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arXiv 2022年

作者： Infanger, Alex Glynn, Peter W. Institute for Computational & Mathematical Engineering Stanford University United States Department of Management Science & Engineering Stanford University United States

In the analysis of Markov chains and processes, it is sometimes convenient to replace an unbounded state space with a "truncated" bounded state space. When such a replacement is made, one often wants to know whether the equilibrium behavior of the truncated chain or process is close to that of the untruncated system. For example, such questions arise naturally when considering numerical methods for computing stationary distributions on unbounded state space. In this paper, we use the principle of "regeneration" to show that the stationary distributions of "fixed state" truncations converge in great generality (in total variation norm) to the stationary distribution of the untruncated limit, when the untruncated chain is positive Harris recurrent. Even in countable state space, our theory extends known results by showing that the augmentation can correspond to an r-regular measure. In addition, we extend our theory to cover an important subclass of Harris recurrent Markov processes that include non-explosive Markov jump processes on countable state space. © 2022, CC BY.

关键词： Markov processes

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QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 998-1011页

作者： Fatima Zohra Smaili Shuye Tian Ambrish Roy Meshari Alazmi Stefan T.Arold Srayanta Mukherjee P.Scott Hefty Wei Chen Xin Gao Computational Bioscience Research Center(CBRC) ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Biology Southern University of Science and Technology of China(SUSTC)Shenzhen 518055China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA College of Computer Science and Engineering University of Ha’ilHa'il 55476Saudi Arabia Biological and Environmental Sciences and Engineering(BESE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Molecular Bioscience University of KansasLawrenceKS 66047USA

The number of available protein sequences in public databases is increasing ***,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems ***,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)*** uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence *** three pieces of information are combined by consensus averaging to make the final *** approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark *** results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or *** further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

关键词： Protein function prediction GO term EC number Protein structure similarity Functionally discriminative motif

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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Revolutionizing Skin Cancer Detection with Raspberry Pi-Embedded ANN Technology in an Automated Screening Booth

Revolutionizing Skin Cancer Detection with Raspberry Pi-Embe...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Srinivasan G. Indra T R Saravanan S. Murugan C. Srinivasan M. Muthulekshmi Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this ground-breaking endeavor, we describe an automated skin cancer screening booth that can transform current approaches to early detection. The merging of Raspberry Pi-controlled technology with cutting-edge Artificial Neural Network (ANN) models forms the basis of our groundbreaking breakthrough. The booth uses high-resolution imaging sensors to acquire detailed photographs of the user's skin, which are then analyzed in real-time by an ANN. The ability to identify potentially malignant anomalies in a timely and precise manner is made possible by this combination. In public places, user contact is facilitated by the use of an intuitive touchscreen interface, which ensures both accessibility and convenience of use. The ANN model, trained on a wide variety of data, is very good at differentiating between normal and malignant skin diseases, with the findings being instantly shown on the interface. Beyond its technological capabilities, our technology bears the prospect of broad application, which will turn screening for skin cancer into a preventative healthcare tool available to everyone. This all-encompassing strategy highlights the significance of early detection and argues for a solution that is both scalable and effective at the crossroads of technology and public health.

关键词： Technological innovation Accuracy Artificial neural networks Transforms User interfaces Touch sensitive screens Skin

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Solutions of Poisson's Equation for Stochastically Monotone Markov Chains

arXiv

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arXiv 2022年

作者： Glynn, Peter W. Infanger, Alex Department of Management Science & Engineering Stanford University United States Institute for Computational & Mathematical Engineering Stanford University United States

Stochastically monotone Markov chains arise in many applied domains, especially in the setting of queues and storage systems. Poisson's equation is a key tool for analyzing additive functionals of such models, such as cumulative sums of waiting times or sums of rewards. In this paper, we show that when the reward function for such a Markov chain is monotone, the solution of Poisson's equation is monotone. This implies that the value function associated with infinite horizon average reward is monotone in the state when the reward is monotone. © 2022, CC BY.

关键词： Poisson equation

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An Augmented Reality Measurement Tool for the da Vinci Research Kit

An Augmented Reality Measurement Tool for the da Vinci Resea...

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International Symposium on Medical Robotics (ISMR)

作者： Shiyue Wang Jing-Fen Wang Yubin Koh Haoying Zhou Anton Deguet Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Purdue University West Lafayette IN USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

We present an augmented reality surgical robotics console for the da Vinci Research Kit (dVRK) that integrates a real-time distance measurement tool to enhance spatial awareness during robotic-assisted surgical procedures through an interactive system. The system integrates an RViz-based interface with ROS topics, enabling virtual cursor visualization and interactive controls for precise measurements on tissue or phantom models. It supports two distinct measurement approaches: (1) a virtual measurement capability, where the operator uses the two Master Tool Manipulators (MTMs) to drive virtual 3D measurement cursors in the stereo display (a “masters as mice” mode), and (2) a physical measurement tool, where the user teleoperates one or more Patient Side Manipulators (PSMs) to touch two features and report the intervening distance. We conduct a user study on virtual (MTM) and teleoperation (PSM) modes to assess performance and accuracy and gather user feedback. The results show a Root Mean Square Error (RMSE) of 2.19 mm to 7.25 mm across different modes and tasks. This work underscores the importance of such systems for enhancing accuracy and usability in endoscopic surgery, where spatial awareness and intraoperative assessment are critical. Code is available at https://***/capricieuxV/RVinci_***.

关键词： Visualization Three-dimensional displays Medical robotics Accuracy Surgery Manipulators Real-time systems Usability Root mean square Augmented reality

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Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements

arXiv

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arXiv 2022年

作者： Jalota, Devansh Ye, Yinyu Institute for Computational and Mathematical Engineering Stanford University United States Department of Management Science and Engineering Stanford University United States

Fisher markets are one of the most fundamental models for resource allocation. However, the problem of computing equilibrium prices in Fisher markets typically relies on complete knowledge of users’ budgets and utility functions and requires transactions to happen in a static market where all users are present simultaneously. Motivated by these practical considerations, we study an online variant of Fisher markets, wherein users with privately known utility and budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this setting, we first study the limitations of static pricing algorithms, which set uniform prices for all users, along two performance metrics: (i) regret, i.e., the optimality gap in the objective of the Eisenberg-Gale program between an online algorithm and an oracle with complete information, and (ii) capacity violations, i.e., the over-consumption of goods relative to their capacities. Given the limitations of static pricing, we design adaptive posted-pricing algorithms, one with knowledge of the distribution of users’ budget and utility parameters and another that adjusts prices solely based on past observations of user consumption, i.e., revealed preference feedback, with improved performance guarantees. Finally, we present numerical experiments to compare our revealed preference algorithm’s performance to several benchmarks. © 2022, CC BY.

关键词： Stochastic systems

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Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems

arXiv

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arXiv 2024年

作者： Anandh, Thivin Ghose, Divij Jain, Himanshu Sunkad, Pratham Ganesan, Sashikumaar John, Volker Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore India Department of Mechanical Engineering Indian Institute of Technology Tamil Nadu Madras India Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Department of Mathematics and Computer Science Freie University Berlin Germany

This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG stabilization is included in the loss functional and a network architecture is proposed that predicts spatially varying stabilization parameters. Having observed that the selection of the indicator function in hard-constrained Dirichlet boundary conditions has a big impact on the accuracy of the computed solutions, the second novelty is the proposal of a network architecture that learns good parameters for a class of indicator functions. Numerical studies show that both proposals lead to noticeably more accurate results than approaches that can be found in the literature. © 2024, CC BY-NC-SA.

关键词： Stabilization

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Advantage Actor-Critic Reinforcement Learning with Technical Indicators for Stock Trading Decisions

SSRN

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SSRN 2023年

作者： Lincy, Rubell Marion G. Sagar, Som Narayanan, Vishnu Binu, Dhanush Selby, Nevin Thomas, Sheba Elizabeth Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India Computational Mathematics Indian Institute of Information Technology Kerala Kottayam India

Investors and traders are always looking for new ways to understand the complex environment so that they may reduce their exposure to risk, make more informed choices, and boost their returns. The integration of AI and ML has emerged as a game-changer in this area of research. Reinforcement learning is one such technology that has the potential to significantly change the way we approach trading in the financial markets. This work aims to explore the use of the Advantage Actor-Critic (A2C) reinforcement learning model in the context of deciding buy and sell choices for stocks. The training of the model incorporates diverse types of stock market data, including a reward function that evaluates both profits and losses. Furthermore, technical indicators such as 50-day and 200-day moving averages, as well as volume averages, are utilised as input features. The purpose of our research is to determine whether the A2C model can effectively learn to make profitable stock trading decisions in a simulated environment. The results demonstrated that the A2C model adapted to the fluctuating market conditions and consistently generated decisions that resulted in significant financial gain. © 2023, The Authors. All rights reserved.

关键词： Decision making

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