and compares several survival models - including Weibull, Weibull AFT, Weibull AFT with Gamma Frailty, Cox Proportional Hazards (CoxPH), Random Survival Forest (RSF), and DeepSurv - using a publicly available breast cancer dataset. This study aims to benchmark classical, machine learning, and Bayesia…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Joe Harriott:
🎵 Akee Blues
#JoeHarriott
https://open.spotify.com/track/4IdVTPDW9Ik83a9Vt6wi8Q

🇺🇦 #NowPlaying on #BBC6Music's #HuwStephens
Happy Mondays:
🎵 Kinky Afro
#HappyMondays
https://jimmyelsanto.bandcamp.com/track/happy-mondays-kinky-afro-remix-by-jimmy-el-santo
https://open.spotify.com/track/2ItPID4dgSL1t1ZbZCrg5S

Determination of ground states of one-dimensional quantum systems using the cluster iTEBD method
Tao Yang, Rui Wang, Z. Y. Xie, Baigeng Wang
https://arxiv.org/abs/2508.21405 htt…

Determination of ground states of one-dimensional quantum systems using the cluster iTEBD method
Within the framework of imaginary-time evolution for matrix product states, we introduce a cluster version of the infinite time-evolving block decimation algorithm for simulating quantum many-body systems, addressing the computational accuracy challenges in strongly correlated physics. By redefining the wave-function ansatz to incorporate multiple physical degrees of freedom, we enhance the representation of entanglement, thereby improving the accuracy of the ground states. Utilizing the Trotte…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Joe Harriott:
🎵 Akee Blues
#JoeHarriott
https://open.spotify.com/track/4IdVTPDW9Ik83a9Vt6wi8Q

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Georgia Maq:
🎵 Slightly Below the Middle
#GeorgiaMaq
https://open.spotify.com/track/4iCHY1fIDHb1tJmJXr6iCE

Efficient DFT of Zadoff-Chu Sequences using lmFH Pattern
Fanping Du
https://arxiv.org/abs/2507.23200 https://arxiv.org/pdf/2507.23200

Efficient DFT of Zadoff-Chu Sequences using lmFH Pattern
Having established that Zadoff-Chu (ZC) sequences are inherently linear micro-frequency hopping (lmFH) symbols, this paper first presents an intuitive and visual exposition of the computation of the DFT and IDFT of ZC sequences using the lmFH pattern. This yields interesting results. Subsequently, an alternative form for computing the cumulative sum of ZC sequences using the Generalized Quadratic Gauss Sum is introduced. Furthermore, building on the micro-frequency hopping (mFH) concept, this p…

A Dual-Feature Extractor Framework for Accurate Back Depth and Spine Morphology Estimation from Monocular RGB Images
Yuxin Wei, Yue Zhang, Moxin Zhao, Chang Shi, Jason P. Y. Cheung, Teng Zhang, Nan Meng
https://arxiv.org/abs/2507.22691

A Dual-Feature Extractor Framework for Accurate Back Depth and Spine Morphology Estimation from Monocular RGB Images
Scoliosis is a prevalent condition that impacts both physical health and appearance, with adolescent idiopathic scoliosis (AIS) being the most common form. Currently, the main AIS assessment tool, X-rays, poses significant limitations, including radiation exposure and limited accessibility in poor and remote areas. To address this problem, the current solutions are using RGB images to analyze spine morphology. However, RGB images are highly susceptible to environmental factors, such as lighting…