InAs nanowire superconducting tunnel junctions: Quasiparticle spectroscopy,thermometry, and nanorefrigeration

We demonstrate an original method based on controlled oxidation for creating high-quality tunnel junctions between superconducting Al reservoirs and InAs semiconductor nanowires (NWs).We show clean tunnel characteristics with a current suppression by ＞4 orders of magnitude for a junction bias well below the Al gap of A0 ≈ 200 μeV.The experimental data agree well with the BardeenCooper-Schrieffer theoretical expectations for a superconducting tunnel junction.The studied devices employ small-scale tunnel contacts functioning as thermometers as well as larger electrodes that provide proof-of-principle active cooling of the electron distribution in the NWs.A peak refrigeration of approximately δT =10 mK is achieved at a bath temperature of Tbath ≈ 250-350 mK for our prototype devices.This method introduces important perspectives for the investigation of the thermoelectric effects in semiconductor nanostructures and for nanoscale refrigeration.     

Detergent impurity effect on recycled HDPE: Properties after repetitive processing

High density polyethylene (rHDPE) is extruded 1 to 8 times, with and without detergent, to simulate the effects of impurities on the material and on the artificial ageing process. The mechanical properties, thermal stability, rheology, Fourier transform infrared spectroscopy (FTIR), and volatile organic compound (VOC) emissions are measured. According to the results, ageing of rHDPE increases tensile strength, reduces elongation, and enhances side chain branching of the material and thus causes rheological changes. The addition of detergent reduces changes in mechanical properties and rheological behavior but accelerates thermal degradation. VOC and FTIR measurements of the samples with detergent addition show generation of harmful 1,4‐dioxane. The amount of total emission, as well as emissions of important perfumes limonene and 1R‐α‐pinene, decreases during multiple extrusion cycles. Heating of the plastics is found to be a major factor in the VOC emission reduction. Impurities have a notable effect on the artificial ageing results. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43766.     

Hybrid Generative-Discriminative Visual Categorization

Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to naturally establish explicit correspondence between model components and scene features—this, in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach, using ‘Fisher Kernels’ (Jaakola, T., et al. in Advances in neural information processing systems, Vol. 11, pp. 487–493, 1999), which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach. In addition, we demonstrate how this hybrid learning paradigm can be extended to address several outstanding challenges within computer vision including how to combine multiple object models and learning with unlabeled data.     

Generalized darting Monte Carlo

One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge of the location of these modes can be incorporated into the MCMC sampler by introducing mode-hopping moves that satisfy detailed balance. The proposed sampling algorithm explores local mode structure through local MCMC moves (e.g. diffusion or Hybrid Monte Carlo) but in addition also represents the relative strengths of the different modes correctly using a set of global moves. This ‘mode-hopping’ MCMC sampler can be viewed as a generalization of the darting method [1]. We illustrate the method on learning Markov random fields and evaluate it against the spherical darting algorithm on a ‘real world’ vision application of inferring 3D human body pose distributions from 2D image information.     

Probabilistic sequential independent components analysis

Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets.