Frequency-place compression and expansion in cochlear implant listeners

In multichannel cochlear implants, low frequency information is delivered to apical cochlear locations while high frequency information is delivered to more basal locations, mimicking the normal acoustic tonotopic organization of the auditory nerves. In clinical practice, little attention has been paid to the distribution of acoustic input across the electrodes of an individual patient that might vary in terms of spacing and absolute tonotopic location. In normal-hearing listeners, Ba?kent and Shannon (J. Acoust. Soc. Am. 113, 2003) simulated implant signal processing conditions in which the frequency range assigned to the array was systematically made wider or narrower than the simulated stimulation range in the cochlea, resulting in frequency-place compression or expansion, respectively. In general, the best speech recognition was obtained when the input acoustic information was delivered to the matching tonotopic place in the cochlea with least frequency-place distortion. The present study measured phoneme and sentence recognition scores with similar frequency-place manipulations in six Med-El Combi 40+ implant subjects. Stimulation locations were estimated using the Greenwood mapping function based on the estimated electrode insertion depth. Results from frequency-place compression and expansion with implants were similar to simulation results, especially for postlingually deafened subjects, despite the uncertainty in the actual stimulation sites of the auditory nerves. The present study shows that frequency-place mapping is an important factor in implant performance and an individual implant patient's map could be optimized with functional tests using frequency-place manipulations.     

Computationally Efficient Exact Calculation of Kernel Density Derivatives

Machine learning research related to the derivatives of the kernel density estimator has received limited attention compared to the density estimator itself. This is despite of the general consensus that most of the important features of a data distribution, such as modes, curvature or even cluster structure, are characterized by its derivatives. In this paper we present a computationally efficient algorithm to calculate kernel density estimates and their derivatives for linearly separable kernels, with significant savings especially for high dimensional data and higher order derivatives. It significantly reduces the number of operations (multiplications and derivative evaluations) to calculate the estimates, while keeping results exact (i.e. no approximations are involved). The main idea is that the calculation of multivariate separable kernels and their derivatives, such as the gradient vector and the Hessian matrix involves significant number of redundant operations that can be eliminated using the chain rule. A tree-based algorithm that calculates exact kernel density estimate and derivatives in the most efficient fashion is presented with the particular focus being on optimizing kernel evaluations for individual data pairs. In contrast, most approaches in the literature resort to approximations of functions or downsampling. Overall computational savings of the presented method could be further increased by incorporating such approximations, which aim to reduce the number of pairs of data considered. The theoretical computational complexity of the tree-based and direct methods that perform all multiplications are compared. In experimental results, calculating separable kernels and their derivatives is considered, as well as a measure that evaluates how close a point is to the principal curve of a density, which employs first and second derivatives. These results indicate considerable improvement in computational complexity, hence time over the direct approach.

     

Surfactant-induced healing of tough hydrogels formed via hydrophobic interactions

Different reversible molecular interactions have been used in the past few years to generate self-healing in synthetic hydrogels. However, self-healing hydrogels synthesized so far suffer from low mechanical strength which may limit their use in any stress-bearing applications. Here, we present a simple technique to heal mechanically strong polyacrylamide hydrogels formed via hydrophobic interactions between stearyl groups. A complete healing in the hydrogels was achieved by the treatment of the damaged areas with an aqueous solution of wormlike sodium dodecyl sulfate micelles. The micelles in the healing agent solubilize the hydrophobes in the cut surfaces, so that they easily find their partners in the other cut surface due to the hydrophobic interactions. Surfactant-induced healing produces high toughness (～1 MPa) gels withstanding 150 kPa of stress at a deformation ratio of 1,100 %. The healing technique developed here is generally applicable to the physical gels formed by hydrophobic associations.     

Spatio-spectral analyses of electromagnetic wave energy absorption and heating effect

This study presents a theoretical analysis method to calculate electromagnetic (EM) wave power absorption spectrum of materials by using attenuation coefficients. The heating effect of EM waves is modeled to analyze spectral distribution of temperature rises inside material body as a result of EM wave power absorption. These analyses are very useful for the investigation of electromagnetic wave-material interaction on the bases of electro-physical material parameters (permittivity, permeability and conductivity). An illustrative analysis of spatio-spectral distribution of EM wave energy absorption and resulting heating effect were conducted for muscle tissues and the results are discussed.     

ALIPHATIC THIOETHERS BY S-ALKYLATION OF THIOLS VIA TRIALKYL BORATES

A simple and convenient one-pot procedure is described for the synthesis of thioethers via boron esters. This procedure involves in-situ generation of alkyl sulfates by reaction of trialkyl borates with concentrated sulfuric acid and subsequent reaction with thiols in the presence of pyridine. The reactions with boron esters of primary or secondary alcohols proceed cleanly at 100°C and afford aliphatic thioethers in reasonable yields (59–93%) within 24 h. Interestingly, the ¹H NMR spectra of the products showed no sign of positional isomerisms. The method fails however with thiophenol and does not yield aromatic thioethers, due to electrophilic substitution at the phenyl ring.     

Synthesis and Spectroscopic Properties of S-,O-Substituted Naphthoquinone Dyes

New S-,O-substituted naphthoquinone compounds (3a, 4b, 6, 7c, 9d, 10, 12, 13c, 14d, 15) were synthesized via vinilic substitution. 2,3-Dichloro-1,4-naphthoquinone gave 3a and 4b with 4,4′-thiobisbenzenethiol, respectively. Compounds 6 and 7c were obtained from the reaction of 2,3-dichloro-1,4-naphthoquinone with cyclohexylmercaptane. The compounds 9d and 10 were prepared from the reaction of 2,3-dichloro-1,4-naphthoquinone with 6-mercapto-1-hexanol. Compounds 12, 13c, 14d, and 15 were synthesized from the reaction of 2,3-dichloro-1,4-naphthoquinone with 1,6-hexanedithiol. Their structures were characterized by micro analysis, FT-IR, ¹H NMR, ¹³C NMR, MS, UV-Vis, and fluorescence spectroscopy.     

Emission of Toxic HCN During NOx Removal by Ammonia SCR in the Exhaust of Lean‐Burn Natural Gas Engines

Reducing greenhouse gas and pollutant emissions is one of the most stringent priorities of our society to minimize their dramatic effects on health and environment. Natural gas (NG) engines, in particular at lean conditions, emit less CO₂ in comparison to combustion engines operated with liquid fuels but NG engines still require emission control devices for NO_x removal. Using state‐of‐the‐art technologies for selective catalytic reduction (SCR) of NO_x with NH₃, we evaluated the interplay of the reducing agent NH₃ and formaldehyde, which is always present in the exhaust of NG engines. Our results show that a significant amount of highly toxic hydrogen cyanide (HCN) is formed. All catalysts tested partially convert formaldehyde to HCOOH and CO. Additionally, they form secondary emissions of HCN due to catalytic reactions of formaldehyde and its oxidation intermediates with NH₃. With the present components of the exhaust gas aftertreatment system the HCN emissions are not efficiently converted to non‐polluting gases. The development of more advanced catalyst formulations with improved oxidation activity is mandatory to solve this novel critical issue.     

Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples

Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and CVPR. These defenses are mainly focused on mitigating white-box attacks. They do not properly examine black-box attacks. In this paper, we expand upon the analyses of these defenses to include adaptive black-box adversaries. Our evaluation is done on nine defenses including Barrage of Random Transforms, ComDefend, Ensemble Diversity, Feature Distillation, The Odds are Odd, Error Correcting Codes, Distribution Classifier Defense, K-Winner Take All and Buffer Zones. Our investigation is done using two black-box adversarial models and six widely studied adversarial attacks for CIFAR-10 and Fashion-MNIST datasets. Our analyses show most recent defenses (7 out of 9) provide only marginal improvements in security (<25%), as compared to undefended networks. For every defense, we also show the relationship between the amount of data the adversary has at their disposal, and the effectiveness of adaptive black-box attacks. Overall, our results paint a clear picture: defenses need both thorough white-box and black-box analyses to be considered secure. We provide this large scale study and analyses to motivate the field to move towards the development of more robust black-box defenses.