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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Viktor Bengs Eyke Hüllermeier Willem Waegeman Institute of Informatics University of Munich (LMU) Institute of Informatics University of Munich (LMU) and Munich Center for Machine Learning Department of Data Analysis and Mathematical Modeling Ghent University

ISBN: (纸本)9781713871088

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful way.

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A Review of the Effectiveness of Management Information System in Decision Making 1

A Review of the Effectiveness of Management Information Syst...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Zeidan Al-Kaseasbeh, Hazem Mohammad Wahsheh, Firas Rashed Mohamad, Fatma Susilawati Jusoh, Julaily Aida Farea, Mazen Mohammed Archana, V.K. Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information Systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information Systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information Systems, including Executive Information Systems (EIS), Decision Support Systems (DSS), and Transaction Processing Systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information Systems and decision-making effectiveness

关键词： Decision support systems

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No Six-Cell Neighborhood Cellular Automaton Solves the Parity Problem

SSRN

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SSRN 2024年

作者： Nenca, Anna Wolnik, Barbara De Baets, Bernard Institute of Informatics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland Institute of Mathematics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland KERMIT Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 GentB-9000 Belgium

The parity problem is one of the best-known classification problems studied to examine the computational abilities of cellular automata. In this inverse problem, one is looking for a cellular automaton that can classify each initial configuration into one of two classes according to its parity. In the case of deterministic one-dimensional cellular automata, there exists a local rule that effectively solves the parity problem, but it is unknown whether it is the simplest possible rule. Specifically, it is known that a cellular automaton with a nine-cell neighborhood can solve the parity problem, whereas no cellular automaton with a five-cell neighborhood is capable of doing so. These findings have remained unimproved for the past 10 years. In this paper, we present novel tools that allow to narrow down the existing gap. With the help of these tools, we are able to demonstrate that there is no cellular automaton with a six-cell neighborhood capable of solving the parity problem. © 2024, The Authors. All rights reserved.

关键词： Cellular automata

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Molecular and Clinical Aspects of the Action of Cytidine Diphosphocholine on Cognitive Functions

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Neuroscience and Behavioral Physiology 2022年第3期52卷 347-355页

作者： Gromova, O.A. Torshin, I. Yu. Grishina, T.R. Demidov, V.I. Bogacheva, T.E. Institute of Pharmacoinformatics Federal Research Center for Informatics and Control Russian Academy of Sciences Moscow Russian Federation Center for Big Data Storage and Analysis National Center for Digital Economics Lomonosov Moscow State University Moscow Russian Federation Ivanovo State Medical Academy Russian Ministry of Health Ivanovo Russian Federation

Objectives. To systematize publications on drugs based on cytidine diphosphocholine (CDP-choline). Materials and methods. Systematic computer analysis of all currently available publications on CDPcholine (1750 publications in PubMed) using topological analysis theory for big data. Results. CDP-choline is required for acetylcholine biosynthesis, phospholipid metabolism, and DNA methylation. This article sequentially considers the effects of CDP-choline on acetylcholinergic and other types of neurotransmission and the anti-inflammatory and neuroprotective effects of CDP-choline, as well as the influences of this molecule on fat metabolism and gene expression in the context of the postgenomic paradigm (particularly elevated expression of nicotinic and muscarinic acetylcholine receptors). Results from basic and clinical studies of CDP-choline in the treatment of cognitive impairments associated with cerebral ischemia and neurodegeneration are presented. Conclusions. The pharmacological effects of CDP-choline are realized via multiple molecular mechanisms contributing to the nootropic actions of this molecule. © 2022, Springer Nature Switzerland AG.

关键词： citicoline cognitive disorders molecular pharmacology Neipilept

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3D Skull Completion via Two-stage Conditional Diffusion-Based Signed Distance Fields

3D Skull Completion via Two-stage Conditional Diffusion-Base...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Zhenhong Ru, Xudong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Zhang, Chong Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Bioimage Analysis Barcelona Spain School of Engineering University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Department of Computer Science United Kingdom Department of Cardiovascular Sciences Department of Electrical Engineering Leuven Belgium Alan Turing Institute London United Kingdom

ISBN: (纸本)9798350386226

A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by proposing a two-stage diffusion model based on the representation of 3D shapes using signed distance function (SDF). Specifically, we design a diffusion model conditioned on partial shapes, we compress the 3D shape into a compact latent representation using the encoder in the vector quantized variational autoencoder (VQ-VAE) and learn the diffusion model based on this compressed discrete representation. Encoding the latent space with the autoencoder can achieve high-quality 3D cranial shape completion. In order to accurately capture local and fine-grained shape details, the training data is geometrically encoded from a compactly learned code-book. The two-stage diffusion generator with a coarse-to-fine approach possesses precise and expressive structural modeling capabilities to ensure the supplementation of detailed geometric information. Experimental results verified sufficient expressiveness of our model with generating high-fidelity results with fine-grained local details, outperforming the state-of-the-art methods. © 2024 IEEE.

关键词： Signal encoding

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Road Segmentation with U-Net Architecture Using Jetson AGX Xavier For Autonomous Vehicle

Road Segmentation with U-Net Architecture Using Jetson AGX X...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Gunawan Muhammad Fikri Fadillah Esa Prakasa Bambang Sugiarto Teguh Nurhadi Suharsono Rini Nuraini Sukmana Department of Informatics Engineering Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Image and Video Data Analysis Research Group Research Center for Data and Information Sciences BRIN Bandung Indonesia

Autonomous Vehicle is a technology that has been often discussed in the last few years in the category of research and industry. This technology is able to sense the surrounding environment and control the vehicle autonomously without any human intervention. In its implementation, this technology requires a lot of information, especially the road track that will be passed. Because of that, the thing that must be considered is to segment the road first. The aim of this research is to develop a method that can segment the roads to produce a model that can recognize the road track as well. This research uses Convolutional Neural Network (CNN) with U-Net architecture. The datasets have a form of car trips video recordings from the dashboard camera, which are then extracted into a frame. After this process, it is annotated or manual segmentation using Supervisely to be used as a reference for training and testing. From the results of the calculation process with the confusion matrix, the accuracy of the U-net architecture gets a value of 95%, precision value is 81%, recall value is 92%, F1-Score value is 86% IOU value is 76%. Followed by testing the model in real-time using Jetson AGX Xavier, this tool is specially designed to develop artificial intelligence with high specifications. The test is carried out with two types of testing. The first test with an RGB background produces an FPS of 0.17, and the second test without an RGB background gets an FPS in the range of 0.55-0.67.

关键词： Training Roads Cameras Real-time systems Trajectory Telecommunications Convolutional neural networks

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Nonlinear Monte Carlo methods with polynomial runtime for Bellman equations of discrete time high-dimensional stochastic optimal control problems

arXiv

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arXiv 2023年

作者： Beck, Christian Jentzen, Arnulf Kleinberg, Konrad Kruse, Thomas Department of Mathematics ETH Zurich Switzerland Applied Mathematics Institute for Analysis and Numerics University of Münster Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Mathematics & Informatics University of Wuppertal Germany

Discrete time stochastic optimal control problems and Markov decision processes (MDPs), respectively, serve as fundamental models for problems that involve sequential decision making under uncertainty and as such constitute the theoretical foundation of reinforcement learning. In this article we study the numerical approximation of MDPs with infinite time horizon, finite control set, and general state spaces. Our set-up in particular covers infinite-horizon optimal stopping problems of discrete time Markov processes. A key tool to solve MDPs are Bellman equations which characterize the value functions of the MDPs and determine the optimal control strategies. By combining ideas from the full-history recursive multilevel Picard approximation method, which was recently introduced to solve certain nonlinear partial differential equations, and ideas from Q-learning we introduce a class of suitable nonlinear Monte Carlo methods and prove that the proposed methods do overcome the curse of dimensionality in the numerical approximation of the solutions of Bellman equations and the associated discrete time stochastic optimal control *** Codes 90C40, 90C39, 60J05, 93E20, 65C05 Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Fuzzy Logical-Linguistic analysis of data Quality in Financial Information Systems

Fuzzy Logical-Linguistic Analysis of Data Quality in Financi...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Lomazov Vadim Lomazov Vladislav Anichin David Petrosov Department of Applied Informatics and Mathematics Belgorod State Agricultural University Belgorod Russia Department of Economics Belgorod State Agricultural University Belgorod Russia Department of Data Analysis and Machine Learning Financial University Moscow Russia

The proposed approach to the analysis of data quality is based on the use of the apparatus of fuzzy logical-linguistic analysis by L. Zadeh. The indicators are presented in the form of linguistic variables, the semantics of which are set by experts. The constructed hierarchy of indicators makes it possible to increase the scientific validity of decisions while ensuring the quality of data in financial systems.

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RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training

arXiv

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arXiv 2023年

作者： Zhou, Chulun Liang, Yunlong Meng, Fandong Xu, Jinan Su, Jinsong Zhou, Jie Pattern Recognition Center WeChat AI Tencent Inc. China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Informatics Xiamen University Xiamen China

Multilingual vision-language (V&L) pretraining has achieved remarkable progress in learning universal representations across different modalities and languages. In spite of recent success, there still remain challenges limiting further improvements of V&L pre-trained models in multilingual settings. Particularly, current V&L pre-training methods rely heavily on strictly-aligned multilingual image-text pairs generated from English-centric datasets through machine translation. However, the cost of collecting and translating such strictly-aligned datasets is usually unbearable. In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs. Specifically, we design a regularized cross-lingual visio-textual contrastive learning objective that constrains the representation proximity of weakly-aligned visio-textual inputs according to textual relevance. Besides, existing V&L pre-training approaches mainly deal with visual inputs by either region-of-interest (ROI) features or patch embeddings. We flexibly integrate the two forms of visual features into our model for pre-training and downstream multi-modal tasks. Extensive experiments on 5 downstream multi-modal tasks across 6 languages demonstrate the effectiveness of our proposed method over competitive contrast models with stronger zero-shot capability. © 2023, CC BY-NC-ND.

关键词： Image segmentation

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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