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The soldering reactions of three-segmented Sn/Cu/Sn and Cu/Sn/Cu diffusion couples are monitored by in-situ transmission electron microscopy to reveal the metallurgical reaction mechanism and the associated phase transformation pathway. For Sn/Cu/Sn diffusion couples, there is no ε-Cu3Sn formation due to the relatively insufficient Cu as compared to Sn. Kirkendall voids form initially in the Cu segment and then disappear due to the volume expansion associated with the continued intermetallic compound (IMC) formation as the reaction progresses. The incoming Sn atoms react with Cu to form η-Cu6Sn5, and the continuous reaction then transforms the entire nanowire to η-Cu6Sn5 grains with remaining Sn. With continued heating slightly above the melting point of Sn, an Sn-rich liquid phase forms between η-Cu6Sn5 grains. By contrast, the reaction in the Cu/Sn/Cu diffusion couples results in the intermetallic phases of both Cu3Sn and Cu6Sn5 and the development of Cu6Sn5 bulges on Cu3Sn grains. Kirkendall voids form in the two Cu segments, which grow and eventually break the nanowire into multiple segments. [ABSTRACT FROM AUTHOR]

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are insufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. [ABSTRACT FROM AUTHOR]

One dimensional tin/indium (Sn/In) nanowires have been synthesized through a surfactant assisted chemical reduction method in aqueous solutions at low temperature (∼0 °C). Synthetic parameters such as stirring speed, injection rate of reducing agent, and alloy ratio were used to control the size and aspect ratio of the resulting nanowires. It was observed that the diameter of the nanowires was around 70 nm for all the synthesis conditions that were studied, whereas the length of the nanowires can be controlled in the range of 400 nm to 2 μm. The morphology and structure of the Sn/In nanowires were characterized by field-emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HRTEM), and X-ray powder diffraction (XRD), and their thermal properties were measured by differential scanning calorimetry (DSC). Structure analysis indicated that the Sn/In nanowires were mainly composed of InSn 4 and Sn; whereas no In 3 Sn phase was observed in the nanowires. DSC results confirmed the structures of the Sn/In nanowires that were measured through XRD and indicated that the nanowires started to melt at a relatively low temperature around 118 °C. Finally, a growth mechanism based on the reaction and structural evolution was proposed to explain the nanowire formation and growth process. [ABSTRACT FROM AUTHOR]

Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In this paper, we propose a novel subspace learning framework for incomplete and unlabeled multi-view data. The model directly optimizes the class indicator matrix, which establishes a bridge for incomplete feature sets. Besides, feature selection is considered to deal with high dimensional and noisy features. Furthermore, the inter-view and intra-view data similarities are preserved to enhance the model. To these ends, an objective is developed along with an efficient optimization strategy. Finally, extensive experiments are conducted for multi-view clustering and cross-modal retrieval, achieving the state-of-the-art performance under various settings. [ABSTRACT FROM AUTHOR]

The isothermal metallurgical reaction in two-segmented Cu-Sn nanowires results in the formation of a Sn/Cu 6 Sn 5 /Cu 3 Sn/Cu sandwich structure. In-situ transmission electron microscopy is used to study how Cu 6 Sn 5 /Sn and Cu/Cu 3 Sn interfaces propagate and change shape during the intermetallic compound growth. The Cu 6 Sn 5 /Sn interface is observed to evolve from an inclined configuration to a vertical, edge-on configuration with the propagation of the Cu 6 Sn 5 phase towards the Sn segment. The Cu/Cu 3 Sn interface also becomes less inclined as it propagates toward the Cu segment. This interface evolution is driven by the minimization of the interface energy associated with minimizing the interface area associated with the edge-on interface. The Kirkendall void growth induces the breakage of the Cu segment and results in the Cu 3 Sn → Cu 6 Sn 5 transformation with the final Sn/Cu 6 Sn 5 /void/Cu sandwich structure. [ABSTRACT FROM AUTHOR]

Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually find a low-dimensional embedding of multi-view data, but often ignore some useful prior information that can be utilized to better discover the latent group structure of multi-view data. To alleviate this problem, a novel pairwise sparse subspace representation model for multi-view clustering is proposed in this paper. The objective function of our model mainly includes two parts. The first part aims to harness prior information to achieve a sparse representation of each high-dimensional data point with respect to other data points in the same view. The second part aims to maximize the correlation between the representations of different views. An alternating minimization method is provided as an efficient solution for the proposed multi-view clustering algorithm. A detailed theoretical analysis is also conducted to guarantee the convergence of the proposed method. Moreover, we show that the must-link and cannot-link constraints can be naturally integrated into the proposed model to obtain a link constrained multi-view clustering model. Extensive experiments on five real world datasets demonstrate that the proposed model performs better than several state-of-the-art multi-view clustering methods. [ABSTRACT FROM AUTHOR]

The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis. [ABSTRACT FROM AUTHOR]

Highlights: [•] Reduction of CuO nanowires results in the formation of a CuO/Cu2O hybrid structure. [•] The surface of CuO nanowires is decorated by embedded Cu2O particles. [•] Reduction of CuO nanowires occurs via nucleation and growth of Cu2O particles. [ABSTRACT FROM AUTHOR]

• An exclusivity term is introduced to enforce the diversity of different views. • Multi-view subspace representation is constrained to find the cluster structure. • An effective optimization algorithm is proposed to solve the objective function. • Experiments demonstrate that our algorithm outperforms state-of-the-art approaches. Multi-view subspace clustering algorithms have recently been developed to process multi-view dataset clustering by accurately depicting the essential characteristics of multi-view data. Most existing methods focus on conduct self-representation property using a consistent representation and a set of specific representations with well-designed regularization to learn the common and specific knowledge among different views. However, specific representations only contain the unique information of each individual view, which limits their ability to fully excavate the diversity of multi-view data to enhance the complementarity among different views. Moreover, when conducting multi-view subspace clustering, the learned subspace self-representation and clustering are sequential and independent, which lacks consideration of the interaction between representation learning and the final clustering calculation. In this paper, a novel method termed consistent and diverse multi-view subspace clustering with structure constraint (CDMSC 2) is proposed to overcome the above-described deficiencies. (1) An exclusivity constraint term is employed to enhance the diversity of specific representations among different views for modeling consistency and diversity in a unified framework. (2) A clustering structure constraint is imposed on the subspace self-representation by factorizing the learned subspace self-representation into the cluster centroids and the cluster assignments with the goal of obtaining a clustering-oriented subspace self-representation. In addition, we carefully designed an efficient optimization algorithm to solve the objective function through relaxation and alternating minimization. Extensive experiments on five benchmark datasets in terms of six evaluation metrics demonstrate that our method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot. [ABSTRACT FROM AUTHOR]

The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, F 1 score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals. [ABSTRACT FROM AUTHOR]

AbstractThe peroxidase-like properties of a graphene oxide/platinum-cobalt nanocomposite and the rapid color development mechanism of 3,3′,5,5′-tetramethylbenzidine (TMB) through the decomposition of ·OH from H2O2 were utilized to prepare a nonlabelled, simple, and sensitive paper-based colorimetric sensor. This sensor allows visualization of the results and instantaneous detection, enabling quantitative measurement of H2O2. The graphene oxide/platinum cobalt composite was synthesized using a two-step procedure. Its properties were investigated using transmission electron microscopy (TEM), Raman spectroscopy, and energy-dispersive X-ray spectroscopy (EDS). The composite was subsequently transferred onto a paper substrate to create the colorimetric sensor. The optimal catalytic conditions were a composite concentration of 201.17 µg/mL, a color development time of 3 min, a TMB concentration of 2 mmol·L−1, and a pH of 4. Using the optimal conditions, the paper-based colorimetric sensor has a linear range for H2O2 from 1.0 × 10−5 to 0.1 mol·L−1, with a limit of detection of 1.0 × 10−6 mol·L−1 which is comparable or better than comparable methods. This paper-based colorimetric sensor has potential applications for the rapid determination of hydrogen peroxide. [ABSTRACT FROM AUTHOR]

In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the sketch community – that annotated supervisory data are difficult to obtain. This problem is very challenging in which sketches are highly abstract and subject to different drawing styles, making existing solutions tailored for photos unsuitable. Key for the success of our self-supervised learning paradigm lies with our sketch-specific designs: (i) we propose a set of pretext tasks specifically designed for sketches that mimic different drawing styles, and (ii) we further exploit the use of the textual convolution network (TCN) together with the convolutional neural network (CNN) in a dual-branch architecture for sketch feature learning, as means to accommodate the sequential stroke nature of sketches. We demonstrate the superiority of our sketch-specific designs through two sketch-related applications (retrieval and recognition) on a million-scale sketch dataset, and show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods, and significantly narrows the performance gap between with supervised representation learning. 1 PyTorch code of this work is available at https://github.com/zzz1515151/self-supervised_learning_sketch. [ABSTRACT FROM AUTHOR]

Abstract Compared with the abundant achievements of technological innovation, management innovation is still under-researched in academia. Putting management innovation in the context of environmental issues, this paper seeks to answer two key questions: (1) what factors stimulate green management innovation and (2) how can green management innovation affect firm performance. On the basis of literature review an operational definition of green management innovation is given. Then the relationships between green management innovation, its antecedents and consequences are hypothesized. A total of 267 valid questionnaires from mining industry in China are used. Different from the market pull and technology push of technological innovation, green management innovation is mainly pushed by external knowledge supply, while the internal efficiency demand does not play a significant role in our findings. Moreover, green management innovation has a positive effect on firm economic performance and internal efficiency demand moderates the effect. Our findings provide complementary insights to green management innovation. [ABSTRACT FROM AUTHOR]

The volumetric energy density of today's lithium-ion batteries is limited mostly by the graphitic carbon anode. Silicon is a promising replacement but its excessive volume expansion on lithiation limits its long-term cyclability performance. A nano-sized aluminium containing silicon, leached in acid, with a porous structure is shown to maintain its capacity higher than pure bulk silicon or nano-sized silicon by over 700 mAh/g. The capacity of leached silicon is maintained at 1400 mAh/g for more than 60 cycles. X-ray diffraction, scanning electron microscopy, transmission electron microscopy and nuclear magnetic resonance spectroscopy have been used to correlate the electrochemical performance with the materials' morphology and composition. [ABSTRACT FROM AUTHOR]

Focusing on green technology innovation, the paper researches its motivation and implementation results from the perspective of internal corporate management. The impact of top manager team faultlines on green technology innovation and the moderating role of environmental information disclosure in the relationship between green technology innovation and financial performance are discussed. Using listed manufacturing corporate data obtained from China Stock Market & Accounting Research database, the hypotheses are tested. It is found that there is an inverted U-shaped relationship between task-related faultline and green technology innovation, while bio-demographic faultline has no significant influence on green technology innovation. In addition, both green technology innovation and environmental information disclosure have positive effects on financial performance, while the moderating effect of environmental information disclosure on innovation-performance doesn't exist. Combined with the research results, the practical implications for policy makers and managers and future research avenue are proposed. • It expands the achievements of the existing GTI research by studying its drivers from the perspective of top managers. • The inverted U-shaped relationship between top management team faultline and green technology innovation is posited and verified. • Environmental information disclosure is integrated in our model when analyzing the green technology innovation-financial performance relationship. [ABSTRACT FROM AUTHOR]

We report in situ atomic-scale transmission electron microscopy observations of the surface dynamics during Cu2O reduction. We show inhomogeneous oxide reduction caused by the preferential adsorption of hydrogen at step edges that induces oxygen loss and destabilizes Cu atoms within the step edge, thereby resulting in the retraction motion of atomic steps at the oxide surface. [ABSTRACT FROM AUTHOR]