Results 81 to 90 of about 2,159,110 (277)

Feasibility and Safety of High‐Dose Proton Re‐Irradiation in Recurrent Pediatric Central Nervous System Tumors: A Single‐Institution Retrospective Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Pediatric central nervous system (CNS) tumors often recur despite multimodality therapy. Although re‐irradiation (re‐RT) has historically been limited by concerns for severe late toxicities, modern techniques have renewed interest in this approach. Proton therapy provides dosimetric advantages that may enable curative re‐treatment with
Jin‐Ho Song   +15 more
wiley   +1 more source

Defining Roles in Pediatric Palliative Care: Perspectives From Oncology and Palliative Care Teams

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Early integration of pediatric palliative care (PPC) is associated with improved symptom management, quality of life, and healthcare utilization for children with cancer. Despite this, variation persists in how PPC is understood, operationalized, and integrated within pediatric oncology programs. In particular, ambiguity surrounding
Leeat Granek   +13 more
wiley   +1 more source

Feasibility and Preliminary Efficacy of Integrative Neuromuscular Training for Childhood Cancer Survivors: A Pilot Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Survivors of childhood acute lymphoblastic leukemia (ALL) often exhibit early deficits in muscle and movement competence, which can compromise long‐term health. Integrative neuromuscular training (INT), a multifaceted approach combining fundamental movement activities with strength exercises, may help address these deficits during ...
Anna Maria Markarian   +7 more
wiley   +1 more source

Looking elsewhere: improving variational Monte Carlo gradients by importance sampling

open access: yesMachine Learning: Science and Technology
Neural-network quantum states (NQSs) offer a powerful and expressive ansatz for representing quantum many-body wave functions. However, their training via Variational Monte Carlo (VMC) methods remains challenging.
Antoine Misery   +3 more
doaj   +1 more source

Variance Reduction Trends on "Boosted" Classifiers [PDF]

open access: yesJournal of Applied Mathematics and Decision Sciences, 2004
Ensemble classification techniques such as bagging, (Breiman, 1996a), boosting (Freund & Schapire, 1997) and arcing algorithms (Breiman, 1997) have received much attention in recent literature. Such techniques have been shown to lead to reduced classification error on unseen cases.
openaire   +1 more source

MCNP variance reduction overview [PDF]

open access: yes, 2006
The MCNP code is rich in variance reduction features. Standard variance reduction methods found in most Monte Carlo codes are available as well as a number of methods unique to MCNP. We discuss the variance reduction features presently in MCNP as well as new ones under study for possible inclusion in future versions of the code.
Hendricks, J. S., Booth, T. E.
openaire   +1 more source

Two Faces of NOTCH1 in Childhood Lymphoblastic T‐Cell Neoplasia: Prognostic Divergence of Mutational and Structural Aberrations

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT In pediatric patients, T‐cell lymphoblastic lymphoma (T‐LBL) survival exceeds 80%. Relapse remains associated with limited curative options. Frontline treatment is largely extrapolated from T‐cell acute lymphoblastic leukemia (T‐ALL) treatment, reflecting the ongoing debate, whether both entities represent distinct diseases or variants within ...
Marie C. Heider   +4 more
wiley   +1 more source

Analysis of the Variance Reduction in SVRG and a New Acceleration Method

open access: yesIEEE Access, 2018
Stochastic gradient descent is a popular method in large-scale optimization for machine learning but suffers from a slow convergence. In recent years, stochastic variance reduced gradient (SVRG) is proposed to remedy this problem.
Erxue Min, Jun Long, Jianjing Cui
doaj   +1 more source

Variance Reduction Techniques in Monte Carlo Methods [PDF]

open access: yes
Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs.
Kleijnen, Jack P.C.   +2 more
core   +1 more source

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