Neural Gradient Regularizer

Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one of the most representative regularizers, is known for its ability to capture the sparsity of gradient maps. Nonetheless, TV and its variants often underestimate the gradient maps, leading to the weakening of edges and details whose gradients should not be zero in the original image. Recently, total deep variation (TDV) has been introduced, assuming the sparsity of feature maps, which provides a flexible regularization learned from large-scale datasets for a specific task. However, TDV requires retraining when the image or task changes, limiting its versatility. In this paper, we propose a neural gradient regularizer (NGR) that expresses the gradient map as the output of a neural network. Unlike existing methods, NGR does not rely on the sparsity assumption, thereby avoiding the underestimation of gradient maps. NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer. Extensive experimental results demonstrate the superior performance of NGR over state-of-the-art counterparts for a range of different tasks, further validating its effectiveness and versatility

Interplay between spin wave and magnetic vortex

In this paper, the interplay between spin wave and magnetic vortex is studied. We find three types of magnon scatterings: skew scattering, symmetric side deflection and back reflection, which associate with respectively magnetic topology, energy density distribution and linear momentum transfer torque within vortex. The vortex core exhibits two translational modes: the intrinsic circular mode and a coercive elliptical mode, which can be excited based on permanent and periodic magnon spin-transfer torque effects of spin wave. Lastly, we propose a vortex-based spin wave valve in which via inhomogeneity modulation we access arbitrary control of the phase shift.Comment: 33 pages, 23 figures, 1 tabl

Dynamic pricing in the presence of consumer inertia

a b s t r a c t Customer behavior modeling has gained increasing attention in the context of dynamic pricing. As an important behavior phenomenon, consumer inertia refers to consumers' inherent tendency of purchase procrastination and may induce consumers to wait even when immediate purchase is optimal from an objective perspective. This paper studies a dynamic pricing problem for a monopolist firm selling perishable goods to consumers who may be influenced by inertia. We formulate this problem using the finite-horizon dynamic programming approach and derive the optimal dynamic pricing policy. We demonstrate that consumer inertia produces negative effects on firms' expected revenues and optimal prices, which are monotonically decreasing in both inertia depth and breadth. Through numerical illustrations, we further show that the marginal effects of inertia depth on optimal prices and expected revenues are decreasing, whereas the marginal effects of inertia breadth are increasing. Finally we propose some suggestions for firms to influence the level of consumer inertia

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.Vehicle routing Heterogeneous fixed fleet Adaptive memory programming Path relinking Metaheuristic

Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm

This paper studies a version of stochastic vehicle routing problems, in which travel and service times are stochastic, and a time window constraint is associated with each customer. This problem is originally formulated as a chance constrained programming model and a stochastic programming model with recourse in terms of different optimization criteria. To efficiently solve these two models, a heuristic based on tabu search, which takes into account the stochastic nature of this problem, is then proposed. Finally, some testing instances with different properties are established to investigate the algorithmic performance, and the computational results are then reported.Vehicle routing problem Stochastic travel time Stochastic service time Time windows Tabu search

Identification and Characterization of the HD-Zip Gene Family and Dimerization Analysis of HB7 and HB12 in Brassica napus L.

Homeodomain-leucine zipper (HD-Zip) genes encode plant-specific transcription factors, which play important roles in plant growth, development, and response to environmental stress. These genes have not been fully studied in allopolyploid Brassica napus, an important kind of oil crop. In this study, 165 HD-Zip genes were identified in B. napus and classified into four subfamilies. If proteins belong to the same subfamily, they exhibit similarities in gene structure, motifs, and domain distribution patterns. BnHD-Zip genes were unevenly distributed in the An and Cn subgenomes. Whole genome triplication (WGT) events may be major mechanisms accounting for this gene family expansion. Orthologous gene analysis showed that the process of this gene family expansion was accompanied by domain loss. We further found three genes homologous to HB7 and three genes homologous to HB12, all induced by PEG, ABA, and NaCl treatment. HB7 could not form homodimers but could form heterodimers with HB12 based on yeast two-hybrid assays. The results of this study provide valuable information for further exploration of the HD-Zip gene family in B. napus

Analysis of the Influence of Ferromagnetic Material on the Output Characteristics of Halbach Array Energy-Harvesting Structure

Due to the particular arrangement of permanent magnets, a Halbach array has an significant effect of magnetism and magnetic self-shielding. It can stretch the magnetic lines on one side of the magnetic field to obtain an ideal sinusoidal unilateral magnetic field. It has a wide application range in the field of energy harvesting. In practical applications, magnetic induction intensity of each point in magnetic field is not only related to the induced current and conductor but also related to the permeability of the medium (also known as a magnetic medium) in the magnetic field. Permeability is the physical quantity that represents the magnetism of the magnetic medium, which indicates the resistance of magnetic flux or the ability of magnetic lines to be connected in the magnetic field after coil flows through current in space or in the core space. When the permeability is much greater than one, it is a ferromagnetic material. Adding a ferromagnetic material in a magnetic field can increase the magnetic induction intensity B. Iron sheet is a good magnetic material, and it is easy to magnetize to generate an additional magnetic field to strengthen the original magnetic field, and it is easy to obtain at low cost. In this paper, in order to explore the influence of ferromagnetic material on the magnetic field and energy harvesting efficiency of the Halbach array energy harvesting structure, iron sheets are installed on the periphery of the Halbach array rotor. Iron sheet has excellent magnetic permeability. Through simulation, angle between iron sheet and Halbach array, radian size of iron sheet itself and distance between iron sheet and Halbach array can all have different effects on the magnetic field of the Halbach array. It shows that adding iron sheets as a magnetic medium could indeed change the magnetic field distribution of the Halbach array and increase energy harvesting efficiency. In this paper, a Halbach array can be used to provide electrical power for passive wireless low-power devices

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal denoiser but ignore the understanding why these pseudoimages have a similar spatial structure as the original HSI. In this work, we revisit such vein from the respective of principal component analysis (PCA). Inspired by an alternative sparse PCA, we propose a spectral sparse subspace representation strategy to simultaneously learn low-dimensional spectral subspace and novel RCIs with sparse loadings. It turns out that the resulting RCIs possess a more significant spatial structure due to the adaptive sparse combination of spectral bands. A simple nonlocal low-rank approximation is then employed to further remove the residual noise of the RCIs. Finally, the entire denoised HSI is obtained by inverse spectral sparse PCA. Extensive experiments on the simulated and real HSI datasets show that the proposed nonlocal spectral sparse subspace representation method, dubbed as NS3R, has excellent performance both in denoising effect and running time compared with many other state-of-the-art methods

Novel Prognostic Nomograms for Hepatocellular Carcinoma Patients with Microvascular Invasion: Experience from a Single Center

Background/Aims Microvascular invasion (MVI) is an established risk factor for hepatocellular carcinoma (HCC). However, prediction models that specifically focus on the individual prognoses of HCC patients with MVI is lacking. Methods : A total of 385 HCC patients with MVI were randomly assigned to training and validation cohorts in a 2:1 ratio. The outcomes were disease-free survival (DFS) and overall survival (OS). Prognostic nomograms were established based on the results of multivariate analyses. The concordance index (C-index), calibration plots and Kaplan-Meier curves were employed to evaluate the accuracy, calibration and discriminatory ability of the models. Results : The independent risk factors for both DFS and OS included age, tumor size, tumor number, the presence of gross vascular invasion, and the presence of Glisson’s capsule invasion. The platelet-to-lymphocyte ratio was another risk factor for OS. On the basis of these predictors, two nomograms for DFS and OS were constructed. The C-index values of the nomograms for DFS and OS were 0.712 (95% confidence interval [CI], 0.679 to 0.745; p<0.001) and 0.698 (95% CI, 0.657 to 0.739; p<0.001), respectively, in the training cohort and 0.704 (95% CI, 0.650 to 0.708; p<0.001) and 0.673 (95% CI, 0.607 to 0.739; p<0.001), respectively, in the validation cohort. The calibration curves showed optimal agreement between the predicted and observed survival rates. The Kaplan-Meier curves suggested that these two nomograms had satisfactory discriminatory abilities. Conclusion : s These novel predictive models have satisfactory accuracy and discriminatory abilities in predicting the prognosis of HCC patients with MVI after hepatectomy