Research on Batch Scheduling in Cloud Computing

In the existing cloud computing environment, batch scheduling strategies mainly focus on the management of resources allocation. This paper provides the task scheduling algorithm based on service quality which fully considers priority and scheduling deadline. The improved algorithm combines the advantages of Min-min algorithm with higher throughput and linear programming with global optimization, considers not only all the tasks but also the high priority tasks. The experiment result shows that compared with the Min-min and DBCT the completed tasks of the improved algorithm increase about 10.6% and 22.0%, on the other hand the completed high priority tasks also increases approximately 20% and 40%

An efficient symmetric primal-dual algorithmic framework for saddle point problems

In this paper, we propose a new primal-dual algorithmic framework for a class of convex-concave saddle point problems frequently arising from image processing and machine learning. Our algorithmic framework updates the primal variable between the twice calculations of the dual variable, thereby appearing a symmetric iterative scheme, which is accordingly called the {\bf s}ymmetric {\bf p}r{\bf i}mal-{\bf d}ual {\bf a}lgorithm (SPIDA). It is noteworthy that the subproblems of our SPIDA are equipped with Bregman proximal regularization terms, which make SPIDA versatile in the sense that it enjoys an algorithmic framework covering some existing algorithms such as the classical augmented Lagrangian method (ALM), linearized ALM, and Jacobian splitting algorithms for linearly constrained optimization problems. Besides, our algorithmic framework allows us to derive some customized versions so that SPIDA works as efficiently as possible for structured optimization problems. Theoretically, under some mild conditions, we prove the global convergence of SPIDA and estimate the linear convergence rate under a generalized error bound condition defined by Bregman distance. Finally, a series of numerical experiments on the matrix game, basis pursuit, robust principal component analysis, and image restoration demonstrate that our SPIDA works well on synthetic and real-world datasets.Comment: 32 pages; 5 figure; 7 table

The coarse Baum-Connes conjecture for certain extensions and relative expanders

Let $\left( 1\to N_n\to G_n\to Q_n\to 1 \right)_{n\in \mathbb{N}}$ be a sequence of extensions of finitely generated groups with uniformly finite generating subsets. We show that if the sequence $\left( N_n \right)_{n\in \mathbb{N}}$ with the induced metric from the word metrics of $\left( G_n \right)_{n\in \mathbb{N}}$ has property A, and the sequence $\left( Q_n \right)_{n\in \mathbb{N}}$ with the quotient metrics coarsely embeds into Hilbert space, then the coarse Baum-Connes conjecture holds for the sequence $\left( G_n \right)_{n\in \mathbb{N}}$, which may not admit a coarse embedding into Hilbert space. It follows that the coarse Baum-Connes conjecture holds for the relative expanders and group extensions exhibited by G. Arzhantseva and R. Tessera, and special box spaces of free groups discovered by T. Delabie and A. Khukhro, which do not coarsely embed into Hilbert space, yet do not contain a weakly embedded expander. This in particular solves an open problem raised by G. Arzhantseva and R. Tessera \cite{Arzhantseva-Tessera 2015}

An efficient approach to acoustic emission source identification based on harmonic wavelet packet and hierarchy support vector machine

A new approach for acoustic emission (AE) source type identification based on harmonic wavelet packet (HWPT) feature extraction and hierarchy support vector machine (H-SVM) classifier is proposed for solving the fatigue damage identification problem of helicopter moving component. In this approach, HWPT is employed to extract the energy feature of AE signals on different frequency bands, as well as to reduce the dimensionality of original data features. We trained the H-SVM classifier on a subset of the experimental data for known AE source type, and then tested on the remaining set of data. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The experimental results indicate that the proposed approach can implement AE source type identification effectively, and achieves better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification

Robustness of double-layer group-dependent combat network with cascading failure

The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. The achievement of combat effectiveness requires CSOS to have a good ability to deal with external interference. Here we report a modeling method of CSOS from the perspective of complex networks and explore the robustness of the combat network based on this. Firstly, a more realistic double-layer heterogeneous dependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analyzed for cascading failure. Based on this, the initial load and capacity of the node are defined, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and different parameters, and the results show that the robustness of the combat network can be effectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional subnet and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for network structure optimization and network security protection in the military field

Evaluación de la cohorte occidental de invierno-primavera del calamar volador neon (Ommastrephes bartramii) utilizando modelos de producción excedente dependientes del medio ambiente

The western winter-spring cohort of neon flying squid, Ommastrephes bartramii, is targeted by Chinese squidjigging fisheries in the northwest Pacific from August to November. Because this squid has a short lifespan and is an ecological opportunist, the dynamics of its stock is greatly influenced by the environmental conditions, which need to be considered in its assessment and management. In this study, an environmentally dependent surplus production (EDSP) model was developed to evaluate the stock dynamics of O. bartramii. Temporal variability of favourable spawning habitat with sea surface temperature (SST) of 21-25°C (Ps) was assumed to influence carrying capacity (K), while temporal variability in favourable feeding habitat areas with different SST ranges in different months (Pf) was assumed to influence intrinsic growth rate (r). The parameters K and r in the EDSP model were thus assumed to be linked to temporal variability in the proportion of Ps and Pf, respectively. According to Deviance Information Criterion values, the estimated EDSP model with Ps was considered to be better than the conventional surplus production model or other EDSP models. For this model, the maximum sustainable yield (MSY) varied from 210000 to 262500 t and biomass at MSY level varied from 360000 to 450000 t. The fishing mortality rates of O. bartramii from 2003 to 2013 were much lower than the fishing mortality at target level and MSY level (Ftar and FMSY) and stock biomass was higher than BMSY, suggesting that this squid was not in the status of overfishing and stock was not overfished. The management reference points in the EDSP model for O. bartramii were more conservative than those in the conventional model. This study suggests that the environmental conditions on the spawning grounds should be considered in squid stock assessment and management in the northwest Pacific Ocean.La cohorte occidental de invierno-primavera de los calamares voladores neon, Ommastrephes bartramii, es objeto de las pesquerías chinas de calamares que operan con jigging en el Pacifico Noroeste, desde agosto a noviembre. Debido a que esta especie tiene un ciclo de vida corto y es ecológicamente oportunista, la dinámica de este stock de calamares está muy influenciada por las condiciones ambientales, las cuales necesitan ser consideradas en su evaluación y manejo. En este estudio fue desarrollado un modelo de producción excedente ambientalmente dependiente (PEAD), para evaluar la dinámica del stock de O. bartramii. Se asumió que la variabilidad temporal de un hábitat favorable para el desove sea a una temperatura superficial del mar de 21-25°C (Ps), para influir en la capacidad de carga (K); mientras que la variabilidad temporal en áreas con hábitat favorable para la alimentación, fue asumida con diferentes rangos de temperatura superficial del mar en diferentes meses (Pf), para influir la tasa intrínseca de crecimiento (r). Los parámetros K y r en el modelo PEAD fueron asumidos como vinculados a la variabilidad temporal en la proporción Ps y Pf , respectivamente. De acuerdo a los valores del Criterio de Información de la Desvianza, el modelo PEAD estimado con Ps fue considerado el mejor, comparado con los modelos de producción excedente convencionales, así como otros modelos PEAD. Para este modelo el rendimiento máximo sostenible (RMS) estuvo entre 210000 a 262500 t y la biomasa al nivel RMS, entre de 360000 a 450000 t. Las tasas de mortalidad por pesca de O. bartramii entre 2003 a 2013 fueron mucho menores que la mortalidad por pesca a nivel objetivo y nivel de RMS (Ftar and FRMS) y la biomasa del stock fue superior a BRMS, sugiriendo que este calamar no estuvo en el estado de sobrepesca y el stock no fue sobrepescado. Los puntos de referencia de manejo (PRMs) en el modelo PEAD para O. bartramii fueron más conservativos que aquéllos obtenidos en los modelos convencionales. Este estudio sugiere que las condiciones ambientales sobre las zonas de desove deberían ser consideradas en las evaluaciones y en el manejo de stock de calamares en el Océano Pacifico Noroeste

Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range of unseen classes, it may struggle when domain gap increases, such as in fine-grained classification and satellite image segmentation. To address this limitation, we propose Retrieval-enhanced Prompt learning (RePrompt), which introduces retrieval mechanisms to cache the knowledge representations from downstream tasks. we first construct a retrieval database from training examples, or from external examples when available. We then integrate this retrieval-enhanced mechanism into various stages of a simple prompt learning baseline. By referencing similar samples in the training set, the enhanced model is better able to adapt to new tasks with few samples. Our extensive experiments over 15 vision datasets, including 11 downstream tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate that RePrompt achieves considerably improved performance. Our proposed approach provides a promising solution to the challenges faced by prompt learning when domain gap increases. The code and models will be available

Correlation Between Crystal Rotation and Redundant Shear Strain in Rolled Single Crystals: A Crystal Plasticity FE Analysis

The correlation between crystal rotation and redundant shear strain in rolled single crystals was investigated by using the crystal plasticity finite element (CPFE) model in this paper. The deformation in aluminium single crystals of four representative orientations (rotated-Cube, Goss, Copper, and Brass) after rolling and plain strain compression was simulated, and the predictions have been validated by the experimental observations. In the rotated-Cube and Goss, the redundant shear strain and crystal rotation were in the same pattern, alternating along the thickness, while the relation between them was not obvious for the Copper and Brass due to their asymmetrical distributions of activated slip systems. The relations between slip system activation, crystal rotation, and shear strain were investigated based on the CPFE model, and the correlation between shear strain and crystal rotation has been built

Low-dose interleukin-2 reverses chronic migraine-related sensitizations through peripheral interleukin-10 and transforming growth factor beta-1 signaling

Low-dose interleukin-2 (LD-IL-2) treatment has been shown to effectively reverse chronic migraine-related behaviors and the sensitization of trigeminal ganglion (TG) neurons through expansion and activation of peripheral regulatory T cells (Tregs) in mice. In this study, we investigated the molecular mechanisms underlying the effects of LD-IL-2 and Treg cells. LD-IL-2 treatment increases the production of cytokines interleukin-10 (IL-10) and transforming growth factor beta-1 (TGFβ1) in T cells, especially Treg cells, suggesting that they may mediate the therapeutic effect of LD-IL-2. Indeed, neutralizing antibodies against either IL-10 or TGFβ completely blocked the effects of LD-IL-2 on the facial mechanical hypersensitivity as well as the sensitization of TG neurons resulting from repeated nitroglycerin (NTG, a reliable trigger of migraine in patients) administration in mice, indicating that LD-IL-2 and Treg cells engage both peripheral IL-10 and TGFβ signaling pathways to reverse chronic-migraine related sensitizations. In a