5 research outputs found
Enhancing the success rates by performing pooling decisions adjacent to the output layer
Learning classification tasks of (2^nx2^n) inputs typically consist of \le n
(2x2) max-pooling (MP) operators along the entire feedforward deep
architecture. Here we show, using the CIFAR-10 database, that pooling decisions
adjacent to the last convolutional layer significantly enhance accuracy success
rates (SRs). In particular, average SRs of the advanced VGG with m layers
(A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m=6, 8,
14, 13, and 16, respectively. The results indicate A-VGG8s' SR is superior to
VGG16s', and that the SRs of A-VGG13 and A-VGG16 are equal, and comparable to
that of Wide-ResNet16. In addition, replacing the three fully connected (FC)
layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation
FC layers, yielded similar SRs. These significantly enhanced SRs stem from
training the most influential input-output routes, in comparison to the
inferior routes selected following multiple MP decisions along the deep
architecture. In addition, SRs are sensitive to the order of the
non-commutative MP and average pooling operators adjacent to the output layer,
varying the number and location of training routes. The results call for the
reexamination of previously proposed deep architectures and their SRs by
utilizing the proposed pooling strategy adjacent to the output layer.Comment: 27 pages, 3 figures, 1 table and Supplementary Informatio
The mechanism underlying successful deep learning
Deep architectures consist of tens or hundreds of convolutional layers (CLs)
that terminate with a few fully connected (FC) layers and an output layer
representing the possible labels of a complex classification task. According to
the existing deep learning (DL) rationale, the first CL reveals localized
features from the raw data, whereas the subsequent layers progressively extract
higher-level features required for refined classification. This article
presents an efficient three-phase procedure for quantifying the mechanism
underlying successful DL. First, a deep architecture is trained to maximize the
success rate (SR). Next, the weights of the first several CLs are fixed and
only the concatenated new FC layer connected to the output is trained,
resulting in SRs that progress with the layers. Finally, the trained FC weights
are silenced, except for those emerging from a single filter, enabling the
quantification of the functionality of this filter using a correlation matrix
between input labels and averaged output fields, hence a well-defined set of
quantifiable features is obtained. Each filter essentially selects a single
output label independent of the input label, which seems to prevent high SRs;
however, it counterintuitively identifies a small subset of possible output
labels. This feature is an essential part of the underlying DL mechanism and is
progressively sharpened with layers, resulting in enhanced signal-to-noise
ratios and SRs. Quantitatively, this mechanism is exemplified by the VGG-16,
VGG-6, and AVGG-16. The proposed mechanism underlying DL provides an accurate
tool for identifying each filter's quality and is expected to direct additional
procedures to improve the SR, computational complexity, and latency of DL.Comment: 33 pages, 8 figure
Nuclear spin effects in biological processes
Traditionally, nuclear spin is not considered to affect biological processes. Recently, this has changed as isotopic fractionation that deviates from classical mass dependence was reported both in vitro and in vivo. In these cases, the isotopic effect correlates with the nuclear magnetic spin. Here, we show nuclear spin effects using stable oxygen isotopes (16O, 17O, and 18O) in two separate setups: an artificial dioxygen production system and biological aquaporin channels in cells. We observe that oxygen dynamics in chiral environments (in particular its transport) depend on nuclear spin, suggesting future applications for controlled isotope separation to be used, for instance, in NMR. To demonstrate the mechanism behind our findings, we formulate theoretical models based on a nuclear-spin-enhanced switch between electronic spin states. Accounting for the role of nuclear spin in biology can provide insights into the role of quantum effects in living systems and help inspire the development of future biotechnology solutions