On the equivalence of the Nernst theorem and its consequence

One general consequence of the Nernst theorem is derived, i.e., the various heat capacities of a thermodynamic system under different constraints approach zero as the temperature approaches absolute zero. The temperature dependence of the heat capacity of any thermodynamic system at ultra-low temperatures is revealed through this consequence. Moreover, the general form and the simplest expression of the heat capacities of thermodynamic systems at ultra-low temperatures are deduced. Some significant discussion and results are given. One new research method is provided by using this consequence. Finally, the equivalence between the Nernst theorem and its consequence is rigorously proved, so that this consequence may be referred to another description of the third law of thermodynamics

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion. In this paper, we propose to decompose the CSC workflow into detection, reasoning, and searching subtasks so that the rich external knowledge about the Chinese language can be leveraged more directly and efficiently. Specifically, we design a plug-and-play detection-and-reasoning module that is compatible with existing SOTA non-autoregressive CSC models to further boost their performance. We find that the detection-and-reasoning module trained for one model can also benefit other models. We also study the primary interpretability provided by the task decomposition. Extensive experiments and detailed analyses demonstrate the effectiveness and competitiveness of the proposed module.Comment: Accepted for publication in Findings of EMNLP 202

Optimal design of sand blown wind tunnel

This work investigates the airflow driven by dual axial-flow fans in an atmospheric boundary layer (ABL) wind tunnel and the expected entrainment of sand movement together. The present study is conducted via 3D numerical simulation based on modelling the entire wind tunnel, including the power fan sections. Three configurations of dual fans in the tunnel are proposed. Simulation results show that the airflow in the tunnel with dual-fan configuration can satisfy the logarithmic distribution law for ABL flows. The airflow driven by the dual fans placed together at the tunnel outlet is highly similar to that in the tunnel with single fans. Although the boundary layer thickness is reduced, the maximum airflow velocity (53.393 m/s) and turbulence intensity (12.02%), which are respectively 1.75 and 1.49 times higher than those under the single-fan configuration, can be reached when dual fans are separately placed at the tunnel inlet and outlet. The simulation and experiment manifest that the separated arrangement of dual fans in the tunnel should be suitable for the experimental study of aeolian sand transport. Some measures, such as wind tunnel construction adjustment and optimal roughness element arrangement, are necessary to guarantee the required boundary layer thickness in the wind tunnel

4,5-Bis(4-meth­oxy­phen­oxy)phthalonitrile

The title compound, C22H16N2O4, was obtained unintentionally as the product of an attempted synthesis of a new phthalocyanine. The dihedral angles formed by the central benzene ring with the aromatic rings of the meth­oxy­phen­oxy groups are 85.39 (5) and 64.19 (5)°

Retrieval-Augmented Meta Learning for Low-Resource Text Classification

Meta learning have achieved promising performance in low-resource text classification which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed. To deal with this issue, we propose a meta-learning based method called Retrieval-Augmented Meta Learning(RAML). It not only uses parameterization for inference but also retrieves non-parametric knowledge from an external corpus to make inferences, which greatly alleviates the problem of poor generalization performance caused by the lack of diverse training data in meta-learning. This method differs from previous models that solely rely on parameters, as it explicitly emphasizes the importance of non-parametric knowledge, aiming to strike a balance between parameterized neural networks and non-parametric knowledge. The model is required to determine which knowledge to access and utilize during inference. Additionally, our multi-view passages fusion network module can effectively and efficiently integrate the retrieved information into low-resource classification task. The extensive experiments demonstrate that RAML significantly outperforms current SOTA low-resource text classification models.Comment: Under Revie

Contextual Similarity is More Valuable than Character Similarity: Curriculum Learning for Chinese Spell Checking

Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. In recent years, related researches focus on introducing the character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual similarity, we propose a simple yet effective curriculum learning framework for the CSC task. With the help of our designed model-agnostic framework, existing CSC models will be trained from easy to difficult as humans learn Chinese characters and achieve further performance improvements. Extensive experiments and detailed analyses on widely used SIGHAN datasets show that our method outperforms previous state-of-the-art methods

Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection

Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-stage learning framework, which sequentially learns the knowledge of different intents in various periods via prompt learning. And then we exploit prototypes for categorizing both seen and novel intents. Furthermore, to achieve the transfer knowledge of intents in different stages, for different scenarios we design two knowledge preservation methods which close to realistic applications. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance.Comment: Under Revie

Integrated analysis of the structure and function of bacterial community in water and shrimp intestine microbes reveals their interaction

Shrimp is closely associated with different microbial populations of the gut and the environment, particularly of the water. Despite significant microbiome research in shrimp, a direct relationship between the shrimp's gut microbiota and the habitat environment remains unclear. The bacterial profiles of the shrimp intestine and its aqueous environment were compared by compiling data from earlier research to characterize the dynamic interaction between shrimp and habitat. According to the integrated analysis, shrimp, water, and sediment all had significant operational taxonomic units (OTUs), with shrimp intestine having less OTUs and sediment having more. Furthermore, 66 biological activities were shown to be common in shrimp and water bacteria, including nitrate reduction, methylotrophy, methanol oxidation, intracellular parasites, human infectious diarrhoea, fermentation, and others. These mechanisms might represent the primary bacterial processes related with intestine function, revealing new information on shrimp and water ecology. Although the relative abundances in the bacterial composition were different in shrimp intestine, water and sediment, the bacterial communities were almost similar, indicating the close interaction between host and the environment in microbiome. Notably, the significant distribution of disease-related pathogens including Vibrio and Flavobacterium in shrimp intestine and habitat water provided valuable information for disease prediction and shrimp health management in the aquaculture industry. In summary, many common microbes and bacterial processes that occur in the shrimp intestine and surrounding environment were revealed, and further functional analysis might help to modulate these processes to promote shrimp development and health