A Note on Two-Agent Scheduling with Resource Dependent Release Times on a Single Machine

We consider a scheduling problem in which both resource dependent release times and two agents exist simultaneously. Two agents share a common single machine, and each agent wants to minimize a cost function dependent on its own jobs. The release time of each A-agent's job is related to the amount of resource consumed. The objective is to find a schedule for the problem of minimizing A-agent's total amount of resource consumption with a constraint on B-agent's makespan. The optimal properties and the optimal polynomial time algorithm are proposed to solve the scheduling problem

A Note on Two-Agent Scheduling with Resource Dependent Release Times on a Single Machine

We consider a scheduling problem in which both resource dependent release times and two agents exist simultaneously. Two agents share a common single machine, and each agent wants to minimize a cost function dependent on its own jobs. The release time of each A-agent’s job is related to the amount of resource consumed. The objective is to find a schedule for the problem of minimizing A-agent’s total amount of resource consumption with a constraint on B-agent’s makespan. The optimal properties and the optimal polynomial time algorithm are proposed to solve the scheduling problem

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively

Deep Learning for Facial Beauty Prediction

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts

A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times

In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm

Empagliflozin in Patients with Chronic Kidney Disease

Background The effects of empagliflozin in patients with chronic kidney disease who are at risk for disease progression are not well understood. The EMPA-KIDNEY trial was designed to assess the effects of treatment with empagliflozin in a broad range of such patients. Methods We enrolled patients with chronic kidney disease who had an estimated glomerular filtration rate (eGFR) of at least 20 but less than 45 ml per minute per 1.73 m(2) of body-surface area, or who had an eGFR of at least 45 but less than 90 ml per minute per 1.73 m(2) with a urinary albumin-to-creatinine ratio (with albumin measured in milligrams and creatinine measured in grams) of at least 200. Patients were randomly assigned to receive empagliflozin (10 mg once daily) or matching placebo. The primary outcome was a composite of progression of kidney disease (defined as end-stage kidney disease, a sustained decrease in eGFR to < 10 ml per minute per 1.73 m(2), a sustained decrease in eGFR of & GE;40% from baseline, or death from renal causes) or death from cardiovascular causes. Results A total of 6609 patients underwent randomization. During a median of 2.0 years of follow-up, progression of kidney disease or death from cardiovascular causes occurred in 432 of 3304 patients (13.1%) in the empagliflozin group and in 558 of 3305 patients (16.9%) in the placebo group (hazard ratio, 0.72; 95% confidence interval [CI], 0.64 to 0.82; P < 0.001). Results were consistent among patients with or without diabetes and across subgroups defined according to eGFR ranges. The rate of hospitalization from any cause was lower in the empagliflozin group than in the placebo group (hazard ratio, 0.86; 95% CI, 0.78 to 0.95; P=0.003), but there were no significant between-group differences with respect to the composite outcome of hospitalization for heart failure or death from cardiovascular causes (which occurred in 4.0% in the empagliflozin group and 4.6% in the placebo group) or death from any cause (in 4.5% and 5.1%, respectively). The rates of serious adverse events were similar in the two groups. Conclusions Among a wide range of patients with chronic kidney disease who were at risk for disease progression, empagliflozin therapy led to a lower risk of progression of kidney disease or death from cardiovascular causes than placebo