Joint Quantization and Diffusion for Compressed Sensing Measurements of Natural Images

Recent research advances have revealed the computational secrecy of the compressed sensing (CS) paradigm. Perfect secrecy can also be achieved by normalizing the CS measurement vector. However, these findings are established on real measurements while digital devices can only store measurements at a finite precision. Based on the distribution of measurements of natural images sensed by structurally random ensemble, a joint quantization and diffusion approach is proposed for these real-valued measurements. In this way, a nonlinear cryptographic diffusion is intrinsically imposed on the CS process and the overall security level is thus enhanced. Security analyses show that the proposed scheme is able to resist known-plaintext attack while the original CS scheme without quantization cannot. Experimental results demonstrate that the reconstruction quality of our scheme is comparable to that of the original one.Comment: 4 pages, 4 figure

A Recommender System Approach for Very Large-scale Multiobjective Optimization

We define very large multi-objective optimization problems to be multiobjective optimization problems in which the number of decision variables is greater than 100,000 dimensions. This is an important class of problems as many real-world problems require optimizing hundreds of thousands of variables. Existing evolutionary optimization methods fall short of such requirements when dealing with problems at this very large scale. Inspired by the success of existing recommender systems to handle very large-scale items with limited historical interactions, in this paper we propose a method termed Very large-scale Multiobjective Optimization through Recommender Systems (VMORS). The idea of the proposed method is to transform the defined such very large-scale problems into a problem that can be tackled by a recommender system. In the framework, the solutions are regarded as users, and the different evolution directions are items waiting for the recommendation. We use Thompson sampling to recommend the most suitable items (evolutionary directions) for different users (solutions), in order to locate the optimal solution to a multiobjective optimization problem in a very large search space within acceptable time. We test our proposed method on different problems from 100,000 to 500,000 dimensions, and experimental results show that our method not only shows good performance but also significant improvement over existing methods.Comment: 12 pages, 6 figure

An Effective Ensemble Framework for Multi-Objective Optimization

This work was supported by the National Natural Science Foundation of China under Grants 61876110, 61876163, and 61836005, a grant from ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityU101/16), the Joint Funds of the National Natural Science Foundation of China under Key Program Grant U1713212, and CONACyT grant no. 221551.Peer reviewedPostprin

The prognostic value of prognostic nutritional index in postoperative onset of PAH in children with isolated VSD: a prospective cohort study based on propensity score matching analysis

BackgroundThe mechanism of pulmonary arterial hypertension (PAH) after surgery/intervention for isolated venticlular septal defect (VSD) in children is unknown. Reliable prognostic indicators for predicting postoperative PAH are urgently needed. Prognostic nutration index (PNI) is widely used to predict postoperative complications and survival in adults, but it is unclear whether it can be used as an indicator of prognosis in children.MethodsA total of 251 children underwent VSD repair surgery or interventional closure in Hunan Children's Hospital from 2020 to 2023 were collected. A 1:1 propensity score matching (PSM) analysis was performed using the nearest neighbor method with a caliper size of 0.2 Logistics regression analysis is used to examine factors associated with the development of PAH.ResultsThe cut-off value for PNI was determined as 58.0. After 1:1 PSM analysis, 49 patients in the low PNI group were matched with high PNI group. Children in the low PNI group had higher risk of postoperative PAH (P = 0.002) than those in the high PNI group. Multivariate logistics regression analysis showed that PNI (RR: 0.903, 95% CI: 0.816–0.999, P = 0.049) and tricuspid regurgitation velocity (RR: 4.743, 95% CI: 1.131–19.897, P = 0.033) were independent prognostic factors for the development of PAH.ConclusionPNI can be used as a prognostic indicator for PAH development after surgery/intervention in children with isolated VSD

A case report of multiple artery pseudoaneurysms associated with SARS-CoV-2

Arterial pseudoaneurysms are rare vascular abnormalities that can occur as a complication of infections. Artery pseudoaneurysms associated with SARS-CoV-2 are a rare occurrence in COVID-19 patients, and their rupture can result in significant hemorrhage and sudden death. Few cases of SARS-CoV-2-associated artery pseudoaneurysms have been reported, and their underlying pathophysiological mechanisms remain unclear. This study presents the first reported case of a patient who developed both pulmonary and gallbladder artery pseudoaneurysms following SARS-CoV-2 infection. We investigate the potential pathogenesis of these pseudoaneurysms and aim to improve the understanding of this rare complication

Identification and functional analysis of SWEET gene family in Averrhoa carambola L. fruits during ripening

Sugar Will Eventually be Exported Transporters (SWEETs), a type of sugar efflux transporters, have been extensively researched upon due to their role in phloem loading for distant sugar transport, fruit development, and stress regulation, etc. Several plant species are known to possess the SWEET genes; however, little is known about their presence in Averrhoa Carambola L. (Oxalidaceae), an evergreen fruit crop (star fruit) in tropical and subtropical regions of Southeast Asia. In this study, we established an Averrhoa Carambola L. unigenes library from fruits of ‘XianMiyangtao’ (XM) by RNA sequencing (RNA-seq). A total of 99,319 unigenes, each longer than 200 bp with a total length was 72.00 Mb, were identified. A total of 51,642 unigenes (52.00%) were annotated. Additionally, 10 AcSWEET genes from the Averrhoa Carambola L. unigenes library were identified and classified, followed by a comprehensive analysis of their structures and conserved motif compositions, and evolutionary relationships. Moreover, the expression patterns of AcSWEETs in ‘XM’ cultivars during fruit ripening were confirmed using quantitative real-time PCR (qRT-PCR), combined with the soluble sugar and titratable acids content during ripening, showed that AcSWEET2a/2b and AcSWEET16b might participate in sugar transport during fruit ripening. This work presents a general profile of the AcSWEET gene family in Averrhoa Carambola L., which can be used to perform further studies on elucidating the functional roles of AcSWEET genes

APPLICATION OF NOVEL CLONAL ALGORITHM IN MULTIOBJECTIVE OPTIMIZATION

In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is performed in order to reduce the gaps between the solutions and the Pareto-optimal front. Based on this purpose, a cooling schedule is adopted in these approaches, reducing the parameters gradually to a minimal threshold, the aim of which is to keep a desirable balance between fine-grained search and coarse-grained search. By this means, the exploratory capabilities of NCMO are enhanced. When compared with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO has remarkable performance.Multiobjective optimization, immune algorithm, clonal selection, hybrid mutation

A hybrid immune multiobjective optimization algorithm

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently.Multiple objective programming Artificial immune systems Clonal selection principle Hybrid mutation Artificial intelligence