Empirical prediction of traffic noise transmission loss across plenum windows

A parametric study on the traffic noise transmission loss across plenum windows was carried out experimentally in this investigation in an attempt to establish a simple empirical model for predicting this transmission loss. A total of fourteen full scale plenum windows were included in this study. The results of a site mockup measurement were used for model validation. The present model was developed based on the existing plenum chamber theory in which the sound fields inside the plenum window cavities were assumed to make up of a diffracted wave and a reverberant field. Results suggest that both the diffracted and reverberant field inside the plenum window cavities are weaker than those assumed in existing plenum chamber theory. It is found that a model, which assumes frequency-independent diffraction directivity and percentage reverberant field attenuation, gives the best prediction of traffic noise transmission loss. This prediction model is also able to predict site test results with good accurac

Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.Comment: 6 page

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure

Chunking with Max-Margin Markov Networks

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Lifting the Abstraction Level of Compiler Transformations

Production compilers implement optimizing transformation rules for built-in types. What justifies applying these optimizing rules is the axioms that hold for built-in types and the built-in operations supported by these types. Similar axioms also hold for user-defined types and the operations defined on them, and therefore justify a set of optimization rules that may apply to user-defined types. Production compilers, however, do not attempt to construct and apply these optimization rules to user-defined types. Built-in types together the axioms that apply to them are instances of more general algebraic structures. So are user-defined types and their associated axioms. We use the technique of generic programming, a programming paradigm to design efficient, reusable software libraries, to identify the commonality of classes of types, whether built-in or user-defined, convey the semantics of the classes of types to compilers, design scalable and effective program analysis for them, and eventually apply optimizing rules to the operations on them. In generic programming, algorithms and data structures are defined in terms of such algebraic structures. The same definitions are reused for many types, both built-in and user-defined. This dissertation applies generic programming to compiler analyses and transformations. Analyses and transformations are specified for general algebraic structures, and they apply to all types, both built-in and primitive types

GPT-4-powered analysis and prediction of selective catalytic reduction experiments through an effective chain-of-thought prompting strategy

This study explores the use of Large Language Models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NOx with NH3 as a case study. We implement the Chain of Thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions