A Fast Learning Method for Multilayer Perceptrons in Automatic Speech Recognition Systems

We propose a fast learning method for multilayer perceptrons (MLPs) on large vocabulary continuous speech recognition (LVCSR) tasks. A preadjusting strategy based on separation of training data and dynamic learning-rate with a cosine function is used to increase the accuracy of a stochastic initial MLP. Weight matrices of the preadjusted MLP are restructured by a method based on singular value decomposition (SVD), reducing the dimensionality of the MLP. A back propagation (BP) algorithm that fits the unfolded weight matrices is used to train the restructured MLP, reducing the time complexity of the learning process. Experimental results indicate that on LVCSR tasks, in comparison with the conventional learning method, this fast learning method can achieve a speedup of around 2.0 times with improvement on both the cross entropy loss and the frame accuracy. Moreover, it can achieve a speedup of approximately 3.5 times with only a little loss of the cross entropy loss and the frame accuracy. Since this method consumes less time and space than the conventional method, it is more suitable for robots which have limitations on hardware

Rashba spin-orbit coupling enhanced magnetoresistance in junctions with one ferromagnet

We explain how Rashba spin-orbit coupling (SOC) in a two-dimensional electron gas (2DEG), or in a conventional $s$-wave superconductor, can lead to a large magnetoresistance even with one ferromagnet. However, such enhanced magnetoresistance is not generic and can be nonmonotonic and change its sign with Rashba SOC. For an in-plane rotation of magnetization, it is typically negligibly small for a 2DEG and depends on the perfect transmission which emerges from a spin-parity-time symmetry of the scattering states, while this symmetry is generally absent from the Hamiltonian of the system. The key difference from considering the normal-state magnetoresistance is the presence of the spin-dependent Andreev reflection at superconducting interfaces. In the fabricated junctions of quasi-2D van der Waals ferromagnets with conventional $s$-wave superconductors (Fe$_{0.29}$TaS$_2$/NbN) we find another example of enhanced magnetoresistance where the presence of Rashba SOC reduces the effective interfacial strength and is responsible for an equal-spin Andreev reflection. The observed nonmonotonic trend in the out-of-plane magnetoresistance with the interfacial barrier is an evidence for the proximity-induced equal-spin-triplet superconductivity.Comment: This work is submitted to the special issue of Phys. Rev. B dedicated to Professor Emmanuel Rashb

Approximate solutions to large nonsymmetric differential Riccati problems with applications to transport theory

In the present paper, we consider large scale nonsymmetric differential matrix Riccati equations with low rank right hand sides. These matrix equations appear in many applications such as control theory, transport theory, applied probability and others. We show how to apply Krylov-type methods such as the extended block Arnoldi algorithm to get low rank approximate solutions. The initial problem is projected onto small subspaces to get low dimensional nonsymmetric differential equations that are solved using the exponential approximation or via other integration schemes such as Backward Differentiation Formula (BDF) or Rosenbrok method. We also show how these technique could be easily used to solve some problems from the well known transport equation. Some numerical experiments are given to illustrate the application of the proposed methods to large-scale problem

Recovery of an embedded obstacle and the surrounding medium for Maxwell's system

In this paper, we are concerned with the inverse electromagnetic scattering problem of recovering a complex scatterer by the corresponding electric far-field data. The complex scatterer consists of an inhomogeneous medium and a possibly embedded perfectly electric conducting (PEC) obstacle. The far-field data are collected corresponding to incident plane waves with a fixed incident direction and a fixed polarisation, but frequencies from an open interval. It is shown that the embedded obstacle can be uniquely recovered by the aforementioned far-field data, independent of the surrounding medium. Furthermore, if the surrounding medium is piecewise homogeneous, then the medium can be recovered as well. Those unique recovery results are new to the literature. Our argument is based on low-frequency expansions of the electromagnetic fields and certain harmonic analysis techniques.Comment: 15 page

Genome-Wide Association Study for Milk Protein Composition Traits in a Chinese Holstein Population Using a Single-Step Approach

Genome-wide association studies (GWASs) have been widely used to determine the genetic architecture of quantitative traits in dairy cattle. In this study, with the aim of identifying candidate genes that affect milk protein composition traits, we conducted a GWAS for nine such traits (αs1-casein, αs2-casein, β-casein, κ-casein, α-lactalbumin, β-lactoglobulin, casein index, protein percentage, and protein yield) in 614 Chinese Holstein cows using a single-step strategy. We used the Illumina BovineSNP50 Bead chip and imputed genotypes from high-density single-nucleotide polymorphisms (SNPs) ranging from 50 to 777 K, and subsequent to genotype imputation and quality control, we screened a total of 586,304 informative high-quality SNPs. Phenotypic observations for six major milk proteins (αs1-casein, αs2-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin) were evaluated as weight proportions of the total protein fraction (wt/wt%) using a commercial enzyme-linked immunosorbent assay kit. Informative windows comprising five adjacent SNPs explaining no < 0.5% of the genomic variance per window were selected for gene annotation and gene network and pathway analyses. Gene network analysis performed using the STRING Genomics 10.0 database revealed a co-expression network comprising 46 interactions among 62 of the most plausible candidate genes. A total of 178 genomic windows and 194 SNPs on 24 bovine autosomes were significantly associated with milk protein composition or protein percentage. Regions affecting milk protein composition traits were mainly observed on chromosomes BTA 1, 6, 11, 13, 14, and 18. Of these, several windows were close to or within the CSN1S1, CSN1S2, CSN2, CSN3, LAP3, DGAT1, RPL8, and HSF1 genes, which have well-known effects on milk protein composition traits of dairy cattle. Taken together with previously reported quantitative trait loci and the biological functions of the identified genes, we propose 19 novel candidate genes affecting milk protein composition traits: ARL6, SST, EHHADH, PCDHB4, PCDHB6, PCDHB7, PCDHB16, SLC36A2, GALNT14, FPGS, LARP4B, IDI1, COG4, FUK, WDR62, CLIP3, SLC25A21, IL5RA, and ACADSB. Our findings provide important insights into milk protein synthesis and indicate potential targets for improving milk quality

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Phenotype evaluation on downy mildew resistance of spinach germplasm resources

Downy mildew is the main disease in the production of spinach.The selection and evaluation of germplasm resources is the implantation and basis of the breeding of spinach against downy mildew.In this study,the spinachtraits from different germplasm resources,such as botanical characteristics,reproduction cycle and disease resistance,which have downy mildew resistance,were identifiedby field observation.The results showed that the diversity index of 10 qualitative traits of 59 spinach germplasm resources was distributed between 0.25 and 1.23,and the coefficient of variation of 8 quantitative traits was between 24.25% and 42.80%,with an average of 29.54%.The identification of the resistance to downy mildew show that the spinach materials S1 to S36 are high-resistant materials,S37-S41 are disease-resistant materials,and S42-S59 are medium-resistant materials.The results of the bolting period survey showed that 81.4% of the materials belonged to early or middle bolting materials,18.6% of the spinach materials belonged to the late bolting materials.In conclusion,the spinach material S1 has an erect plant type,oval leaves,late bolting and high resistance to downy mildew,and there was no downy mildew disease in the test period,and the downy mildew disease index and incidence rate were both 0. It was excellent germplasm resource for resisting to spinach downy mildew and for late bolting breeding

Deep Neural Networks with Multistate Activation Functions

We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF. DNNs with these MSAFs can be trained via conventional Stochastic Gradient Descent (SGD) as well as mean-normalised SGD. We also discuss how these MSAFs perform when used to resolve classification problems. Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates. Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets. The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates