280 research outputs found
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
Recovery of thermophilic campylobacter spp. in healthy and diarrhoeic pets by three culture methods and identification of the isolates by multiplex polymerase chain reaction (mpcr)
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
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