.— Sex ratios in clutches of moorhens (Gallinula chloropus) in Britain were measured on 83 chicks using the sex‐linked CHD1 gene (Chromo‐helicase/ATPase‐DNA binding protein 1). Among birds, the ...female is the hetero‐gametic sex (Z and W chromosomes), and the male is homogametic (two copies of the Z chromosome). We report variation among the PCR‐amplified fragments of the CHD1Z, and the death of nearly all heterozygous male chicks (92%). In contrast, survivorship among females and homozygote males was 54–60%. Mortality in male heterozygotes was significantly higher than that of male homozygotes (P < 0.001). Chick and egg biometrics were not significantly different between these males. The CHD1Z was unlikely to be directly responsible but may have been hitchhiked by the causal gene(s). The observations appear to follow a classic underdominance (heterozygote inferiority) pattern, but raise the paradoxical question of why one form of the Z chromosome has not been fixed, as is expected from evolutionary theory. We discuss possible explanations and include a survey of British populations based on skin specimens.
Media coverage of the Child B case Entwistle, Vikki A; Watt, Ian S; Bradbury, Richard ...
BMJ,
06/1996, Letnik:
312, Številka:
7046
Journal Article
Recenzirano
Odprti dostop
The case of a girl with leukaemia, known as Child B, hit the headlines in March 1995 when her father refused to accept the advice of doctors who counselled against further treatment and took ...Cambridge and Huntingdon Health Authority to court for refusing to fund chemotherapy and a second bone transplant for her in the private sector. British national newspapers varied greatly in the way they covered the case. Some paid little attention to clinical considerations and presented the case as an example of rationing based on financial considerations. Their selective presentations meant that anyone reading just one newspaper would have received only limited and partial information. If members of the public are to praticipate in debates about treatment decisions and health care rationing, means other than the media will need to be found to inform and involve them.
Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates ...needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.
The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural ...network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization. Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains. The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.
Energy disaggregation algorithms decompose building-level energy data into device-level information. We conduct a head-to-head comparison of energy disaggregation techniques across multiple metrics ...and data sets. Our framework for analyzing the performance of a complete energy disaggregation system includes event detection, classification, and power assignment. We use receiver operating characteristics (ROCs) to evaluate event detection performance, and we introduce a technique to evaluate device-level event detection. We use confusion matrices to compare classification performance across several classifiers, and evaluate the resulting power assignments using several assignment metrics that are commonly used in the literature to demonstrate the varying strengths of the techniques that were considered. We apply this framework to several publicly available datasets and demonstrate how system performance varies with sampling frequency and the inclusion of reactive power. Our results suggest that (1) disaggregation performance varies considerably across data sets (2) increased data sampling rate improves disaggregation performance, and (3) additional features such as reactive power yields disaggregation performance improvements.
2006 Visualization Challenge winners Chatterjee, Rhitu
Science (American Association for the Advancement of Science),
2006-Sep-22, 2006-09-22, 20060922, Letnik:
313, Številka:
5794
Journal Article
Recenzirano
Science and the National Science Foundation announce the winners and honorable mentions in the categories of photography, illustration, informational graphics, noninteractive multimedia, and ...interactive multimedia in this year's Science and Engineering Visualization Challenge.
Molecular modeling was used evaluate conformational effects of side chain modifications to kainate and the pharmacological consequences of such modifications on binding to KA, NMDA, and AMPA ...receptors and to the high-affinity sodium-dependent glutamate transporter.
Molecular modeling was used to evaluate conformational effects of side chain modifications to kainate and the pharmacological consequences of such modifications on binding to KA, NMDA, and AMPA receptors and to the high-affinity sodium-dependent glutamate transporter.
Full-depth reclamation (FDR) is the technique of in-place recycling of the asphalt-bound layer of a pavement along with part of the underlying unbound layer to produce an improved base material. The ...objective was to develop a mix design system for FDR and evaluate the performance of designed reclaimed materials from the western part of Maine. Mixes were prepared in the laboratory, and samples were compacted with the Superpave® gyratory compactor. The samples were then tested for bulk specific gravity and resilient modulus. Samples of mixes prepared with asphalt emulsion, water, emulsion plus lime, emulsion plus cement, and emulsion plus lime and cement were also tested for their resilient moduli at different cure times and for their shear strengths. Rut tests were also conducted with the samples under water to evaluate the stripping potentials of the different mixes. The test results showed that maximum density and resilient modulus criteria can be used to select the optimum additive content for water and asphalt emulsion mixes. Comparison of performance testing results showed that mixes with additives develop strength faster and show significantly higher shear strength and stripping resistance than mixes with water only. For the materials tested, addition of lime and cement with asphalt emulsion appears to increase the rate of gain in strength and, hence, to result in faster curing and to increase the shear strength as well as resistance against moisture damage. It is recommended that FDR sections with asphalt emulsion, lime, and cement be constructed and evaluated for in-place performance.
Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs ...unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size. Due to their increased parallelism, they are up to 16 times faster at train and test time. Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.
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