Performance of Latent Dirichlet Allocation with Different Topic and Document Structures

Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of documents. One popular method is the Latent Dirichlet Allocation (LDA). LDA assumes a Bayesian generative model with multinomial distributions of topics and vocabularies within the topics. The LDA model result (i.e., the number and types of topics in the corpus) depends on tuning parameters. Several methods, ad hoc or heuristic, have been proposed and analyzed for selecting these parameters. But all these methods have been developed using one or more real corpora. Unfortunately, with real corpora, the true number and types of topics are unknown and it is difficult to assess how well the data follow the assumptions of LDA. To address this issue, we developed a factorial simulation design to create corpora with known structure that varied on the following four factors: 1) number of topics, 2) proportions of topics in documents, 3) size of the vocabulary in topics, and 4) proportion of vocabulary that is contained in documents. Results suggest that the quality of LDA fitting depends on the document-topic distribution and the fitting performs the best when the document lengths are at least four times the vocabulary size. We have also proposed a pre-processing method that may be used to increase quality of the LDA result in some of the worst-case scenarios from the factorial simulation study

Parameterization-based Neural Network: Predicting Non-linear Stress-Strain Response of Composites

Composite materials like syntactic foams have complex internal microstructures that manifest high-stress concentrations due to material discontinuities occurring from hollow regions and thin walls of hollow particles or microballoons embedded in a continuous medium. Predicting the mechanical response as non-linear stress-strain curves of such heterogeneous materials from their microstructure is a challenging problem. This is true since various parameters, including the distribution and geometric properties of microballoons, dictate their response to mechanical loading. To that end, this paper presents a novel Neural Network (NN) framework called Parameterization-based Neural Network (PBNN), where we relate the composite microstructure to the non-linear response through this trained NN model. PBNN represents the stress-strain curve as a parameterized function to reduce the prediction size and predicts the function parameters for different syntactic foam microstructures. We show that our approach can predict more accurate non-linear stress-strain responses and corresponding parameterized functions using smaller datasets than existing approaches. This is enabled by extracting high-level features from the geometry data and tuning the predicted response through an auxiliary term prediction. Although built in the context of the compressive response prediction of syntactic foam composites, our NN framework applies to predict generic non-linear responses for heterogeneous materials with internal microstructures. Hence, our novel PBNN is anticipated to inspire more parameterization-related studies in different Machine Learning methods

Intellectual Property Pledge Value Evaluation for Listed Companies: a Case Study of Yunnan Baiyao Enterprise

In modern enterprise assets, the proportion of intangible assets is not only an important standard to measure the competitiveness of an enterprise, but also an important way to obtain financing. Bank loans can be obtained through the pledge of intellectual property rights to solve the capital problems encountered in the development process of enterprises. However, the difficulty of intellectual property pledge is value evaluation, and intellectual property belongs to intangible assets, and its uncertain characteristics easily lead to more problems in the process of value evaluation. In addition, the traditional value evaluation methods have certain limitations. This paper selects Yunnan Baiyao enterprises as an example to study the value evaluation of intellectual property pledge of listed companies.In this paper, domestic and foreign scholars on the status of intellectual property pledge research, analysis of the focus of domestic and foreign research, at the same time, the status quo of value evaluation is analyzed, and combined with three basic methods of intellectual property value evaluation are analyzed, and taking Yunnan Baiyao enterprise as an example, the application of intellectual property pledge value evaluation method is illustrated, and relevant enlightenment and suggestions are obtained. Finally, this paper summarizes the full text and prospects the evaluation methods of intellectual property pledge value of Listed Companies in China. The text studies the advanced experience of municipal solid waste treat

Role of Material Directionality on the Mechanical Response of Miura-Ori Composite Structures

This paper aims to understand the role of directional material properties on the mechanical responses of origami structures. We consider the Miura-Ori structures our target model due to their collapsibility and negative Poisson's ratio (NPR) effects, which are widely used in shock absorbers, disaster shelters, aerospace applications, etc. Traditional Miura-Ori structures are made of isotropic materials (Aluminum, Acrylic), whose mechanical properties like stiffness and NPR are well understood. However, how these responses are affected by directional materials, like Carbon Fiber Reinforced Polymer (CFRP) composites, lacks in-depth understanding. To that end, we study how fiber directions and arrangements in CFRP composites and Miura-Ori's geometric parameters control the stiffness and NPR of such structures. Through finite element analysis, we show that Miura-Ori structures made of CFRP composites can achieve higher stiffness and Poisson's ratio values than those made of an isotropic material like Aluminum. Then through regression analysis, we establish the relationship between different geometric parameters and the corresponding mechanical responses, which is further utilized to discover the Miura-Ori structure's optimal shape. We also show that the shear modulus is a dominant parameter that controls the mechanical responses mentioned above among the individual composite material properties within the Miura-Ori structure. We demonstrate that we can optimize the Miura-Ori structure by finding geometric and material parameters that result in combined stiffest and most compressible structures. We anticipate our research to be a starting point for designing and optimizing more sophisticated origami structures with composite materials incorporated

Physics-Constrained Neural Network for the Analysis and Feature-Based Optimization of Woven Composites

Woven composites are produced by interlacing warp and weft fibers in a pattern or weave style. By changing the pattern or material, the mechanical properties of woven composites can be significantly changed; however, the role of woven composite architecture (pattern, material) on the mechanical properties is not well understood. In this paper, we explore the relationship between woven composite architectures (weave pattern, weave material sequence) and the corresponding modulus through our proposed Physics-Constrained Neural Network (PCNN). Furthermore, we apply statistical learning methods to optimize the woven composite architecture to improve mechanical responses. Our results show that PCNN can effectively predict woven architecture for the desired modulus with much higher accuracy than several baseline models. PCNN can be further combined with feature-based optimization to determine the optimal woven composite architecture at the initial design stage. In addition to relating woven composite architecture to its mechanical responses, our research also provides an in-depth understanding of how architectural features govern mechanical responses. We anticipate our proposed frameworks will primarily facilitate the woven composite analysis and optimization process and be a starting point to introduce Physics knowledge-guided Neural Networks into the complex structural analysis

Experimental Investigation on Failure Modes and Mechanical Properties of Rock-Like Specimens with a Grout-Infilled Flaw under Triaxial Compression

Flaws existing in rock mass are one of the main factors resulting in the instability of rock mass. Epoxy resin is often used to reinforce fractured rock mass. However, few researches focused on mechanical properties of the specimens with a resin-infilled flaw under triaxial compression. Therefore, in this research, epoxy resin was selected as the grouting material, and triaxial compression tests were conducted on the rock-like specimens with a grout-infilled flaw having different geometries. This study draws some new conclusions. The high confining pressure suppresses the generation of tensile cracks, and the failure mode changes from tensile-shear failure to shear failure as the confining pressure increases. Grouting with epoxy resin leads to the improvement of peak strengths of the specimens under triaxial compression. The reinforcement effect of epoxy resin is better for the specimens having a large flaw length and those under a relatively low confining pressure. Grouting with epoxy resin reduces the internal friction angle of the samples but improves their cohesion. This research may provide some useful insights for understanding the mechanical behaviors of grouted rock masses.National Natural Science Foundation of China [41672258, 41102162]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_0622]; Fundamental Research Funds for the Central Universities [2018B695X14]; Chinese Scholarship CouncilOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Quantum Circuits of AES with a Low-depth Linear Layer and a New Structure

In recent years quantum computing has developed rapidly. The security threat posed by quantum computing to cryptography makes it necessary to better evaluate the resource cost of attacking algorithms, some of which require quantum implementations of the attacked cryptographic building blocks. In this paper we manage to optimize quantum circuits of AES in several aspects. Firstly, based on de Brugière \textit{et al.}\u27s greedy algorithm, we propose an improved depth-oriented algorithm for synthesizing low-depth CNOT circuits with no ancilla qubits. Our algorithm finds a CNOT circuit of AES MixColumns with depth 10, which breaks a recent record of depth 16. In addition, our algorithm gives low-depth CNOT circuits for many MDS matrices and matrices used in block ciphers studied in related work. Secondly, we present a new structure named compressed pipeline structure to synthesize quantum circuits of AES, which can be used for constructing quantum oracles employed in quantum attacks based on Grover and Simon\u27s algorithms. When the number of ancilla qubits required by the round function and its inverse is not very large, our structure will have a better trade-off of $D$-$W$ cost. We then give detailed quantum circuits of AES-128 under the guidance of our structure and make some comparisons with other circuits. Finally, our encryption circuit and key schedule circuit have their own application scenarios. The Encryption oracle used in Simon\u27s algorithm built with the former will have smaller depth. For example, we can construct an AES-128 Encryption oracle with $T$-depth 33, while the previous best result is 60. A small variant of the latter, along with our method to make an Sbox input-invariant, can avoid the allocation of extra ancilla qubits for storing key words in the shallowed pipeline structure. Based on this, we achieve a quantum circuit of AES-128 with the lowest $TofD$-$W$ cost 130720 to date