Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. Deep neural network architectures and computational issues have been ...well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.
Establishing a solid theoretical foundation for structured deep neural networks is greatly desired due to the successful applications of deep learning in various practical domains. This paper aims at ...an approximation theory of deep convolutional neural networks whose structures are induced by convolutions. To overcome the difficulty in theoretical analysis of the networks with linearly increasing widths arising from convolutions, we introduce a downsampling operator to reduce the widths. We prove that the downsampled deep convolutional neural networks can be used to approximate ridge functions nicely, which hints some advantages of these structured networks in terms of approximation or modeling. We also prove that the output of any multi-layer fully-connected neural network can be realized by that of a downsampled deep convolutional neural network with free parameters of the same order, which shows that in general, the approximation ability of deep convolutional neural networks is at least as good as that of fully-connected networks. Finally, a theorem for approximating functions on Riemannian manifolds is presented, which demonstrates that deep convolutional neural networks can be used to learn manifold features of data.
The knowledge and skills of employees could play a valuable role in organizational success. Organizations seek practices to create a knowledge-sharing culture to take full advantage of individual ...competencies. However, the knowledge-hiding behavior of individuals is a hurdle in the internal dissemination of knowledge and expertise. It becomes more critical in the case of teaching institutions, where the students are taught and trained. Scholars are now putting their efforts into seeking the antecedents and consequences of knowledge-hiding behavior. This study also attempts to determine the role of interpersonal distrust as an antecedent of knowledge hiding behavior of music education students. Based on the social exchange theory, the present study attempts to check the association of interpersonal distrust with emotional exhaustion and knowledge hiding. For empirical investigation, this study assumes that interpersonal distrust positively enhances knowledge hiding and emotional exhaustion, respectively. Moreover, the present study also attempts to check the association of emotional exhaustion with knowledge hiding. This study also assessed the mediating role of emotional exhaustion in the relationship between interpersonal distrust and knowledge hiding. This current study also aims to check the moderating role of mental health self-efficacy in the relationship between emotional exhaustion and knowledge hiding. For empirical investigation, the present study collected the data from 310 music learning students of various Chinese universities through a structured questionnaire method using a convenient sampling technique. This study applied partial least square structural equation modeling for empirical analyses using Smart PLS software. The findings of this study revealed that interpersonal distrust does not directly influence knowledge hiding; however, interpersonal distrust has a positive association with emotional exhaustion. The findings also acknowledged that emotional exhaustion positively correlates with knowledge hiding. The results also confirmed that emotional exhaustion positively mediates the relationship between interpersonal distrust and knowledge hiding. Further, the outcomes depicted that mental health self-efficacy negatively moderates the relationship between emotional exhaustion and knowledge hiding. In addition, this study’s findings also serve the literature of knowledge hiding by providing important theoretical and practical implications.
Full-color emissive carbon dots (CDs) hold a great promise for various applications, especially in light emitting diodes (LEDs). However, the existing synthetic routes for CDs are carried out in ...solutions, which suffer from low yields, high pressures, various byproducts, large amounts of waste solvents, and complicated photoluminescence (PL) origins. Therefore, it is necessary to explore large scale synthesis of CDs with high quantum yield (QY) across the entire visible range from a single carbon source by a solvent-free method. In this work, a series of CDs with tunable PL emission from 442 to 621 nm, QY of 23%–56%, and production yield within 34%–72%, are obtained by heating o-phenylenediamine with the catalysis of KCl. Detailed characterizations identify that, the differences between these CDs with respect to the graphitization degree, graphitic nitrogen content, and oxygen-containing functional groups, are responsible for their distinct optical properties, which can be modulated by controlling the deamination and dehydrogenation processes during reactions. Blue, green, yellow, red emissive films, and LEDs are prepared by dispersing the corresponding CDs into polyvinyl alcohol (PVA). All types of white LEDs (WLEDs) with high colorrendering- index (CRI), including warm WLEDs, standard WLEDs, and cool WLEDs, are also fabricated by mixing the red, green, and blue emissive CDs into PVA matrix by the appropriate ratios.
Herein we describe a rhodium-catalyzed (4+1) cyclization between cyclobutanones and allenes, which provides a distinct 4.2.1-bicyclic skeleton containing two quaternary carbon centers. The reaction ...involves C–C activation of cyclobutanones and employs allenes as a one-carbon unit. A variety of functional groups can be tolerated, and a diverse range of polycyclic scaffolds can be accessed. Excellent enantioselectivity can be obtained, which is enabled by a TADDOL-derived phosphoramidite ligand. The bridged bicyclic products can be further functionalized or derivatized though simple transformations.
The development and progression of human cancers are continuously and dynamically regulated by intrinsic and extrinsic factors. As a converging point of multiple oncogenic pathways, signal transducer ...and activator of transcription 3 (STAT3) is constitutively activated both in tumor cells and tumor-infiltrated immune cells. Activated STAT3 persistently triggers tumor progression through direct regulation of oncogenic gene expression. Apart from its oncogenic role in regulating gene expression in tumor cells, STAT3 also paves the way for human cancer growth through immunosuppression. Activated STAT3 in immune cells results in inhibition of immune mediators and promotion of immunosuppressive factors. Therefore, STAT3 modulates the interaction between tumor cells and host immunity. Accumulating evidence suggests that targeting STAT3 may enhance anti-cancer immune responses and rescue the suppressed immunologic microenvironment in tumors. Taken together, STAT3 has emerged as a promising target in cancer immunotherapy.
•STAT3 is constitutively activated in both tumor cells and immune cells.•Tumor progression can be triggered by STAT3 through direct regulation of oncogenes.•STAT3 also mediates human cancer growth through tumor-induced immunosuppression.•STAT3's dual role in both cancer and immunity renders it a promising target for cancer therapy.
As a promising luminescent nanomaterial, carbon dots (CDs) have received tremendous attention for their great potential in biomedical applications, owing to their distinctive merits of ease in ...preparation, superior optical properties, good biocompatibility, and adjustable modification in structure and functionalities. However, most of the reported CDs exhibit insufficient excitation and emission in red/near-infrared (R/NIR) regions, which significantly limits their practical applications in biomedical assays and therapy. In the latest years, extensive studies have been performed to produce CDs with intensified R/NIR excitation and emission by designed reactions and precise separations. This review article summarizes state-of-the-art progress towards design and manufacture of CDs with long-wavelength or multicolor emissions, involving their synthetic routes, precursors, and luminescence mechanisms. Meanwhile, the applicable availability of CDs in bioimaging, sensing, drug delivery/release, and photothermal/photodynamic therapy, is systematically overlooked. The current challenges concerning feasible controls over optical properties of CDs and their new opportunities in biomedical fields are discussed.
The synthesis, optical properties, and biomedical applications of carbon dots with red or near-infrared emissions are summarized. Display omitted
With the rapid development of the coal industry, the possibility of spontaneous combustion of coal in the process of mining, storage and transportation has gradually increased. Coal in the pile state ...is very easy to lead to the occurrence of coal spontaneous combustion disaster because of the good heat storage environment and ventilation conditions. In order to study the impact regulation on the heat pipe heat dissipation effect under different heat source input power conditions, the working performance of the heat pipe is investigated by using methanol working material and no working material as a contrast. The experiments were conducted to determine the suitable working heat source environment for the gravity heat pipe by comparing and analyzing the wall temperature of the gravity heat pipe under different heat source input power as well as the use of the working material. The results show that the copper-methanol gravity heat pipe can best control the temperature of the heat source location to continue to rise, destroy its heat storage environment, and suppress the self-heating of the coal pile. The heat dissipation effect of the copper-methanol gravity heat pipe at the heat source input power of 75 W. It shows that different working materials have corresponding working environment. This experimental study is conducted to be able to destroy the self-heating environment of coal in the process of piling, which has certain guiding significance for the improvement of coal pile spontaneous combustion prevention technology.
In this paper, we study data-dependent generalization error bounds that exhibit a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label ...classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as the regularizer. Key to our analysis is new structural results for multi-class Gaussian complexities and empirical ℓ ∞ -norm covering numbers, which exploit the Lipschitz continuity of the loss function with respect to the ℓ 2 - and ℓ ∞ -norm, respectively. We establish data-dependent error bounds in terms of the complexities of a linear function class defined on a finite set induced by training examples, for which we show tight lower and upper bounds. We apply the results to several prominent multi-class learning machines and show a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class support vector machine of Crammer and Singer (2002), we obtain a data-dependent bound with a logarithmic dependency, which is a significant improvement of the previous square-root dependency. The experimental results are reported to verify the effectiveness of our theoretical findings.
5G cellular networks are assumed to be the key enabler and infrastructure provider in the ICT industry, by offering a variety of services with diverse requirements. The standardization of 5G cellular ...networks is being expedited, which also implies more of the candidate technologies will be adopted. Therefore, it is worthwhile to provide insight into the candidate techniques as a whole and examine the design philosophy behind them. In this article, we try to highlight one of the most fundamental features among the revolutionary techniques in the 5G era, i.e., there emerges initial intelligence in nearly every important aspect of cellular networks, including radio resource management, mobility management, service provisioning management, and so on. However, faced with ever-increasingly complicated configuration issues and blossoming new service requirements, it is still insufficient for 5G cellular networks if it lacks complete AI functionalities. Hence, we further introduce fundamental concepts in AI and discuss the relationship between AI and the candidate techniques in 5G cellular networks. Specifically, we highlight the opportunities and challenges to exploit AI to achieve intelligent 5G networks, and demonstrate the effectiveness of AI to manage and orchestrate cellular network resources. We envision that AI-empowered 5G cellular networks will make the acclaimed ICT enabler a reality.
PDF
