A sparse approach for high-dimensional data with heavy-tailed noise

High-dimensional data have commonly emerged in diverse fields, such as economics, finance, genetics, medicine, machine learning, and so on. In this paper, we consider the sparse quantile regression problem of high-dimensional data with heavy-tailed noise, especially when the number of regressors is much larger than the sample size. We bring the spirit of Lp-norm support vector regression into quantile regression and propose a robust Lp-norm support vector quantile regression for high-dimensional data with heavy-tailed noise. The proposed method achieves robustness against heavy-tailed noise due to its use of the pinball loss function. Furthermore, Lp-norm support vector quantile regression ensures that the most representative variables are selected automatically by using a sparse parameter. We use a simulation study to test the variable selection performance of Lp-norm support vector quantile regression, where the number of explanatory variables greatly exceeds the sample size. The simulation study confirms that Lp-norm support vector quantile regression is not only robust against heavy-tailed noise but also selects representative variables. We further apply the proposed method to solve the variable selection problem of index construction, which also confirms the robustness and sparseness of Lp-norm support vector quantile regression

Separate, Dynamic and Differentiable (SMART) Pruner for Block/Output Channel Pruning on Computer Vision Tasks

Deep Neural Network (DNN) pruning has emerged as a key strategy to reduce model size, improve inference latency, and lower power consumption on DNN accelerators. Among various pruning techniques, block and output channel pruning have shown significant potential in accelerating hardware performance. However, their accuracy often requires further improvement. In response to this challenge, we introduce a separate, dynamic and differentiable (SMART) pruner. This pruner stands out by utilizing a separate, learnable probability mask for weight importance ranking, employing a differentiable Top k operator to achieve target sparsity, and leveraging a dynamic temperature parameter trick to escape from non-sparse local minima. In our experiments, the SMART pruner consistently demonstrated its superiority over existing pruning methods across a wide range of tasks and models on block and output channel pruning. Additionally, we extend our testing to Transformer-based models in N:M pruning scenarios, where SMART pruner also yields state-of-the-art results, demonstrating its adaptability and robustness across various neural network architectures, and pruning types

Significant Comparative Characteristics between Orphan and Nonorphan Genes in the Rice (Oryza sativa L.) Genome

Microsatellites are short tandem repeats of one to six bases in genomic DNA. As microsatellites are highly polymorphic and play a vital role in gene function and recombination, they are an attractive subject for research in evolution and in the genetics and breeding of animals and plants. Orphan genes have no known homologs in existing databases. Using bioinformatic computation and statistical analysis, we identified 19,26 orphan genes in the rice (Oryza sativa ssp. Japanica cv. Nipponbare) proteome. We found that a larger proportion of orphan genes are expressed after sexual maturation and under environmental pressure than nonorphan genes. Orphan genes generally have shorter protein lengths and intron size, and are faster evolving. Additionally, orphan genes have fewer PROSITE patterns with larger pattern sizes than those in nonorphan genes. The average microsatellite content and the percentage of trinucleotide repeats in orphan genes are also significantly higher than in nonorphan genes. Microsatellites are found less often in PROSITE patterns in orphan genes. Taken together, these orphan gene characteristics suggest that microsatellites play an important role in orphan gene evolution and expression

Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation

Free energy profile (FE Profile) is an essential quantity for the estimation of reaction rate and the validation of reaction mechanism. For chemical reactions in condensed phase or enzymatic reactions, the computation of FE profile at ab initio (ai) quantum mechanical/molecular mechanics (QM/MM) level is still far too expensive. Semiempirical (SE) method can be hundreds or thousands of times faster than the ai methods. However, the accuracy of SE methods is often unsatisfactory, due to the approximations that have been adopted in these methods. In this work, we proposed a new method termed MBAR+wTP, in which the ai QM/MM free energy profile is computed by a weighted thermodynamic perturbation (TP) correction to the SE profile generated by the multistate Bennett acceptance ratio (MBAR) analysis of the trajectories from umbrella samplings (US). The weight factors used in the TP calculations are a byproduct of the MBAR analysis in the post-processing of the US trajectories, which are often discarded after the free energy calculations. The results show that this approach can enhance the efficiency of ai FE profile calculations by several orders of magnitude

QED effects on phase transition and Ruppeiner geometry of Euler-Heisenberg-AdS black holes

Taking the quantum electrodynamics (QED) effect into account, we study the black hole phase transition and Ruppeiner geometry for the Euler-Heisenberg anti-de Sitter black hole in the extended phase space. For negative and small positive QED parameter, we observe a small/large black hole phase transition and reentrant phase transition, respectively. While a large positive value of the QED parameter ruins the phase transition. The phase diagrams for each case are explicitly exhibited. Then we construct the Ruppeiner geometry in the thermodynamic parameter space. Different features of the corresponding scalar curvature are shown for both the small/large black hole phase transition and reentrant phase transition cases. Of particular interest is that an additional region of positive scalar curvature indicating dominated repulsive interaction among black hole microstructure is present for the black hole with a small positive QED parameter. Furthermore, the universal critical phenomena are also observed for the scalar curvature of the Ruppeiner geometry. These results indicate that the QED parameter has a crucial influence on the black hole phase transition and microstructure.Comment: 19 pages, 14 figure

Features For Automated Tongue Image Shape Classification

Inspection of the tongue is a key component in Traditional Chinese Medicine. Chinese medical practitioners diagnose the health status of a patient based on observation of the color, shape, and texture characteristics of the tongue. The condition of the tongue can objectively reflect the presence of certain diseases and aid in the differentiation of syndromes, prognosis of disease and establishment of treatment methods. Tongue shape is a very important feature in tongue diagnosis. A different tongue shape other than ellipse could indicate presence of certain pathologies. In this paper, we propose a novel set of features, based on shape geometry and polynomial equations, for automated recognition and classification of the shape of a tongue image using supervised machine learning techniques. We also present a novel method to correct the orientation/deflection of the tongue based on the symmetry of axis detection method. Experimental results obtained on a set of 303 tongue images demonstrate that the proposed method improves the current state of the art method. © 2012 IEEE

Quantum Fisher information and coherence in one-dimensional XYXY spin models with Dzyaloshinsky-Moriya interactions

We investigate quantum phase transitions in $XY$ spin models using Dzyaloshinsky-Moriya (DM) interactions. We identify the quantum critical points via quantum Fisher information and quantum coherence, finding that higher DM couplings suppress quantum phase transitions. However, quantum coherence (characterized by the $l_1$-norm and relative entropy) decreases as the DM coupling increases. Herein, we present both analytical and numerical results.Comment: 7 pages, 5 figure