Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data

The capability of extracting useful information from documents and further transferring into knowledge is essential to advance technology innovations in industries. However, the overwhelming majority of scientific literature primarily published as unstructured human-readable formats is incompatible for machine analysis via contemporary artificial intelligence (AI) methods that effectively discovers knowledge from data. Therefore, the extraction approach transforming of unstructured data are fundamental in establishing state-of-the-art digital knowledge-based platforms. In this paper, we integrated multiple Python libraries and developed a method as a cohesive package for automated data extraction and quick processing to convert unstructured documents into machine-interpretable data. Transformed data can be further incorporated with AI analytical methods. The output files have shown excellent quality of digitalised data without major flaws in terms of context inconsistency. All scripts were written in Python with functional modules providing easy accessibility and proficiency to achieve objectives. Eventually, the finalised well-structured data can be implemented for further knowledge discovery

Compromise principle based methods of identifying capacities in the framework of multicriteria decision analysis

The basic aim of the compromise principle employed in this paper is to seek the capacity by which each alternative has a relatively equal chance to reach as close as possible to its highest reachable overall evaluation. According to the compromise principle, three types of capacity identification methods - the simple arithmetic average based compromise method, the least squares based compromise method and the linear programming based compromise method - are proposed. The input information required for the compromise principle based identification methods consists of the preference information with respect to the decision criteria and the partial evaluations of the alternatives provided by the decision maker. An illustrative example is given to show the processes of the proposed methods, and a comparison analysis with the maximum entropy principle based identification method is also presented. © 2014 Elsevier B.V

Synthetic minority oversampling technique and fractal dimension for identifying multiple sclerosis

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski–Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy

Pyramid: Enabling Hierarchical Neural Networks with Edge Computing

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared with centralized cloud AI, edge AI enables low-latency ML inference which is critical to many delay-sensitive web applications, e.g., web AR/VR, web gaming and Web-of-Things applications. Existing studies of edge AI focused on resource and performance optimization in training and inference, leveraging edge computing merely as a tool to accelerate training and inference processes. However, the unique ability of edge computing to process data with context awareness, a powerful feature for building the web-of-things for smart cities, has not been properly explored. In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. We motivate and present Pyramid with traffic prediction as an illustrative example, and evaluate it through extensive experiments conducted on two real-world datasets. The results demonstrate the superior performance of Pyramid neural networks in hierarchical traffic prediction and weather analysis

Synthetic minority oversampling technique and fractal dimension for identifying multiple sclerosis

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski–Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy

Ti-adsorption induced strain release in promoting α-Al nucleation at TiB2 - Al interfaces

An ab-initio study of (0001) TiB2/Al-Ti interface reveals the effect of Ti-adsorption on enhancement of α-Al nucleation. Ti-adsorbed TiB2/Al atomistic interfacial models were established to simulate Ti-terminated TiB2/Al and B-terminated TiB2/Al interfaces. It is found that formation of Al3Ti intermediate layer initiates dislocations at the interfaces. Such an effect promotes the release of interfacial strain and free-growth of α-Al, thus enhancing the nucleation potency. Ti-adsorption plays a critical role in promoting strain release at the (0001) TiB2/Al-Ti interface, which also ensures the lattice-mismatch superiority of Al3Ti structure. It is proposed that the effect of solute-adsorption on the interfacial strain-release and lattice-mismatch shall be both considered when designing the substrate-solute pairs to enhance nucleation potency

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible

Ti-adsorption induced strain release in promoting α-Al nucleation at TiB2 - Al interfaces

An ab-initio study of (0001) TiB2/Al-Ti interface reveals the effect of Ti-adsorption on enhancement of α-Al nucleation. Ti-adsorbed TiB2/Al atomistic interfacial models were established to simulate Ti-terminated TiB2/Al and B-terminated TiB2/Al interfaces. It is found that formation of Al3Ti intermediate layer initiates dislocations at the interfaces. Such an effect promotes the release of interfacial strain and free-growth of α-Al, thus enhancing the nucleation potency. Ti-adsorption plays a critical role in promoting strain release at the (0001) TiB2/Al-Ti interface, which also ensures the lattice-mismatch superiority of Al3Ti structure. It is proposed that the effect of solute-adsorption on the interfacial strain-release and lattice-mismatch shall be both considered when designing the substrate-solute pairs to enhance nucleation potency

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible

The role of stathmin, a regulator of mitosis, in hematopoiesis

Introduction Stathmin is a 17KDa cytosolic protein that plays an important role in the regulation of microtubule dynamics, mitotic spindle formation, cell cycle progression and cell differentiation. Stathmin knockout (KO) mice were initially reported to have a normal phenotype but were subsequently shown to develop an age-related neurological phenotype with axonopathy evident in both central and peripheral nervous systems. These mice were also shown to have a defect in recovery from acute ischemic renal injury. We had previously shown that stathmin plays an important role in the differentiation and proliferation of megakaryocytes (MK) and that down-regulation of stathmin is necessary for the maturation of MK and platelet production in vitro. In this study, we investigated the role of stathmin in megakaryopoiesis and hematopoiesis in vivo using the stathmin KO mouse as an experimental model. Results Stathmin KO mice had lower platelet (PLT) counts at 3 weeks of age when compared to WT mice. The WT mice had a mean PLT count of 662 ± 27 K/μL while KO mice had a mean PLT count of 543 ± 37 K/μL. This correlated with larger and fewer MK in the bone marrow of KO mice (WT: 4.2 ± 0.7 MK/40X field; KO: 3.6 ± 0.2 MK/40X field). Furthermore, in the spleen, there was a 10 fold decrease in the number of MK in KO mice compared to WT mice (6.6 ± 0.6 vs 0.7 ± 0.1 MK/40X field). By 8 weeks, PLT counts and MK size and numbers in the bone marrow and spleen were similar in WT and KO mice. Interestingly, by 16 weeks, the mean PLT of KO mice became significantly higher than that of WT and by 40 weeks, the mean PLT count of KO mice was 1379 ± 100K/μL compared to 1045 ± 120K/μL in WT mice (P<0.05). Microscopic analysis of the bone marrow at 46 weeks of age showed approximately 50% more MK in KO mice compared to WT mice. Differences in red blood cell counts (RBC) were also observed. While at 3 weeks, there were no significant differences between the 2 groups, at 8 weeks, KO mice had significantly lower RBC counts, hemoglobin levels (Hb) and hematocrit (HCT). This trend continued until the last measurement recorded at 40 weeks. Mean RBC in WT mice was 10.5 ± 0.1M/μL compared to 8.9 ± 0.2M/μL in KO mice. The mean corpuscular volume (MCV) and the red blood cell distribution width (RDW) were consistently higher in KO mice than in WT mice. No significant differences were noted in white blood cell counts. Bone marrow cell counts were significantly lower in KO mice when compared to WT mice at different ages from 3–40 weeks. Progenitor cell assays from 10–12 week old animals have shown that bone marrow from KO mice produce significantly fewer BFU-E and Pre-B colonies while no differences were observed in CFU-GMs. Conclusions The phenotypic characteristics of stathmin KO mice confirmed our prior in vitro findings that suggested a role for stathmin in megakaryopoiesis. We expected to see a decrease in the number of platelets and MK coupled with an increase in MK size. This was confirmed in stathmin KO mice at 3 weeks of age. However, we did not expect to see the marked increase in the number of platelets and MK that was observed as the mice aged. The exact mechanism for this has not been identified. Interestingly, the stathmin KO mice exhibited characteristic features of megaloblastic anemia including mild anemia and a significant increase in MCV and RDW. The megaloblastic anemia that is seen in the presence of B12 and folate deficiency results from interference with DNA synthesis resulting in asynchronous maturation of the nucleus and the cytoplasm. We believe a similar phenomenon is occurring in the stathmin KO mice. The deficiency of stathmin results in aberrant exit from mitosis, thereby delaying nuclear maturation and resulting in the megaloblastic features. Thus, the deficiency of stathmin in the KO mice results in two hematopoietic phenotypes that are seen in humans, megaloblastic anemia and thrombocytosis. It is unclear whether mutations of stathmin in humans might result in similar phenotypes. This is a question that will require further investigation. Future studies will investigate the compensatory mechanisms that result in the switch from decreased to increased platelet production as the mice age. Furthermore, examining the effects of hematopoietic stress (e.g. response to chemotherapy or bleeding) in stathmin KO mice might also elucidate a role for stathmin in the recovery from hematopoietic injury as was seen in acute ischemic renal injury