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I had been travelling in the mountains for months before I stumbled upon a practice I had not imagined existed

On-Line Learning Theory of Soft Committee Machines with Correlated Hidden Units - Steepest Gradient Descent and Natural Gradient Descent -

The permutation symmetry of the hidden units in multilayer perceptrons causes the saddle structure and plateaus of the learning dynamics in gradient learning methods. The correlation of the weight vectors of hidden units in a teacher network is thought to affect this saddle structure, resulting in a prolonged learning time, but this mechanism is still unclear. In this paper, we discuss it with regard to soft committee machines and on-line learning using statistical mechanics. Conventional gradient descent needs more time to break the symmetry as the correlation of the teacher weight vectors rises. On the other hand, no plateaus occur with natural gradient descent regardless of the correlation for the limit of a low learning rate. Analytical results support these dynamics around the saddle point.Comment: 7 pages, 6 figure

Sister

https://digitalcommons.library.umaine.edu/mmb-vp/6344/thumbnail.jp

Sister

https://digitalcommons.library.umaine.edu/mmb-vp/6345/thumbnail.jp

Systems biology of energetic and atomic costs in the yeast transcriptome, proteome, and metabolome

Proteins vary in their cost to the cell and natural selection may favour the use of proteins that are cheaper to produce. We develop a novel approach to estimate the amino acid biosynthetic cost based on genome-scale metabolic models, and directly investigate the effects of biosynthetic cost on transcriptomic, proteomic and metabolomic data in _Saccharomyces cerevisiae_. We find that our systems approach to formulating biosynthetic cost produces a novel measure that explains similar levels of variation in gene expression compared with previously reported cost measures. Regardless of the measure used, the cost of amino acid synthesis is weakly associated with transcript and protein levels, independent of codon usage bias. In contrast, energetic costs explain a large proportion of variation in levels of free amino acids. In the economy of the yeast cell, there appears to be no single currency to compute the cost of amino acid synthesis, and thus a systems approach is necessary to uncover the full effects of amino acid biosynthetic cost in complex biological systems that vary with cellular and environmental conditions

Optimisation of on-line principal component analysis

Different techniques, used to optimise on-line principal component analysis, are investigated by methods of statistical mechanics. These include local and global optimisation of node-dependent learning-rates which are shown to be very efficient in speeding up the learning process. They are investigated further for gaining insight into the learning rates' time-dependence, which is then employed for devising simple practical methods to improve training performance. Simulations demonstrate the benefit gained from using the new methods.Comment: 10 pages, 5 figure

The dynamics of matrix momentum

We analyse the matrix momentum algorithm, which provides an efficient approximation to on-line Newton's method, by extending a recent statistical mechanics framework to include second order algorithms. We study the efficacy of this method when the Hessian is available and also consider a practical implementation which uses a single example estimate of the Hessian. The method is shown to provide excellent asymptotic performance, although the single example implementation is sensitive to the choice of training parameters. We conjecture that matrix momentum could provide efficient matrix inversion for other second order algorithms

Globally optimal learning rates in multilayer neural networks

A method for calculating the globally optimal learning rate in on-line gradient-descent training of multilayer neural networks is presented. The method is based on a variational approach which maximizes the decrease in generalization error over a given time frame. We demonstrate the method by computing optimal learning rates in typical learning scenarios. The method can also be employed when different learning rates are allowed for different parameter vectors as well as to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule

Coming in Warm: Qualitative Study and Concept Map to Cultivate Patient‐Centered Empathy in Emergency Care

Background Increased empathy may improve patient perceptions and outcomes. No training tool has been derived to teach empathy to emergency care providers. Accordingly, we engaged patients to assist in creating a concept map to teach empathy to emergency care providers. Methods We recruited patients, patient caretakers and patient advocates with emergency department experience to participate in three separate focus groups (n = 18 participants). Facilitators guided discussion about behaviors that physicians should demonstrate in order to rapidly create trust, enhance patient perception that the physician understood the patient's point of view, needs, concerns, fears, and optimize patient/caregiver understanding of their experience. Verbatim transcripts from the three focus groups were read by the authors and by consensus, 5 major themes with 10 minor themes were identified. After creating a codebook with thematic definitions, one author reviewed all transcripts to a library of verbatim excerpts coded by theme. To test for inter‐rater reliability, two other authors similarly coded a random sample of 40% of the transcripts. Authors independently chose excerpts that represented consensus and strong emotional responses from participants. Results Approximately 90% of opinions and preferences fell within 15 themes, with five central themes: Provider transparency, Acknowledgement of patient's emotions, Provider disposition, Trust in physician, and Listening. Participants also highlighted the need for authenticity, context and individuality to enhance empathic communication. For empathy map content, patients offered example behaviors that promote perceptions of physician warmth, respect, physical touch, knowledge of medical history, explanation of tests, transparency, and treating patients as partners. The resulting concept map was named the “Empathy Circle”. Conclusions Focus group participants emphasized themes and tangible behaviors to improve empathy in emergency care. These were incorporated into the “Empathy Circle”, a novel concept map that can serve as the framework to teach empathy to emergency care providers

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison

Background: Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specific networks involving a few interacting transcription factors (TFs) and all of their target genes. Results: We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternative regulatory structures for each target gene. We use our methodology to identify targets of five TFs regulating Drosophila melanogaster mesoderm development. We find that confident predicted links between TFs and targets are significantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically significantly outperforms existing alternatives. Conclusions: Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations significantly increases the accuracy