Alpha MAML: Adaptive Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.Comment: 6th ICML Workshop on Automated Machine Learning (2019

A novel mutation of KIF11 in a child with 22q11.2 deletion syndrome associated with MCLMR

Microcephaly with or without chorioretinopathy, lymphedema, or mental retardation (MCLMR; OMIM 152950) is a rare autosomal dominantly inherited syndrome. Mutations in the kinesin family member 11 (KIF11) gene have been associated with this condition. Here, we report a de novo novel heterozygous missense mutation in exon 12 of the KIF11 gene [c.1402T>G; p.(Leu468Val)] in a boy with 22q11.2 microdeletion syndrome. His major features were microcephaly, ventricular septal defect, congenital lymphedema of the feet, and distinct facial appearance including upslanting palpebral fissures, a broad nose with rounded tip, anteverted nares, long philtrum with a thin upper lip, pointed chin, and prominent ears. His right eye was enucleated due to subretinal hemorrhage and retinal detachment at age 3 months. Lacunae of chorioretinal atrophy and the pale optic disc were present in the left eye. He also had a de novo 1.6-Mb microdeletion in the Di George/VCFS region of chromosome 22q11.2 in SNP array, which was confirmed by FISH analysis. In this study, for the first time, we describe the co-occurrence of a KIF11 mutation and 22q11.2 deletion syndrome in a patient with MCLMR

Health network mergers and hospital re-planning

This paper presents an integer programming formulation for the hospital re-planning problem which arises after hospital network mergers. The model finds the best re-allocation of resources among hospitals, the assignment of patients to hospitals and the service portfolio to minimize the system costs subject to quality and capacity constraints. An application in the Turkish hospital networks case is illustrated to show the implications of consolidation of health insurance funds on resource allocations and flow of patients in the system. © 2010 Operational Research Society Ltd. All rights reserved

KL Guided Domain Adaptation

Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training procedure often does not perform well, since it does not account for the change in the distribution. A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain. However, these approaches often require additional networks and/or optimizing an adversarial (minimax) objective, which can be very expensive or unstable in practice. To tackle this problem, we first derive a generalization bound for the target loss based on the training loss and the reverse Kullback-Leibler (KL) divergence between the source and the target representation distributions. Based on this bound, we derive an algorithm that minimizes the KL term to obtain a better generalization to the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples without any additional network or a minimax objective. This leads to a theoretically sound alignment method which is also very efficient and stable in practice. Experimental results also suggest that our method outperforms other representation-alignment approaches

Cytotoxic activities of certain medicinal plants on different cancer cell lines

Objectives: In recent years, the use of plants for the prevention and treatment of cancer is gaining more attention due to their diverse range of phytochemical constituents and fewer adverse effects. In this study, four medicinal plant species from the Kars province of Turkey were investigated for their cytotoxic potential against six different cancer cell lines and one normal cell line. Materials and Methods: MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-dipenyltetrazolium bromide] assay was performed to assess cytotoxic activity and apoptotic effect was determined using flow cytometry and caspase-3 analyses. Results: Significant cytotoxicity (≥70%) was observed with the leaf extract of Artemisia absinthium on A-549, CCC-221, K-562, MCF-7, PC-3 cells, whereas seed extracts caused significant cytotoxicity (≥70%) on CCC-221, K-562, MCF-7, PC-3 cells. Selective cytotoxicity was obtained with leaf extract on A-549 and K-562 cells; and with seed extract on K-562, MCF-7 and PC-3 cells compared with normal Beas-2B cells. The levels of cytotoxicity for both extracts were time- and dose-dependent at lower concentrations. Moreover, selective cytotoxicity (78%) was detected on A-549 cells with the seed extract of Plantago major. Cytotoxicity of extracts from Hyoscyamus niger and Amaranthus retrosa ranged between 10% and 30%. Conclusion: A. absinthium extracts and P. major seed extract have potential for development as therapeutic agents for cytotoxicity on certain cancer cells following further investigation. © Turk J Pharm Sci, Published by Galenos Publishing House

A systematic review of nutrition-based practices in prevention of hypertension among healthy youth

The aim of this systematic review was to analyze the results of observational and interventional research/studies on nutrition-based practices in the prevention of hypertension among healthy youth. The MEDLINE/PubMed database was searched using the key words, "hypertension," "nutrition/diet," "prevention" and "youth." Inclusion criteria were: 1) sample with a majority of adolescents, defined as 10-24 years of age, or findings for adolescents reported separately from other age groups; 2) primary research reports; 3) studies with normotensive participants; and 4) studies that focused on preventing hypertension/lowering blood pressure through at least one nutritional practice. Results of the analysis indicated that increased consumption of unsaturated fats, fruits, vegetables and low-fat dietary products, decreased consumption of dietary sodium and beverages containing caffeine, and breastfeeding were found to have preventive effects against high blood pressure in later years of life. The effects of training given during youth to encourage a healthy lifestyle and behavior changes based on diet and physical activity were also noted.publisher versio

Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework