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The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.

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For high-rate maximum distance separable (MDS) codes, most early constructions can only optimally repair all the systematic nodes but not for all the parity nodes initially. Fortunately, this issue was firstly solved by Li et al. in (IEEE Trans. Inform. Theory, 64(9), [6257][6258][6259][6260][6261][6262][6263][6264][6265][6266][6267] 2018), where a very powerful transformation that can convert any nonbinary MDS code into another MDS code with desired properties was proposed. However, the transformation does not work for binary MDS codes. In this note, we address this issue by proposing another generic transformation that can convert any (n, k) binary MDS code into a new binary MDS code, which endows any r = n − k chosen nodes with the optimal repair bandwidth and the optimal rebuilding access properties, and at the same time, preserves the normalized repair bandwidth and the normalized rebuilding access for the remaining k nodes under some conditions. As two immediate algorithms of this transformation, we show that 1) by applying the transformation multiple times, any (n, k) binary MDS code can be converted into an (n, k) binary MDS code with the optimal repair bandwidth and the optimal rebuilding access for all nodes, 2) any binary MDS code with the optimal repair bandwidth or the optimal rebuilding access for the systematic nodes only can be converted into an MDS code with the corresponding repair optimality for all nodes. Index TermsDistributed storage, high-rate, binary MDS codes, optimal rebuilding access, optimal repair bandwidth. the number of surviving nodes contacted during the repair process. Since 2010, various MDS codes with the optimal repair J. Li is with the ). 2 bandwidth have been proposed in the literature [8]-[27], where most works [9]-[22] consider the case d = n − 1 to maximally reduce the repair bandwidth, as γ * (d) is a decreasing function of d; this setting is also the focus of this work.However, at the practically more important range of high-rate case, i.e., k/n > 1/2, most early systematic code constructions can only optimally repair all the systematic nodes but not for all the parity nodes initially [9]- [17]. Fortunately, this issue was solved by Li et al.[28]-[30] firstly, where a very powerful transformation that can convert any nonbinary MDS code into another MDS code was proposed, which endows any r = n − k chosen nodes with the optimal repair bandwidth and the optimal rebuilding access properties, and at the same time, preserves the normalized repair bandwidth and the normalized rebuilding access for the remaining k nodes. The resultant code uses the same finite field as the base code. As two immediate applications of this transformation, it is showed that 1) any nonbinary MDS code with the optimal repair bandwidth or the optimal rebuilding access for the systematic nodes only can be converted into an MDS code with the corresponding repair optimality for all nodes, and 2) by applying the transformation multiple times, any (n, k) nonbinary scalar MDS code can be converted into a...

Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.