Monk, official and gentry:: multiple writings of jingshan annals and the regional sight of the late ming buddhist revival

When discussing the revival of Buddhism in the late Ming Dynasty, scholars lack the study of rich local records, specific regions and typical cases. Jingshan temple in Hangzhou provides such a sample. An outstanding manifestation of Jingshan Temple in the late Ming Dynasty is the emergency of a whole bunch of annals. Different groups such as monks, magistrates, and gentry all participated in the writing of the history of Jingshan diachronically in the same space. Different versions of Jingshan Annals reveal the interweaving of historical events, trends of the times, and the wishes of various groups in distinct regions, shedding light on the development that Buddhist historical records began to involve other classics outside Buddhism instead of only focusing on Buddhist sutras. This process manifests that the revival of Buddhism in the late Ming Dynasty is also a diachronic active state achieved by the people’s activities with different purposes in a specific area.When discussing the revival of Buddhism in the late Ming Dynasty, scholars lack the study of rich local records, specific regions and typical cases. Jingshan temple in Hangzhou provides such a sample. An outstanding manifestation of Jingshan Temple in the late Ming Dynasty is the emergency of a whole bunch of annals. Different groups such as monks, magistrates, and gentry all participated in the writing of the history of Jingshan diachronically in the same space. Different versions of Jingshan Annals reveal the interweaving of historical events, trends of the times, and the wishes of various groups in distinct regions, shedding light on the development that Buddhist historical records began to involve other classics outside Buddhism instead of only focusing on Buddhist sutras. This process manifests that the revival of Buddhism in the late Ming Dynasty is also a diachronic active state achieved by the people’s activities with different purposes in a specific area

Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes

The conversion of other forms of energy into mechanical work through the geometrical extension and retraction of nanomaterials has a wide variety of potential applications, including for mimicking biomotors. Here, using molecular dynamic simulations, we demonstrate that there exists an intrinsic energy conversion mechanism between thermal energy and mechanical work in the telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can inherently convert heat into mechanical work in its telescopic extension process, while convert mechanical energy into heat in its telescopic retraction process. These two processes are thermodynamically reversible. The underlying mechanism for this reversibility is that the entropy changes with the telescopic overlapping length of concentric individual tubes. We find also that the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a result, the spontaneously telescopic motion of a condensed DWCNT can be switched to extrusion by rising the system temperature above a critical value. These findings are important for fundamentally understanding the mechanical behavior of concentric nanotubes, and may have general implications in the application of DWCNTs as linear motors in nanodevices

Clothing motivation, online critical thinking, and the behavioural intention of clothing collocation:Mediation analysis on Chinese youth

Recent years have witnessed a boom of fashion blogging sharing information about clothing and cosmetics on diverse social media platforms. Constant exposure to fashion-related digital information heavily impacts the conception and behaviours of Chinese youth. Compared to the substantial studies on the impact of social media, scarce research has been conducted on how youth’s cognitive processing of fashion-related digital information interacts with motivational factors to determine the subsequent behaviours. This study made an initial attempt to address this issue by exploring the successive associations between clothing motivation (amotivation, controlled, and autonomous motivation), online critical thinking (for information credibility, objectivity, and relevance), and the subsequent behavioural intention. A total of 1997 Chinese youth with diverse educational backgrounds voluntarily participated in the study. Results confirmed the direct links between clothing motivation and the behavioural intention, and these links were mediated by different online critical thinking practices. This study provides new insights for both practitioners and scholars in the fields of education, psychology, social media, and marketing.</p

Interfacial thermal conductance in graphene/black phosphorus heterogeneous structures

Graphene, as a passivation layer, can be used to protect the black phosphorus from the chemical reaction with surrounding oxygen and water. However, black phosphorus and graphene heterostructures have low efficiency of heat dissipation due to its intrinsic high thermal resistance at the interfaces. The accumulated energy from Joule heat has to be removed efficiently to avoid the malfunction of the devices. Therefore, it is of significance to investigate the interfacial thermal dissipation properties and manipulate the properties by interfacial engineering on demand. In this work, the interfacial thermal conductance between few-layer black phosphorus and graphene is studied extensively using molecular dynamics simulations. Two critical parameters, the critical power Pcr to maintain thermal stability and the maximum heat power density Pmax with which the system can be loaded, are identified. Our results show that interfacial thermal conductance can be effectively tuned in a wide range with external strains and interracial defects. The compressive strain can enhance the interfacial thermal conductance by one order of magnitude, while interface defects give a two-fold increase. These findings could provide guidelines in heat dissipation and interfacial engineering for thermal conductance manipulation of black phosphorus-graphene heterostructure-based devices.Comment: 33 pages, 22 figure

Energy-efficient systems for information transfer and processing

Machine learning (ML) systems are finding excellent utility in tackling the data deluge of the big data era thanks to the exponential increase in computing power. Current ML systems adopt either centralized cloud computing or distributed edge computing. In both, the challenge of energy efficiency has been drawing increased attention. In cloud computing, data transfer due to inter-chip, inter-board, inter-shelf and inter-rack communications (I/O interface) within data centers is one of the dominant energy costs. This will intensify with the growing demand for increased I/O bandwidth of high-performance computing in data centers. On the other hand, in edge computing, energy efficiency is the primary design challenge, as mobile devices have limited energy, computation and storage resources. This challenge is being exacerbated by the need to embed ML algorithms such as convolutional neural networks (CNNs) for enabling local on-device inference capabilities. In this dissertation, we investigate techniques to address these challenges. To address the energy efficiency challenge in data centers, this dissertation focuses on reducing the energy consumption of the I/O interface. Specifically, in the emerging analog-to-digital converter (ADC)-based multi-Gb/s serial link receivers, the power dissipation is dominated by the ADC. ADCs in serial links employ signal-to-noise-and-distortion-ratio (SNDR) and effective-number-of-bits (ENOB) as performance metrics because these are the standard for generic ADC design. This dissertation presents the use of information-based metrics such as bit-error-rate (BER) to design a BER-optimal ADC (BOA) for serial links. First, theoretical analysis is developed to show when the benefits of BOA over a conventional uniform ADC (CUA) in a serial link receiver are substantial. Second, a \unit[4]{GS/s}, 4-\mbox{\textrm{bit}} on-chip ADC in a \unit[90]{nm} CMOS process is designed and integrated into a 4 Gb/s serial link receiver to verify the aforementioned analysis. Specifically, measured results demonstrate that a 3-\mathrm{bit} BOA receiver outperforms a 4-\mathrm{bit} CUA receiver at a BER <10^{-12} and provides \unit[50]{\%} power savings in the ADC. In the process, it is demonstrated conclusively that BER as opposed to ENOB is a better metric when designing ADCs for serial links. For the problem of resource-constrained computing at the edge, this dissertation tackles the issue of energy-efficient implementation of ML algorithms, particularly CNNs which have recently gained considerable interest due to their record-breaking performance in many recognition tasks. However, their implementation complexity hinders their deployment on power-constrained embedded platforms. This dissertation develops two techniques for energy-efficient CNN design. The first technique is a predictive CNN (PredictiveNet), which makes use of high sparsity in well-trained CNNs to bypass a large fraction of power-dominant convolutions at runtime without modifying the CNN structure. Analysis supported by simulations is provided to justify PredictiveNet's effectiveness. When applied to both the MNIST and CIFAR-10 datasets, simulation results show that PredictiveNet achieves 7.2\times and 4.4\times reduction in the computational and representational costs, respectively, compared with a conventional CNN. It is further shown that PredictiveNet enables computational and representational cost reductions of 2.5\times and 1.7\times, respectively, compared to a state-of-the-art CNN, while incurring only 0.02 classification accuracy loss. The second technique is a variation-tolerant architecture for CNN capable of operating in near threshold voltage (NTV) regime for aggressive energy efficiency. It is well-known that NTV computing can achieve up to 10\times energy savings but is sensitive to process, temperature, and voltage (PVT) variations which can lead to timing errors. To leverage the great potential of NTV for energy efficiency, this dissertation develops a new statistical error compensation (SEC) technique referred to as rank decomposed SEC (RD-SEC). RD-SEC makes use of inherent redundancy in CNNs to handle timing errors due to NTV computing. When evaluated in CNNs for both the MNIST and CIFAR-10 datasets, simulation results in \unit[45]{nm} CMOS show that RD-SEC enables robust CNNs operating in the NTV regime. Specifically, the proposed RD-SEC can achieve up to 11\times improvement in variation tolerance and enable up to 113\times reduction in the standard deviation of classification accuracy while incurring marginal degradation in the median classification accuracy

ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer

Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. But both attention and multi-layer perceptions (MLPs) in ViTs are not efficient enough due to dense multiplications, resulting in costly training and inference. To this end, we propose to reparameterize the pre-trained ViT with a mixture of multiplication primitives, e.g., bitwise shifts and additions, towards a new type of multiplication-reduced model, dubbed $\textbf{ShiftAddViT}$, which aims for end-to-end inference speedups on GPUs without the need of training from scratch. Specifically, all $\texttt{MatMuls}$ among queries, keys, and values are reparameterized by additive kernels, after mapping queries and keys to binary codes in Hamming space. The remaining MLPs or linear layers are then reparameterized by shift kernels. We utilize TVM to implement and optimize those customized kernels for practical hardware deployment on GPUs. We find that such a reparameterization on (quadratic or linear) attention maintains model accuracy, while inevitably leading to accuracy drops when being applied to MLPs. To marry the best of both worlds, we further propose a new mixture of experts (MoE) framework to reparameterize MLPs by taking multiplication or its primitives as experts, e.g., multiplication and shift, and designing a new latency-aware load-balancing loss. Such a loss helps to train a generic router for assigning a dynamic amount of input tokens to different experts according to their latency. In principle, the faster experts run, the larger amount of input tokens are assigned. Extensive experiments consistently validate the effectiveness of our proposed ShiftAddViT, achieving up to \textbf{5.18\times} latency reductions on GPUs and \textbf{42.9%} energy savings, while maintaining comparable accuracy as original or efficient ViTs.Comment: Accepted by NeurIPS 202