The paper reports the enhanced effect of multi-frequency ultrasonic irradiation on cavitation yield. The cavitation yield is characterized by electrical conductivity determination, fluorescence intensity determination and iodine release method. Two-frequency (28 kHz/0.87 MHz) orthogonal continuous ultrasound, two-frequency (28 kHz/0.87 MHz) orthogonal pulse ultrasound and three-frequency (28 kHz/1.0 MHz/1.87 MHz) orthogonal continuous ultrasound have been used. It has been found that the combined irradiation of two or more frequencies of ultrasound can produce a significant increase in cavitation yield compared with single frequency irradiation. The possible mechanisms of the enhanced effect are briefly discussed.

No abstract

Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better explain how and why a user has personalized preference on an item. This work builds on non-IID learning to propose a neural user-item coupling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recommenders: neural matrix factorization and Google's Wide&Deep network.

No abstract

BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND METHODS:This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n ¼ 111), ependymoma (n ¼ 70), and pilocytic astrocytoma (n ¼ 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method x 2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P , .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review. ABBREVIATIONS: AUC ¼ area under the curve; AutoML ¼ automatic machine learning; CHSQ ¼ x 2 score; EP ¼ ependymoma; MB ¼ medulloblastoma; ML ¼ machine learning; PA ¼ pilocytic astrocytoma; TPOT ¼ Tree-Based Pipeline Optimization Tool A mong childhood malignancies, pediatric brain tumors are the second most common and the leading cause of death from solid tumors. 1,2 Posterior fossa tumors make up a disproportionate portion of brain tumors in the pediatric population, accounting for 54%-70% of tumors compared with ,20% in the adult population. 3 The most common subtypes of posterior fossa tumors among children are medulloblastoma (MB), pilocytic astrocytoma (PA), and ependymoma (EP). 4,5 Discrimination of these 3 malignancies is important due to the differing natural

No abstract

No abstract

No abstract