Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE

On acoustic scattering by a shell-covered seafloor

Author Posting. © Acoustical Society of America, 2000. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 108 (2000): 551-555, doi:10.1121/1.429585.Acoustic scattering by the seafloor is sometimes influenced, if not dominated, by the presence of discrete volumetric objects such as shells. A series of measurements of target strength of a type of benthic shelled animal and associated scattering modeling have recently been completed (Stanton et al., "Acoustic scattering by benthic and planktonic shelled animals," J. Acoust. Soc. Am., this issue). The results of that study are used herein to estimate the scattering by the seafloor with a covering of shells at high acoustic frequencies. A simple formulation is derived that expresses the area scattering strength of the seafloor in terms of the average reduced target strength or material properties of the discrete scatterers and their packing factor (where the reduced target strength is the target strength normalized by the geometric cross section of the scatterers and the averaging is done over orientation and/or a narrow range of size or frequency). The formula shows that, to first order, the backscattering at high acoustic frequencies by a layer of shells (or other discrete bodies such as rocks) depends principally upon material properties of the objects and packing factor and is independent of size and acoustic frequency. Estimates of area scattering strength using this formula and measured values of the target strength of shelled bodies from Stanton et al. (this issue) are close to or consistent with observed area scattering strengths due to shell-covered seafloors published in other papers.This research was supported by the U.S. Office of Naval Research Grant No. N00014-95-1-0287

Excitonic effects on coherent phonon dynamics in single wall carbon nanotubes

We discuss how excitons can affect the generation of coherent radial breathing modes in ultrafast spectroscopy of single wall carbon nanotubes. Photoexcited excitons can be localized spatially and give rise to a spatially distributed driving force in real space which involves many phonon wavevectors of the exciton-phonon interaction. The equation of motion for the coherent phonons is modeled phenomenologically by the Klein-Gordon equation, which we solve for the oscillation amplitudes as a function of space and time. By averaging the calculated amplitudes per nanotube length, we obtain time-dependent coherent phonon amplitudes that resemble the homogeneous oscillations that are observed in some pump-probe experiments. We interpret this result to mean that the experiments are only able to see a spatial average of coherent phonon oscillations over the wavelength of light in carbon nanotubes and the microscopic details are averaged out. Our interpretation is justified by calculating the time-dependent absorption spectra resulting from the macroscopic atomic displacements induced by the coherent phonon oscillations. The calculated coherent phonon spectra including excitonic effects show the experimentally observed symmetric peaks at the nanotube transition energies in contrast to the asymmetric peaks that would be obtained if excitonic effects were not included.Comment: submitted to Phys. Rev. B on 7 May 2013, revised on 17 July and 13 August 2013, published 30 August 201

Kernel Matrix-Based Heuristic Multiple Kernel Learning

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods

Liquid Phase Electrochemistry at Ultralow Temperatures

Fluid electrolyte solutions based on mixtures of butyronitrile (PrCN) and ethyl chloride (EtCl) with or as electrolyte freeze below −180°C and provide excellent media for cryogenic electrochemical experiments. A 1:2 mixture of PrCN and EtCl exhibits the best combination of freezing point and ionic conductivity for ultralow temperature electrochemistry. Diffusion coefficients for bis(pentamethylcyclopentadienyl) iron are measurable by potential step chronoamperometry down to −160°C using a conventionally sized electrode, but the resistivity of the solvent mixture is such that potential sweep voltammetry benefits from the use of microdisk (10 and 25 μm diam Pt) or microband (0.2 μm wide Au) electrodes. Voltammetry at a chemically modified electrode down to −170°C is presented for the case of thin films

The relationship between the piriformis muscle, low back pain, lower limb injuries and motor control training among elite football players

Objectives: Australian Football League (AFL) players have a high incidence of back injuries. Motor control training to increase lumbopelvic neuromuscular control has been effective in reducing low back pain (LBP) and lower limb injuries in elite athletes. Control of pelvic and femoral alignment during functional activity involves the piriformis muscle. This study investigated (a) the effect of motor control training on piriformis muscle size in AFL players, with and without LBP, during the playing season, and (b) whether there is a relationship between lower limb injury and piriformis muscle size. Design: Stepped-wedge intervention. Methods: 46 AFL players participated in a motor control training programme consisting of two 30. min sessions per week over 7-8 weeks, delivered across the season as a randomised 3 group single-blinded stepped-wedge design. Assessment of piriformis muscle cross-sectional area (CSA) involved magnetic resonance imaging (MRI) at 3 time points during the season. Assessment of LBP consisted of player interview and physical examination. Injury data were obtained from club records. Results: An interaction effect for Time, Intervention Group and LBP group (F=3.7, p=0.03) was found. Piriformis muscle CSA showed significant increases between Times 1 and 2 (F=4.24, p=0.046), and Times 2 and 3 (F=8.59, p=0.006). Players with a smaller increase in piriformis muscle CSA across the season had higher odds of sustaining an injury (OR. =1.08). Conclusions: Piriformis muscle size increases across the season in elite AFL players and is affected by the presence of LBP and lower limb injury. Motor control training positively affects piriformis muscle size in players with LBP