A5103669029- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Advanced Bandit Algorithms Research
- Machine Learning and Algorithms
- Electrospun Nanofibers in Biomedical Applications
- Multi-Criteria Decision Making
- Machine Learning in Materials Science
- Membrane Separation and Gas Transport
- Metal-Organic Frameworks: Synthesis and Applications
- Bayesian Modeling and Causal Inference
- Stochastic Gradient Optimization Techniques
- Software Testing and Debugging Techniques
- Reinforcement Learning in Robotics
- Rough Sets and Fuzzy Logic
- Cognitive Science and Mapping
- Software Reliability and Analysis Research
- Nuclear reactor physics and engineering
- Bone Tissue Engineering Materials
- Advanced Optimization Algorithms Research
- Microbial Metabolic Engineering and Bioproduction
- Osteomyelitis and Bone Disorders Research
- Additive Manufacturing and 3D Printing Technologies
- Machine Learning and Data Classification
- Cytokine Signaling Pathways and Interactions
Nanjing General Hospital of Nanjing Military Command
2022-2024
East China Normal University
2021-2024
Xuzhou Medical College
2024
Jinling Institute of Technology
2024
Nanjing University
2014-2024
Hebei Yiling Hospital
2024
First Hospital of Lanzhou University
2024
Lanzhou University
2024
Southern Medical University
2022-2023
Iowa City Public Library
2022
Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due their costly evaluation. Building cheap surrogate models replace the expensive real has been proved be a practical way reduce number of evaluations. Supervised learning techniques from community machine have widely applied build either regressors, which approximate fitness values candidate solutions, or classifiers, estimate categories solutions. Considering characteristics data produced in...
Poly( l -lactic acid) (PLLA) is a widely used U.S. Food and Drug Administration–approved implantable biomaterial that also possesses strong piezoelectricity. However, the intrinsically low stability of its high-energy piezoelectric β phase random domain orientations associated with current synthesis approaches remain critical roadblock to practical applications. Here, we report an interfacial anchoring strategy for fabricating core/shell PLLA/glycine (Gly) nanofibers (NFs) by...
Organic impurities in compound libraries are known to often cause false-positive signals screening campaigns for new leads, but organic do not fully account all results. We discovered inorganic our library that can also positive a variety of targets and/or readout systems, including biochemical and biosensor assays. investigated depth the example zinc specific project retrospect various HTS screens at Roche propose straightforward counter screen using chelator TPEN rule out inhibition caused by zinc.
Many randomized heuristic derivative-free optimization methods share a framework that iteratively learns model for promising search areas and samples solutions from the model. This paper studies particular setting of such framework, where is implemented by classification discriminating good bad ones. allows general theoretical characterization, critical factors to are discovered. We also prove problems with Local Lipschitz continuity can be solved in polynomial time proper configurations...
Multi-objective (MO) optimization problems require simultaneously optimizing two or more objective functions. An MO algorithm needs to find solutions that reach different optimal balances of the functions, i.e., Pareto front, therefore, high dimensionality solution space can hurt much severer than single-objective optimization, which was little addressed in previous studies. This paper proposes a general, theoretically-grounded yet simple approach ReMO, scale current derivative-free...
The present study aimed to evaluate anti-rheumatoid arthritis (RA) effect of Lonicerin (LON), a safe compound with anti-inflammatory and immunomodulatory properties. Nevertheless, the exact role LON in RA remains elusive. In this test, anti-RA was evaluated collagen-induced (CIA) mouse model. Relevant parameters were measured during experiment; ankle tissue serum collected at end experiment for radiology, histopathology, inflammation analysis. ELISA, qRT-PCR, immunofluorescence, western blot...
Cognitive diagnosis assessment is a fundamental and crucial task for student learning. It models the student-exercise interaction, discovers students' proficiency levels on each knowledge attribute. In real-world intelligent education systems, generalization interpretability of cognitive methods are equal importance. However, most existing can hardly make best both worlds due to complicated interaction. To this end, paper proposes symbolic diagnosis~(SCD) framework simultaneously enhance...
Classification-based optimization is a recently developed framework for derivative-free optimization, which has shown to be effective non-convex problems with many local optima. This requires sample batch of solutions every update the search model. However, in reinforcement learning, direct policy often offers only sequential evaluation. Thus, classificationbased not efficient where have sampled sequentially. In this paper, we adapt classification-based by forming reused historical...
Abstract Effective antitumor agents with concurrent osteogenic properties are essential for comprehensive osteosarcoma (OS) treatment. However, the current clinical therapeutic strategies of OS fail to completely eradicate tumors while simultaneously encouraging bone formation. To address this issue, a switchable strategy dynamic ablation and static regeneration is developed by integrating piezoelectric BaTiO 3 (BTO) atomic‐thin Ti C 2 (TC) through Schottky heterojunction, resulting in...
Symbolic execution is a widely-used program analysis technique. It collects and solves path conditions to guide the traversing. However, due limitation of current constraint solvers, it difficult apply symbolic on programs with complex conditions, like nonlinear constraints function calls. In this paper, we propose new tool MLB handle such problem. Instead relying classical solving, in MLB, feasibility problems are transformed into optimization problems, by minimizing some dissatisfaction...
Treatment of bone defects remains crucial challenge for successful healing, which arouses great interests in designing and fabricating ideal biomaterials. In this regard, the present study focuses on developing a novel fluffy scaffold poly Lactide-co-glycolide (PLGA) composites with hydroxyapatite (HA) used defect repair rabbits. This PLGA/HA composite was fabricated by using multi-electro-spinning combined biomineralization technology. vitro analysis human marrow mesenchymal stem cells...
Simultaneous optimistic optimization (SOO) is a recently proposed global method with strong theoretical foundation. Previous studies have shown that SOO has good performance in low-dimensional problems, however, its unsatisfactory when the dimensionality high. This paper adapts random embedding to scaling SOO, resulting RESOO algorithm. We prove simple regret of depends only on effective dimension problem, while solution space. Empirically, some high-dimensional non-convex testing functions...
Derivative-free optimization has shown advantage in solving sophisticated problems such as policy search, when the environment is noise-free. Many real-world environments are noisy, where solution evaluations inaccurate due to noise. Noisy evaluation can badly injure derivative-free optimization, it may make a worse looks better. Sampling straightforward way reduce noise, while previous studies have that delay noise handling comparison time point (i.e., threshold selection) be helpful for...
Evolutionary algorithms (EAs), a large class of general purpose optimization inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their power statistical view EAs. By summarizing range EAs into sampling-and-learning framework, we that framework directly admits analysis on probable-absolute-approximate (PAA) query complexity. We particularly focus with learning subroutine...
As a kind of model-based optimization framework, the sampling-and-classification (SAC) algorithms, where model is specified to be classifier, has been recently studied in both theoretical foundation and algorithm implementation. However, previous work only SAC algorithms real domains. While significant progresses evolutionary have developed major discrete domains, it interesting understand also finite This paper studies (e,ó)-query complexity which measures how soon can an obtain solution...