Semantic Role Labeling Guided Out-of-distribution Detection

Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications. Previous works identify the OOD instance by leveraging a single global feature embedding to represent the sentence, which cannot characterize subtle OOD patterns well. Another major challenge current OOD methods face is learning effective low-dimensional sentence representations to identify the hard OOD instances that are semantically similar to the ID data. In this paper, we propose a new unsupervised OOD detection method, namely Semantic Role Labeling Guided Out-of-distribution Detection (SRLOOD), that separates, extracts, and learns the semantic role labeling (SRL) guided fine-grained local feature representations from different arguments of a sentence and the global feature representations of the full sentence using a margin-based contrastive loss. A novel self-supervised approach is also introduced to enhance such global-local feature learning by predicting the SRL extracted role. The resulting model achieves SOTA performance on four OOD benchmarks, indicating the effectiveness of our approach. Codes will be available upon acceptance

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

A novel application of a polarization microscope for the study of microbiological specimens

Aspergillus niger was used to demonstrate a novel application of a polarization microscope for the study of microbiological specimens. The results showed that conidiophores of A. niger contain optically anisotropic material but hyphae and hyphal branches are optically isotropic. This method could become a useful tool in identification

Spinning the supercooled PET to obtain highly oriented and crystallized PET fibers at low speeds

An attempt has been made to investigate the feasibility for a novel concept of supercooled spinning to obtain orientation-induced crystallization at speeds lower than that for the high-speed spinning technology. This is achieved by setting the nozzle temperature lower than the melting point for PET, making the polymer a supercooled fluidic liquid, and then spinning the supercooled. The experimental results show that high orientation and high crystallinity can be achieved at a spinning speed of 2500 m/min, which is in good comparison with a speed of 5000-6000 m/min to obtain similar degree of orientation and crystallinity in the high-speed spinning. The properties of the as-spun fibers obtained by supercooled spinning were analyzed, and a rational theoretical account for the supercooled spinning is explored

Numerical simulation of DSC and TMDSC curves as well as reversing and nonreversing curve separation

The basic physical meaning of temperature modulation for DSC is an arguable research topic, and its interpretation affects the development of thermal analysis and polymer science. This article studies the basic physical meaning of TMDSC by numerical simulation. DSC and TMDSC output curves are computed for a sample with step changes in its specific heat and for a sample with crystallites melting over the temperature range. The TMDSC curves are further analyzed to obtain the reversing and nonreversing components. It is shown that separation of the reversing and nonreversing components from the underlying heat flow cannot be justified. Some common misconceptions regarding TMDSC are discussed as well

Obtaining the critical draw ratio of draw resonance in melt spinning for power law polymer fluids

A direct difference method has been developed for Non-Newtonian power law fluids to solve the simultaneous non-linear partial differential equations of melt spinning, and to determine the critical draw ratio for draw resonance. The results show that for shear thin fluids, the logarithm of the critical draw ratio has a well defined linear relationship with the power index for isothermal and uniform tension melt spinning. When the power index approaches zero, the critical draw ratio points at unity, indicating no melt spinning can be processed stably for such fluids. For shear thick fluids, the critical draw ratio increases in a more rapid way with increasing the power index

Structural studies of the transitional behaviour of protein materials

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