Progressive Filtering Using Multiresolution Histograms for Query by Humming System

The rising availability of digital music stipulates effective categorization and retrieval methods. Real world scenarios are characterized by mammoth music collections through pertinent and non-pertinent songs with reference to the user input. The primary goal of the research work is to counter balance the perilous impact of non-relevant songs through Progressive Filtering (PF) for Query by Humming (QBH) system. PF is a technique of problem solving through reduced space. This paper presents the concept of PF and its efficient design based on Multi-Resolution Histograms (MRH) to accomplish searching in manifolds. Initially the entire music database is searched to obtain high recall rate and narrowed search space. Later steps accomplish slow search in the reduced periphery and achieve additional accuracy. Experimentation on large music database using recursive programming substantiates the potential of the method. The outcome of proposed strategy glimpses that MRH effectively locate the patterns. Distances of MRH at lower level are the lower bounds of the distances at higher level, which guarantees evasion of false dismissals during PF. In due course, proposed method helps to strike a balance between efficiency and effectiveness. The system is scalable for large music retrieval systems and also data driven for performance optimization as an added advantage.Comment: 12 Pages, 6 Figures, Full version of the paper published at ICMCCA-2012 with the same title, Link:http://link.springer.com/chapter/10.1007/978-81-322-1143-3_2

NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment

International audienceScale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width

Agile Parameter Affecting Supply Chain Management Strategy

The aim of this paper is to summarize Supply Chain Management (SCM) strategies which are getting affected by the agile parameters. Agile parameters are classified into Capabler, Driver, and Enabler may help an expert to take a decision in an agile environment for accepting changes. Agile development affects the working style of the modules in SCM. This paper shows business process agility defined by the number of the parameter affecting the working style of the SCM modules. The study includes a causal analysis of SCM modules based on the review of a number of research papers and books. SCM based case study of inventory management of swatches is studied with the strategy mapping in different modules based on agile parameters. Set of parameters is studied as per the case study of swatch inventory management in agile development. Mapping agile parameters at different strategies in the changing environment makes a system to understand the impact and future of the agile parameters at different levels of SCM modules. Finding the different type of agility and amount of agility in the SCM system can be an enhancement of this paper

A comprehensive study of the open cluster NGC 6866

We present CCD $UBVRI$ photometry of the field of the open cluster NGC 6866. Structural parameters of the cluster are determined utilizing the stellar density profile of the stars in the field. We calculate the probabilities of the stars being a physical member of the cluster using their astrometric data and perform further analyses using only the most probable members. The reddening and metallicity of the cluster were determined by independent methods. The LAMOST spectra and the ultraviolet excess of the F and G type main-sequence stars in the cluster indicate that the metallicity of the cluster is about the solar value. We estimated the reddening $E(B-V)=0.074 \pm 0.050$ mag using the $U-B$ vs $B-V$ two-colour diagram. The distance modula, the distance and the age of NGC 6866 were derived as $\mu = 10.60 \pm 0.10$ mag, $d=1189 \pm 75$ pc and $t = 813 \pm 50$ Myr, respectively, by fitting colour-magnitude diagrams of the cluster with the PARSEC isochrones. The Galactic orbit of NGC 6866 indicates that the cluster is orbiting in a slightly eccentric orbit with $e=0.12$. The mass function slope $x=1.35 \pm 0.08$ was derived by using the most probable members of the cluster.Comment: 14 pages, including 16 figures and 7 tables, accepted for publication in MNRAS. Table 4 in the manuscript will be published electronicall

Discovery (theoretical prediction and experimental observation) of a large-gap topological-insulator class with spin-polarized single-Dirac-cone on the surface

Recent theories and experiments have suggested that strong spin-orbit coupling effects in certain band insulators can give rise to a new phase of quantum matter, the so-called topological insulator, which can show macroscopic entanglement effects. Such systems feature two-dimensional surface states whose electrodynamic properties are described not by the conventional Maxwell equations but rather by an attached axion field, originally proposed to describe strongly interacting particles. It has been proposed that a topological insulator with a single spin-textured Dirac cone interfaced with a superconductor can form the most elementary unit for performing fault-tolerant quantum computation. Here we present an angle-resolved photoemission spectroscopy study and first-principle theoretical calculation-predictions that reveal the first observation of such a topological state of matter featuring a single-surface-Dirac-cone realized in the naturally occurring Bi$_2$Se$_3$ class of materials. Our results, supported by our theoretical predictions and calculations, demonstrate that undoped compound of this class of materials can serve as the parent matrix compound for the long-sought topological device where in-plane surface carrier transport would have a purely quantum topological origin. Our study further suggests that the undoped compound reached via n-to-p doping should show topological transport phenomena even at room temperature.Comment: 3 Figures, 18 pages, Submitted to NATURE PHYSICS in December 200

Strong habitat-specific phenotypic plasticity but no genome-wide differentiation across a rainforest gradient in an African butterfly

Habitat-specific thermal responses are well documented in various organisms and likely determine the vulnerability of populations to climate change. However, the underlying roles of genetics and plasticity that shape such habitat-specific patterns are rarely investigated together. Here we examined the thermal plasticity of the butterfly Bicyclus dorothea originating from rainforest and ecotone habitats in Cameroon under common garden conditions. We also sampled wild-caught butterflies from forest and ecotone sites and used RADseq to explore genome-wide population differentiation. We found differences in the level of phenotypic plasticity across habitats. Specifically, ecotone populations exhibited greater sensitivity in wing eyespot features with variable development temperatures relative to rainforest populations. Known adaptive roles of wing eyespots in Bicyclus species suggest that this morphological plasticity is likely under divergent selection across environmental gradients. However, we found no distinct population structure of genome-wide variation between habitats, suggesting high level of ongoing gene flow between habitats is homogenizing most parts of the genome

Polymorphisms of Glutathione S-transferases Omega-1 among ethnic populations in China

<p>Abstract</p> <p>Background</p> <p>Glutathione S-transferases (GSTs) is a genetic factor for many diseases and exhibits great diversities among various populations. We assessed association of the genotypes of Glutathione S-transferases Omega-1 (GSTO1) A140D with ethnicity in China.</p> <p>Results</p> <p>Peripheral blood samples were obtained from 1314 individuals from 14 ethnic groups. Polymorphisms of GSTO1 A140D were measured using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Logistic regression was employed to adjustment for regional factor. The frequency of GSTO1 140A allele was 15.49% in the total 14 ethnic populations. Compared to Han ethnic group, two ethnic populations were more likely to have AA or CA genotype [odds ratio (OR): 1.77, 95% confidence interval (95% CI): 1.05–2.98 for Uygur and OR: 1.78, 95% CI: 1.18–2.69 for Hui]. However, there were no statistically significant differences across 14 ethnic groups when region factor was adjusted. In Han ethnicity, region was significantly associated with AA or CA genotype. Han individuals who resided in North-west of China were more likely to have these genotypes than those in South of China (OR: 1.63, 95% CI: 1.21–2.20).</p> <p>Conclusion</p> <p>The prevalence of the GSTO1 140A varied significantly among different regional populations in China, which showed that geography played a more important role in the population differentiation for this allele than the ethnicity/race.</p

SGU-Net: Shape-Guided Ultralight Network for Abdominal Image Segmentation

Copyright © The Author(s) 2023. Convolutional neural networks (CNNs) have achieved significant success in medical image segmentation. However, they also suffer from the requirement of a large number of parameters, leading to a difficulty of deploying CNNs to low-source hardwares, e.g., embedded systems and mobile devices. Although some compacted or small memory-hungry models have been reported, most of them may cause degradation in segmentation accuracy. To address this issue, we propose a shape-guided ultralight network (SGU-Net) with extremely low computational costs. The proposed SGU-Net includes two main contributions: it first presents an ultralight convolution that is able to implement double separable convolutions simultaneously, i.e., asymmetric convolution and depthwise separable convolution. The proposed ultralight convolution not only effectively reduces the number of parameters but also enhances the robustness of SGU-Net. Secondly, our SGU-Net employs an additional adversarial shape-constraint to let the network learn shape representation of targets, which can significantly improve the segmentation accuracy for abdomen medical images using self-supervision. The SGU-Net is extensively tested on four public benchmark datasets, LiTS, CHAOS, NIH-TCIA and 3Dircbdb. Experimental results show that SGU-Net achieves higher segmentation accuracy using lower memory costs, and outperforms state-of-the-art networks. Moreover, we apply our ultralight convolution into a 3D volume segmentation network, which obtains a comparable performance with fewer parameters and memory usage.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271296, 61871259 and 61861024); Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47); 10.13039/501100015401-Key Research and Development Projects of Shaanxi Province (Grant Number: 2022GY-436 and 2021ZDLGY08-07); Natural Science Basic Research Program of Shaanxi (Grant Number: 2022JQ-634 and 2022JQ-018); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Huazhu Fu's A*STAR Central Research Fund; AISG Tech Challenge Funding (Grant Number: AISG2-TC-2021-003)

B-> D* zero-recoil formfactor and the heavy quark expansion in QCD: a systematic study

We present a QCD analysis of heavy quark mesons focussing on the B -> D* formfactor at zero recoil, F_D*(1). An advanced treatment of the perturbative corrections in the Wilsonian approach is presented. We estimate the higher-order power corrections to the OPE sum rule and describe a refined analysis of the nonresonant continuum contribution. In the framework of a model-independent approach, we show that the inelastic contribution in the phenomenological part of the OPE is related to the mQ-dependence of the hyperfine splitting and conclude that the former is large, lowering the prediction for F_D*(1) down to about 0.86. This likewise implies an enhanced yield of radial and D-wave charm excitations in semileptonic B decays and alleviates the problem with the inclusive yield of the wide excited states. We also apply the approach to the expectation values of dimension 7 and 8 local operators and to a few other issues in the heavy quark expansion.Comment: 70 pages, 13 figure

Transport Through Andreev Bound States in a Graphene Quantum Dot

Andreev reflection-where an electron in a normal metal backscatters off a superconductor into a hole-forms the basis of low energy transport through superconducting junctions. Andreev reflection in confined regions gives rise to discrete Andreev bound states (ABS), which can carry a supercurrent and have recently been proposed as the basis of qubits [1-3]. Although signatures of Andreev reflection and bound states in conductance have been widely reported [4], it has been difficult to directly probe individual ABS. Here, we report transport measurements of sharp, gate-tunable ABS formed in a superconductor-quantum dot (QD)-normal system, which incorporates graphene. The QD exists in the graphene under the superconducting contact, due to a work-function mismatch [5, 6]. The ABS form when the discrete QD levels are proximity coupled to the superconducting contact. Due to the low density of states of graphene and the sensitivity of the QD levels to an applied gate voltage, the ABS spectra are narrow, can be tuned to zero energy via gate voltage, and show a striking pattern in transport measurements.Comment: 25 Pages, included SO