Results 161 to 170 of about 2,913,313 (303)
Approximation in stochastic integer programming [PDF]
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate ...
Vlerk, Maarten H. van der, Stougie, Leen
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Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu +7 more
wiley +1 more source
LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties
Capacitated facility location problem (CFLP) is a classical combinatorial optimization problem that has various applications in operations research, theoretical computer science, and management science. In the CFLP, we have a potential facilities set and
Runjie Miao, Chenchen Wu, Jinjiang Yuan
doaj +1 more source
On Estimation Algorithms vs Approximation Algorithms.
In a combinatorial optimization problem, when given an input instance, one seeks a feasible solution that optimizes the value of the objective function. Many combinatorial optimization problems are NP-hard. A way of coping with NP-hardness is by considering approximation algorithms.
openaire +3 more sources
An Approximation Algorithm for #k-SAT
We present a simple randomized algorithm that approximates the number of satisfying assignments of Boolean formulas in conjunctive normal form. To the best of our knowledge this is the first algorithm which approximates #k-SAT for any k >= 3 within a running time that is not only non-trivial, but also significantly better than that of the currently ...
openaire +7 more sources
Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy
ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen +6 more
wiley +1 more source
Maximization of k-Submodular Function with d-Knapsack Constraints Over Sliding Window
Submodular function maximization problem has been extensively studied recently. A natural variant of submodular function is k-submodular function, which has many applications in real life, such as influence maximization and sensor placement problem.
Wenqi Wang +4 more
doaj +1 more source
ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian +6 more
wiley +1 more source
The Effect of Transformations on the Approximation of Univariate (Convex) Functions with Applications to Pareto Curves [PDF]
In the literature, methods for the construction of piecewise linear upper and lower bounds for the approximation of univariate convex functions have been proposed.We study the effect of the use of increasing convex or increasing concave transformations ...
Siem, A.Y.D. +2 more
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