Results 51 to 60 of about 459,286 (299)
Pediatric Blood &Cancer, EarlyView.ABSTRACT Ongoing evidence indicates increased risk of sarcopenic obesity among children and young people (CYP) with acute lymphoblastic leukemia (ALL), often beginning early in treatment, persisting into survivorship. This review evaluates current literature on body composition in CYP with ALL during and after treatment.
Lina A. Zahed +5 more
wiley +1 more source
Image Completion with Hybrid Interpolation in Tensor Representation
Applied Sciences, 2020 The issue of image completion has been developed considerably over the last two decades, and many computational strategies have been proposed to fill-in missing regions in an incomplete image. When the incomplete image contains many small-sized irregular
Rafał Zdunek, Tomasz Sadowski
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study ...
Severin Kasser +6 more
wiley +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite their increased risk for functional impairment resulting from cancer and its treatments, few adolescents and young adults (AYAs) with a hematological malignancy receive the recommended or therapeutic dose of exercise per week during inpatient hospitalizations.
Jennifer A. Kelleher +8 more
wiley +1 more source
AULoRA: Anomaly Understanding With Low-Rank Adaptation for Zero-Shot Anomaly Detection
IEEE AccessZero-Shot Anomaly Detection (ZSAD) aims to identify anomalies in unseen categories or scenarios. Recently, Vision-Language Models (VLMs), most notably CLIP, have been utilized to enhance anomaly detection performance.
Seunghyun Oh +3 more
doaj +1 more source
WaRA: Wavelet Low Rank Adaptation
Parameter-efficient fine-tuning (PEFT) has gained widespread adoption across various applications. Among PEFT techniques, Low-Rank Adaptation (LoRA) and its extensions have emerged as particularly effective, allowing efficient model adaptation while significantly reducing computational overhead. However, existing approaches typically rely on global low-
Heidari, Moein +4 more
openaire +2 more sources
A Bayesian Interpretation of Adaptive Low-Rank Adaptation
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Motivated by the sensitivity-based importance score of the adaptive low-rank adaptation (AdaLoRA), we utilize more theoretically supported metrics, including the signal-to-noise ratio (SNR), along with the Improved Variational Online Newton (IVON) optimizer, for adaptive parameter budget allocation.
Chen, Haolin, Garner, Philip N.
openaire +2 more sources
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression [PDF]
, 2023 Yue Lv +5 more
openalex +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background B‐cell lymphoblastic lymphoma (B‐LBL) represents a rare variety of non‐Hodgkin lymphoma, with limited research on its biology, progression, and management. Methods A retrospective analysis was performed on the clinical characteristics of 256 patients aged ≤18 years who received treatment under the China Net Childhood Lymphoma (CNCL)‐
Zhijuan Liu +20 more
wiley +1 more source
Automatic pore characterization in SEM images of foams using a fine-tuned segment anything model
Materials & DesignIdentifying and analyzing pores in scanning electron microscopy (SEM) images of foams is a labor-intensive task, often requiring manual annotation that limits reproducibility and throughput.
Yung-Chen Cheng +4 more
doaj +1 more source