PonyGE2: Grammatical Evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.Comment: 8 pages, 4 figures, submitted to the 2017 GECCO Workshop on Evolutionary Computation Software Systems (EvoSoft

Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation

The neural transducer is an end-to-end model for automatic speech recognition (ASR). While the model is well-suited for streaming ASR, the training process remains challenging. During training, the memory requirements may quickly exceed the capacity of state-of-the-art GPUs, limiting batch size and sequence lengths. In this work, we analyze the time and space complexity of a typical transducer training setup. We propose a memory-efficient training method that computes the transducer loss and gradients sample by sample. We present optimizations to increase the efficiency and parallelism of the sample-wise method. In a set of thorough benchmarks, we show that our sample-wise method significantly reduces memory usage, and performs at competitive speed when compared to the default batched computation. As a highlight, we manage to compute the transducer loss and gradients for a batch size of 1024, and audio length of 40 seconds, using only 6 GB of memory.Comment: 5 pages, 4 figures, 1 table, 1 algorith

State tomography of capacitively shunted phase qubits with high fidelity

We introduce a new design concept for superconducting quantum bits (qubits) in which we explicitly separate the capacitive element from the Josephson tunnel junction for improved qubit performance. The number of two-level systems (TLS) that couple to the qubit is thereby reduced by an order of magnitude and the measurement fidelity improves to 90%. This improved design enables the first demonstration of quantum state tomography with superconducting qubits using single shot measurements.Comment: submitted to PR

SNX10 gene mutation leading to osteopetrosis with dysfunctional osteoclasts

Acknowledgements We sincerely thank the patients and family members who participated in this study. We would also like to thank Stefan Esher, Umeå University, for help with genealogy, and Anna Westerlund for excellent technical assistance. This work was supported by grants from the FOU, at the Umeå university hospital, and the Medical Faculty at Umeå University. The work at University of Gothenburg was supported by grants from The Swedish Research Council, the Swedish Rheumatism Association, the Royal 80-Year Fund of King Gustav V, ALF/LUA research grant from Sahlgrenska University Hospital in Gothenburg and the Lundberg Foundation. The work at the University of Gothenburg and the University of Aberdeen was supported by Euroclast, a Marie Curie FP7-People-2013-ITN: # 607446.Peer reviewedPublisher PD

Speech production knowledge in automatic speech recognition

Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, we provide a survey of a growing body of work in which such representations are used to improve automatic speech recognition

Coreceptor Choice and T Cell Depletion by R5, X4, and R5X4 HIV-1 Variants in CCR5-Deficient (CCR5Δ32) and Normal Human Lymphoid Tissue

AbstractCoreceptor utilization by HIV-1 is an important determinant of pathogenesis. However, coreceptor selectivity is defined in vitro, while in vivo critical pathogenic events occur in lymphoid tissues. Using pharmacological inhibitors, we recently provided evidence that coreceptor selectivity by the R5X4 dual-tropic isolate 89.6 was more restricted in ex vivo infected lymphoid tissue than in vitro [S. Glushakova, Y. Yi, J. C. Grivel, A. Singh, D. Schols, E. De Clercq, R. G. Collman, and L. Margolis (1999). J. Clin. Invest. 104, R7–R11]. Here we extend those observations using CCR5-deficient (CCR5Δ32) lymphoid tissue as well as additional primary isolates. We definitively show that neither CCR5 nor secondary coreceptors used in vitro mediate 89.6 infection in lymphoid tissue. We also demonstrate that restricted coreceptor use in lymphoid tissue ex vivo compared with in vitro utilization occurs with other dual-tropic primary isolates and is not unique to 89.6. For all strains tested that are dual tropic in vitro, severe CD4 T cell depletion in lymphoid tissue correlated with preferential CXCR4 use in this ex vivo system