“詩言志”之本義謭論 : 讀朱自清先生《詩言志辨》札記

朱自清先生《詩言志辨》，認為“詩言志”之基本内涵是言説思想意志用以教化，强調其思想旨趣在於政教。這個學術影響巨大的認知，其實是對漢代詩學思想的整理和提煉,與“詩言志”之本義不完全相符。本文從先秦“詩言志”之實踐和闡説、詩人自道“詩言志”之内涵以及“詩言志”訓詁三個方面，重新梳理“詩言志”之本義，以為其不僅包含“表達思想意志”之義，也包含“抒發情感”和“紀事論理”之義。 In Shiyanzhibian, Zhu Ziqing believes that the essence of shiyanzhi is that the purpose of expressing thoughts and ideas is to emphasize the significance of official propaganda. This conviction, which has major academic influence, is actually an induction of the overall poetic thoughts of Han Dynasty, rather than aiming at the essence of shiyanzhi. This essay holds this argument from three aspects: the practice and interpretation of shiyanzhi of pre-Qin period, the illustrations of shiyanzhi of poets themselves, and exegetical studies (xungu) of shiyanzhi. In doing so, this essay reevaluates the very essence of shiyanzhi, and proposes that shiyanzhi contains not only the meaning of illustrating ideological thoughts, but also includes expressing emotions and the ethics of narratives

Power Loss and Thermal Analysis of a MW High-Speed Permanent Magnet Synchronous Machine

High speed permanent magnet synchronous machines (PMSMs) have attracted much attention due to their high power density, high efficiency, and compact size for direct-drive applications. However, the consequent power loss density is high and heat transfer is also deemed as a technical challenge. This is particularly the case for high-speed operation. In this paper, electromagnetic and mechanical power losses in a MW level high speed PMSM is comprehensively investigated by finite element analysis; the transient machine demagnetization performance is studied with the proposed rotor composite structure to improve the machine anti-demagnetization capability; the temperature distribution of the high speed PMSM is also analyzed by a fluid-thermal coupling method with calculated power loss. The high speed PMSM is prototyped and experimentally tested to validate the effectiveness of numerical models and calculated results

E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation

Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empathetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions from different resolutions, further modeling emotion correlation. Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation. Experimental results on the benchmark dataset demonstrate the superiority of our model in both empathetic perception and expression.Comment: 19 pages, 6 figure

RHINO: Regularizing the Hash-based Implicit Neural Representation

The use of Implicit Neural Representation (INR) through a hash-table has demonstrated impressive effectiveness and efficiency in characterizing intricate signals. However, current state-of-the-art methods exhibit insufficient regularization, often yielding unreliable and noisy results during interpolations. We find that this issue stems from broken gradient flow between input coordinates and indexed hash-keys, where the chain rule attempts to model discrete hash-keys, rather than the continuous coordinates. To tackle this concern, we introduce RHINO, in which a continuous analytical function is incorporated to facilitate regularization by connecting the input coordinate and the network additionally without modifying the architecture of current hash-based INRs. This connection ensures a seamless backpropagation of gradients from the network's output back to the input coordinates, thereby enhancing regularization. Our experimental results not only showcase the broadened regularization capability across different hash-based INRs like DINER and Instant NGP, but also across a variety of tasks such as image fitting, representation of signed distance functions, and optimization of 5D static / 6D dynamic neural radiance fields. Notably, RHINO outperforms current state-of-the-art techniques in both quality and speed, affirming its superiority.Comment: 17 pages, 11 figure

Vapor phase infiltration for transforming polymers into organic-inorganic hybrid materials: Processing science, structural complexity, and emerging applications

Vapor phase infiltration (VPI) exposes polymers to gaseous metalorganic molecules that sorb, diffuse, and become entrapped in the bulk polymer, transforming it into a complex organic-inorganic hybrid material.1 This process is pictured in Figure 1. While VPI’s gaseous dosing sequences may appear similar to other vapor deposition techniques (e.g., atomic layer deposition) the set of atomic scale processes occurring during synthesis constitute a fundamentally different process that results in not just a simple coating on the polymer but rather a complete alteration of the polymer’s bulk chemistry. Please click Additional Files below to see the full abstract

Differential microRNA expression between shoots and rhizomes in Oryza longistaminata using high-throughput RNA sequencing

AbstractPlant microRNAs (miRNAs) play important roles in biological processes such as development and stress responses. Although the diverse functions of miRNAs in model organisms have been well studied, their function in wild rice is poorly understood. In this study, high-throughput small RNA sequencing was performed to characterize tissue-specific transcriptomes in Oryza longistaminata. A total of 603 miRNAs, 380 known rice miRNAs, 72 conserved plant miRNAs, and 151 predicted novel miRNAs were identified as being expressed in aerial shoots and rhizomes. Additionally, 99 and 79 miRNAs were expressed exclusively or differentially, respectively, in the two tissues, and 144 potential targets were predicted for the differentially expressed miRNAs in the rhizomes. Functional annotation of these targets suggested that transcription factors, including squamosa promoter binding proteins and auxin response factors, function in rhizome growth and development. The expression levels of several miRNAs and target genes in the rhizomes were quantified by RT-PCR, and the results indicated the existence of complex regulatory mechanisms between the miRNAs and their targets. Eight target cleavage sites were verified by RNA ligase-mediated rapid 5′ end amplification. These results provide valuable information on the composition, expression and function of miRNAs in O. longistaminata, and will aid in understanding the molecular mechanisms of rhizome development