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Quantifying the Types of Representations Used in Common Biochemistry Textbooks

ACS Symposium Series 2019年 1337卷 3-20页

作者： Linenberger Cortes, Kimberly House, Chloe Lewis, Rhodelle Krishnan, Shreya Kammerdiener, Kimberly Tamayo, Morgan Bussey, Thomas J. Department of Chemistry and Biochemistry Kennesaw State University 370 Paulding Avenue NW KennesawGA30144 United States Analytics and Data Science Institute Department of Statistics and Analytical Sciences Kennesaw State University 370 Paulding Avenue NW KennesawGA30144 United States Department of Chemistry and Biochemistry University of California San Diego 9500 Gilman Drive San diegoCA92093 United States

Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and revised. Prior studies have looked at the impact of representations on students understanding of biochemistry. Additional work in this area has looked at the types of representations used to teach and assess biochemistry concepts. However, no study to date has looked at the representations available to both instructors and students in biochemistry textbooks. The study presented here analyzed the representations found in the top four purchased one-semester biochemistry textbooks on Amazon in spring 2015. Using the Taxonomy of Biochemistry External Representations (TOBER), representations were coded as symbolic, macroscopic, particulate, microscopic, or schematic. Results show that, regardless of textbook, particulate representations are most commonly used. Additionally, schematic images are typically used in conjunction with at least one additional type of representation particularly when depicting concepts related to metabolism. © 2019 American Chemical Society.

关键词： Biochemistry

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Plantar fasciitis detection based on deep learning architecture 19

Plantar fasciitis detection based on deep learning architect...

引用

3rd International Conference on Medical and Health Informatics, ICMHI 2019

作者： Chien, Ting-Ying Hsieh, Yi-Ting Lee, Hou-Cheng Hsieh, Yun-Jui Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Taichung Veterans General Hospital Taichung City Taiwan

ISBN: (纸本)9781450371995

Background: Plantar fasciitis is one of the most common foot pain problems in adults. The current diagnosis mainly relies on the inquiry of medical history and a physical examination of the body. In the objective laboratory examination, the blood test has not yet provided an effective diagnostic reference. In this study, we combine a deep learning algorithm architecture with thermal imaging to develop a plantar fasciitis medical decision system that predicts whether the patient has the condition. Methods: This study collected patient image-related data, including 360-degree thermal video and RGB images of the affected area (foot), and patient clinical data. In data preprocessing, we first adjust the thermal image data, based on the different detection environments. After data processing, we employed the Convolutional Neural Networks (CNN) deep learning architecture to develop a prediction model. Results: In total, 1,000 frames were used as the training dataset in this study - -300 cases that had the condition and 700 cases that did not. The results showed that the CNN model can effectively predict plantar fasciitis. The inflammatory response is often accompanied by redness and swelling. This study used thermal imaging to detect the temperature of the affected area, which it combined with a deep learning algorithm to successfully detect the inflammatory condition. In the future, this technique can be used to detect other inflammatory reactions such as wound healing and hemorrhoids. © 2019 ACM.

关键词： big data

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Digital quantum simulation of molecular dynamics and control

arXiv

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arXiv 2020年

作者： Magann, Alicia B. Grace, Matthew D. Rabitz, Herschel A. Sarovar, Mohan Department of Chemical & Biological Engineering Princeton University PrincetonNJ08544 United States Extreme-Scale Data Science & Analytics Sandia National Laboratories LivermoreCA94550 United States Department of Chemistry Princeton University PrincetonNJ08544 United States

Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems, and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials applications. Currently, advances in this area are hindered by the prohibitive cost of the quantum dynamics simulations needed to explore the principles and possibilities of molecular control. However, the emergence of nascent quantum-computing devices suggests that efficient simulations of quantum dynamics may be on the horizon. In this article, we study how quantum computers could be employed to design optimally-shaped fields to control molecular systems. We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in polynomial time, in combination with a classical optimization approach for updating the field. Qubit encoding methods relevant for molecular control problems are described, and procedures for simulating the quantum dynamics and obtaining the simulation results are discussed. Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems, and also consider how optimally-shaped fields could be used to elucidate the mechanisms of energy transfer in light-harvesting complexes. Resource estimates, as well as a numerical assessment of the impact of hardware noise and the prospects of near-term hardware implementations, are provided for the latter task. Copyright © 2020, The Authors. All rights reserved.

关键词： Molecular dynamics

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Application of an Improved Watershed Algorithm Based on Distance Map Reconstruction in Bean Image Segmentation

SSRN

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SSRN 2022年

作者： Liu, Hongquan Zhang, Weijin Wang, Fushun Sun, Xiaohua Wang, Junhao Wang, Chen Wang, Xinxin College of Urban and Rural Construction Hebei Agricultural University Baoding071000 China College of Information Science and Technology Hebei Agricultural University Baoding071000 China Hebei Key Laboratory of Agricultural Big Data Baoding071000 China Department of Digital Media Hebei Software Institute Baoding071000 China Agricultural Technology Innovation Center in Mountainous Areas of Hebei Province Baoding071000 China Agricultural Engineering Technology Research Center of National North Mountainous Area Baoding071000 China Hebei Agricultural University Hebei Mountain Research Institute Baoding071000 China

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residual rat

关键词： Image segmentation

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data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

TechRxiv

引用

TechRxiv 2021年

作者： Zhang, Zhao Wei, Yanyan Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Shenzhen China Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Hefei University of Technology China

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research. © 2021, CC BY.

关键词： Object detection

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Application of an Improved Watershed Algorithm Based on Distance Map Reconstruction in Bean Image Segmentation

SSRN

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SSRN 2022年

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven gray level of the original image, traditional watershed algorithms will generate many incorrect edges, resulting in over-segmentation and under-segmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the gray distribution characteristics of the image, the gray reduction amplitude h was selected to generate the mask image with the same gray distribution trend as that of the original image. The original grayscale map was reconstructed with corresponding thresholds selected according to the false minimums of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residua

关键词： Image segmentation

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A codebook-driven approach for low-light image enhancement

引用

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Xu Wu Xianxu Hou Zhihui Lai Jie Zhou Ya-nan Zhang Witold Pedrycz Linlin Shen College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou 215400 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China School of Computer Science Sichuan Normal University Chengdu 610065 China The Department of Electrical & Computer Engineering University of Alberta Alberta T6G 1H9 Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging discrete codebook priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. The code can be obtained from https://***/csxuwu/CodeEnhance or https://***/*** .

关键词： Image coding

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Towards artificial general intelligence via a multimodal foundation model

arXiv

引用

arXiv 2021年

作者： Fei, Nanyi Lu, Zhiwu Gao, Yizhao Yang, Guoxing Huo, Yuqi Wen, Jingyuan Lu, Haoyu Song, Ruihua Gao, Xin Xiang, Tao Sun, Hao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Information Renmin University of China Beijing China Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Electrical and Electronic Engineering University of Surrey Guildford United Kingdom

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI". © 2021, CC BY.

关键词： Semantics

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Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

arXiv

引用

arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

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Neural entropic estimation: A faster path to mutual information estimation

arXiv

引用

arXiv 2019年

作者： Chan, Chung Al-Bashabsheh, Ali Huang, Hing Pang Lim, Michael Tam, Da Sun Handason Zhao, Chao Department of Computer Science City University of Hong Kong Big Data and Brain Computing Beihang University

We point out a limitation of the mutual information neural estimation (MINE) where the network fails to learn at the initial training phase, leading to slow convergence in the number of training iterations. To solve this problem, we propose a faster method called the mutual information neural entropic estimation (MI-NEE). Our solution first generalizes MINE to estimate the entropy using a custom reference distribution. The entropy estimate can then be used to estimate the mutual information. We argue that the seemingly redundant intermediate step of entropy estimation allows one to improve the convergence by an appropriate reference distribution. In particular, we show that MI-NEE reduces to MINE in the special case when the reference distribution is the product of marginal distributions, but faster convergence is possible by choosing the uniform distribution as the reference distribution instead. Compared to the product of marginals, the uniform distribution introduces more samples in low-density regions and fewer samples in high-density regions, which appear to lead to an overall larger gradient for faster convergence. Copyright © 2019, The Authors. All rights reserved.

关键词： Entropy

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