Completely Automated CNN Architecture Design Based on Blocks

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures

Unimodal polyethylenes of high linearity and narrow dispersity by using ortho-4,4′-dichlorobenzhydryl-modified bis(imino)pyridyl-iron catalysts

Six different examples of 4,4′-dichlorobenzhydryl-substituted 2,6-bis(arylimino)pyridyl-iron(ii) chloride complex, [2-{{2,6-((p-ClPh)2CH)2-4-MeC6H2}N = CMe}-6-(ArN CMe)C5H3N]FeCl2 (Ar = 2,6-Me2C6H3Fe1, 2,6-Et2C6H3Fe2, 2,6-iPr2C6H3Fe3, 2,4,6-Me3C6H2Fe4, 2,6-Et2-4-MeC6H2Fe5, 2,6-((p-ClPh)2CH)2-4-MeC6H2Fe6), have been synthesized in good yield and characterized by various spectroscopic and analytical techniques. The molecular structures of Fe2 and Fe5 emphasize the uneven steric protection of the ferrous center imposed by the unsymmetrical N,N,N′-chelate. When treated with either MAO or MMAO (modified-MAO) as activators, Fe1-Fe5 exhibited very high productivities at elevated temperature with peak performance of 21.59 × 106 g PE mol−1(Fe) h−1 for Fe5/MMAO at 50 °C and 15.65 × 106 g PE mol−1(Fe) h−1 for Fe1/MAO at 60 °C. By contrast, the most sterically hindered Fe6 was either inactive (using MAO) or displayed very low activity (using MMAO). As a further feature, this class of iron catalyst was capable of displaying long lifetimes with catalytic activities up to 10.77 × 106 g PE mol−1(Fe) h−1 observed after 1 h. In all cases, strictly linear and unimodal polyethylene was formed with narrow dispersity, while the polymer molecular weight was strongly influenced by the aluminoxane co-catalyst (Mw using MAO > MMAO) and also by the steric properties of the second N-aryl group (up to 32.9 kg mol−1 for Fe3/MAO)

A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

© 1997-2012 IEEE. Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-The-Art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customised convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines

Exceptionally high molecular weight linear polyethylene by using N,N,N′-Co catalysts appended with a N′-2,6-bis{di(4-fluorophenyl)methyl}-4-nitrophenyl group

The bis(arylimino)pyridines, 2-[CMeN{2,6-{(4-FC6H4)2CH}2–4-NO2}]-6-(CMeNAr)C5H3N (Ar = 2,6-Me2C6H3 L1, 2,6-Et2C6H3 L2, 2,6-i-Pr2C6H3 L3, 2,4,6-Me3C6H2 L4, 2,6-Et2–4-MeC6H2 L5), each containing one N′-2,6-bis{di(4-fluorophenyl)methyl}-4-nitrophenyl group, have been synthesized by two successive condensation reactions from 2,6-diacetylpyridine. Their subsequent treatment with anhydrous cobalt (II) chloride gave the corresponding N,N,N′-CoCl2 chelates, Co1 – Co5, in excellent yield. All five complexes have been characterized by 1H/19F NMR and IR spectroscopy as well as by elemental analysis. In addition, the molecular structures of Co1 and Co3 have been determined and help to emphasize the differences in steric properties imposed by the inequivalent N-aryl groups; distorted square pyramidal geometries are adopted by each complex. Upon activation with either methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), precatalyts Co1 – Co5 collectively exhibited very high activities for ethylene polymerization with 2,6-dimethyl-substituted Co1 the most active (up to 1.1 × 107 g (PE) mol−1 (Co) h−1); the MAO systems were generally more productive. Linear polyethylenes of exceptionally high molecular weight (Mw up to 1.3 × 106 g mol−1) were obtained in all cases with the range in dispersities exhibited using MAO as co-catalyst noticeably narrower than with MMAO [Mw/Mn: 3.55–4.77 (Co1 – Co5/MAO) vs. 2.85–12.85 (Co1 – Co5/MMAO)]. Significantly, the molecular weights of the polymers generated using this class of cobalt catalyst are higher than any literature values reported to date using related N,N,N-bis (arylimino)pyridine-cobalt catalysts

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

© 1997-2012 IEEE. Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically handcrafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, the existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows the promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations but also achieves the best prediction among the peer performance predictors

Plastomeric-like polyethylenes achievable using thermally robust N,N'-nickel catalysts appended with electron withdrawing difluorobenzhydryl and nitro groups

A new set of five unsymmetrical N,N'-diiminoacenaphthenes, 1-[2,6-{(4-FC6H4)2CH}2-4-NO2C6H4N]-2-(ArN)C2C10H6 (Ar = 2,6-Me2C6H3L1, 2,6-Et2C6H3L2, 2,6-iPr2C6H3L3, 2,4,6-Me3C6H2L4, 2,6-Et2-4-MeC6H2L5), have been synthesized and used to prepare their corresponding nickel(ii) halide complexes, LNiBr2 (Ni1-Ni5) and LNiCl2 (Ni6-Ni10). The molecular structures of Ni3(OH2) and Ni4 reveal distorted square pyramidal and tetrahedral geometries, respectively, while the 1H NMR spectra of all the nickel(ii) (S = 1) complexes show broad paramagnetically shifted peaks. Upon activation with either methylaluminoxane (MAO) or ethylaluminum sesquichloride (Et3Al2Cl2, EASC), Ni1-Ni10 displayed very high activities for ethylene polymerization with the optimal performance being observed using 2,6-dimethyl-containing Ni1 in combination with EASC (1.66 × 107 g PE mol-1 (Ni) h-1 at 50 °C) which produced high molecular weight plastomeric polyethylene (Mw = 3.93 × 105 g mol-1, Tm = 70.6 °C) with narrow dispersity (Mw/Mn = 2.97). Moreover, Ni1/EASC showed good thermal stability by operating effectively at an industrially relevant 80 °C with a level of activity (6.01 × 106 g of PE mol-1 (Ni) h-1) that exceeds previously disclosed N,N'-nickel catalysts under comparable reaction conditions. This improved thermal stability and activity has been ascribed to the combined effects imparted by the para-nitro and fluoride-substituted benzhydryl ortho-substituents

An Anonymous Authentication and Key Agreement Protocol in Smart Living

Wireless Sensor Networks (WSNs) play an indispensable role in the application of smart homes, smart healthcare, and precision agriculture. However, WSNs confront privacy risks that hinder its practical applications. The leakage of privacy is one of the key factors to restrict the development of WSNs. Hence, in this paper, we propose an Anonymous Authentication and Key Agreement protocol (AAKA) to accomplish identity authentication and privacy protection. Based on the dynamic sequence number, the shared secret value, and the dynamic random number, the AAKA protocol implements a two-way authentication and keys negotiation among users, gateway, and sensors, which achieves the secure access control of legitimate users to WSNs and ensures the confidential transmission of data over the public channel. We perform the security proof with BAN logic for security evaluation. The performance analysis demonstrates that compared with other WSNs authentication schemes, the AAKA protocol obtained better security features, smaller storage, and more efficient communication. Therefore, it is more suitable for applications in smart living

Moderately branched ultra-high molecular weight polyethylene by using N,N′-nickel catalysts adorned with sterically hindered dibenzocycloheptyl groups

Five examples of unsymmetrical 1,2-bis (arylimino) acenaphthene (L1 – L5), each containing one N-2,4-bis (dibenzocycloheptyl)-6-methylphenyl group and one sterically and electronically variable N-aryl group, have been used to prepare the N,N′-nickel (II) halide complexes, [1-[2,4-{(C 15 H 13 } 2 –6-MeC 6 H 2 N]-2-(ArN)C 2 C 10 H 6 ]NiX 2 (X = Br: Ar = 2,6-Me 2 C 6 H 3 Ni1, 2,6-Et 2 C 6 H 3 Ni2, 2,6-i-Pr 2 C 6 H 3 Ni3, 2,4,6-Me 3 C 6 H 2 Ni4, 2,6-Et 2 –4-MeC 6 H 2 Ni5) and (X = Cl: Ar = 2,6-Me 2 C 6 H 3 Ni6, 2,6-Et 2 C 6 H 3 Ni7, 2,6-i-Pr 2 C 6 H 3 Ni8, 2,4,6-Me 3 C 6 H 2 Ni9, 2,6-Et 2 –4-MeC 6 H 2 Ni10), in high yield. The molecular structures Ni3 and Ni7 highlight the extensive steric protection imparted by the ortho-dibenzocycloheptyl group and the distorted tetrahedral geometry conferred to the nickel center. On activation with either Et 2 AlCl or MAO, Ni1 – Ni10 exhibited very high activities for ethylene polymerization with the least bulky Ni1 the most active (up to 1.06 × 10 7  g PE mol −1 (Ni) h −1 with MAO). Notably, these sterically bulky catalysts have a propensity towards generating very high molecular weight polyethylene with moderate levels of branching and narrow dispersities with the most hindered Ni3 and Ni8 affording ultra-high molecular weight material (up to 1.5 × 10 6  g mol −1 ). Indeed, both the activity and molecular weights of the resulting polyethylene are among the highest to be reported for this class of unsymmetrical 1,2-bis (imino)acenaphthene-nickel catalyst

Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification

In hyperspectral image processing, classification is one of the most popular research topics. In recent years, research progress made in deep-learning-based hierarchical feature extraction and classification has shown a great power in many applications. In this paper, we propose a novel local spatial sequential (LSS) method, which is used in a recurrent neural network (RNN). Using this model, we can extract local and semantic information for hyperspectral image classification. First, we extract low-level features from hyperspectral images, including texture and differential morphological profiles. Second, we combine the low-level features together and propose a method to construct the LSS features. Afterwards, we build an RNN and use the LSS features as the input to train the network for optimizing the system parameters. Finally, the high-level semantic features generated by the RNN is fed into a softmax layer for the final classification. In addition, a nonlocal spatial sequential method is presented for the recurrent neural network model (NLSS-RNN) to further enhance the classification performance. NLSS-RNN finds nonlocal similar structures to a given pixel and extracts corresponding LSS features, which not only preserve the local spatial information, but also integrate the information of nonlocal similar samples. The experimental results on three publicly accessible datasets show that our proposed method can obtain competitive performance compared with several state-of-the-art classifiers