Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the organic synthesis of increasingly efficient architectures over successive generations. Existing evolutionary synthesis processes, however, have allowed the mating of parent networks independent of architectural alignment, resulting in a mismatch of network structures. We present a preliminary study into the effects of architectural alignment during evolutionary synthesis using a gene tagging system. Surprisingly, the network architectures synthesized using the gene tagging approach resulted in slower decreases in performance accuracy and storage size; however, the resultant networks were comparable in size and performance accuracy to the non-gene tagging networks. Furthermore, we speculate that there is a noticeable decrease in network variability for networks synthesized with gene tagging, indicating that enforcing a like-with-like mating policy potentially restricts the exploration of the search space of possible network architectures.Comment: 5 page

Assessing Architectural Similarity in Populations of Deep Neural Networks

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations. Despite recent research showing the efficacy of multi-parent evolutionary synthesis, little has been done to directly assess architectural similarity between networks during the synthesis process for improved parent network selection. In this work, we present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results show that networks synthesized using architectural alignment (via gene tagging) maintain higher architectural similarities within each generation, potentially restricting the search space of highly efficient network architectures.Comment: 3 pages. arXiv admin note: text overlap with arXiv:1811.0796

Intensive alternatives to custody process evaluation of pilots in five areas

A qualitative process evaluation of five Intensive Alternative to Custody (IAC) pioneer areas was undertaken to assess implementation of IAC, identify approaches to implementation and capture the lessons learnt. The findings indicated that many of the persistent offenders (those with at least 29 prior convictions) targeted by pilots were positive about the IAC order. Although intensive, it provided order and stability, allowing them to move away from a criminal lifestyle. Sentencers welcomed the order as a viable alternative to custody. Probation staff and partners were equally positive about its efficacy. Only one in four IAC orders were revoked because requirements were breached, which suggests that the pilots had managed to engage many of the offenders

β-arrestin regulates estradiol membrane-initiated signaling in hypothalamic neurons.

Estradiol (E2) action in the nervous system is the result of both direct nuclear and membrane-initiated signaling (EMS). E2 regulates membrane estrogen receptor-α (ERα) levels through opposing mechanisms of EMS-mediated trafficking and internalization. While ß-arrestin-mediated mERα internalization has been described in the cortex, a role of ß-arrestin in EMS, which underlies multiple physiological processes, remains undefined. In the arcuate nucleus of the hypothalamus (ARH), membrane-initiated E2 signaling modulates lordosis behavior, a measure of female sexually receptivity. To better understand EMS and regulation of ERα membrane levels, we examined the role of ß-arrestin, a molecule associated with internalization following agonist stimulation. In the present study, we used an immortalized neuronal cell line derived from embryonic hypothalamic neurons, the N-38 line, to examine whether ß-arrestins mediate internalization of mERα. β-arrestin-1 (Arrb1) was found in the ARH and in N-38 neurons. In vitro, E2 increased trafficking and internalization of full-length ERα and ERαΔ4, an alternatively spliced isoform of ERα, which predominates in the membrane. Treatment with E2 also increased phosphorylation of extracellular-signal regulated kinases 1/2 (ERK1/2) in N-38 neurons. Arrb1 siRNA knockdown prevented E2-induced ERαΔ4 internalization and ERK1/2 phosphorylation. In vivo, microinfusions of Arrb1 antisense oligodeoxynucleotides (ODN) into female rat ARH knocked down Arrb1 and prevented estradiol benzoate-induced lordosis behavior compared with nonsense scrambled ODN (lordosis quotient: 3 ± 2.1 vs. 85.0 ± 6.0; p < 0.0001). These results indicate a role for Arrb1 in both EMS and internalization of mERα, which are required for the E2-induction of female sexual receptivity

Efficient Deep Feature Learning and Extraction via StochasticNets

Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data. One area worth exploring in feature learning and extraction using deep neural networks is efficient neural connectivity formation for faster feature learning and extraction. Motivated by findings of stochastic synaptic connectivity formation in the brain as well as the brain's uncanny ability to efficiently represent information, we propose the efficient learning and extraction of features via StochasticNets, where sparsely-connected deep neural networks can be formed via stochastic connectivity between neurons. To evaluate the feasibility of such a deep neural network architecture for feature learning and extraction, we train deep convolutional StochasticNets to learn abstract features using the CIFAR-10 dataset, and extract the learned features from images to perform classification on the SVHN and STL-10 datasets. Experimental results show that features learned using deep convolutional StochasticNets, with fewer neural connections than conventional deep convolutional neural networks, can allow for better or comparable classification accuracy than conventional deep neural networks: relative test error decrease of ~4.5% for classification on the STL-10 dataset and ~1% for classification on the SVHN dataset. Furthermore, it was shown that the deep features extracted using deep convolutional StochasticNets can provide comparable classification accuracy even when only 10% of the training data is used for feature learning. Finally, it was also shown that significant gains in feature extraction speed can be achieved in embedded applications using StochasticNets. As such, StochasticNets allow for faster feature learning and extraction performance while facilitate for better or comparable accuracy performances.Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:1508.0546

Minimising disjunctive information

In [5, 6], Belnap proposed a number of amendments to Rescher’s strategy for reasoning with maximal consistent subsets. More recently in [18], Horty explicitly endorsed Belnap’s amendment to address a related problem in handling inconsistent instructions and commands. In this paper, we’ll examine Belnap’s amendment and point out that Belnap’s suggestion in the use of conjunctive containment is open to the very objection he raised. We’ll propose a way out. The strategy turns on the use of First Degree Entailment in combination with Quine’s notion of prime implicate

Book review of Daniel Haybron’s Happiness: A Very Short Introduction

Book review of Daniel Haybron’s Happiness: A Very Short Introductio