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Sequential Point Clouds: A Survey [article]

Haiyan Wang, Yingli Tian
2022 arXiv   pre-print
However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud  ...  This paper presents an extensive review of the deep learning-based methods for sequential point cloud research including dynamic flow estimation, object detection \& tracking, point cloud segmentation,  ...  (a) Voxel-based representation. (b) Point-based representation. (c) Lattice-based representation. Fig. 5 : 5 Fig. 5: The illustration of different representations for scene flow estimation methods.  ... 
arXiv:2204.09337v2 fatcat:su7fdqd3xja53jg4zikqcpvoaa


Tharam S. Dillon, Simon C.K. Shiu, Sankar K. Pal
2004 Applied intelligence (Boston)  
A simultaneous optimization method of a CBR system using a genetic algorithm (GA) for financial forecasting is then explained by Kim.  ...  Systems based on this concept are finding widespread applications in problems like medical diagnosis and law interpretation where the knowledge available is incomplete and/or evidence is sparse.  ...  For the first step, a method which uses a hierarchical representation of the target object is used to match the cases.  ... 
doi:10.1023/ fatcat:qdl3bjsls5f4bdtykj4l5gkz5a

Teleconsultation demand classification and service analysis

Wenjia Chen, Jinlin Li
2021 BMC Medical Informatics and Decision Making  
Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study.  ...  In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity  ...  Acknowledgements We wish to acknowledge the help of the staff in the National Telemedicine Center of China (NTCC) for this study.  ... 
doi:10.1186/s12911-021-01610-x fatcat:pya66gzt6vgbviuimfmnrmqc2m

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Regina Sousa, Tiago Lima, António Abelha, José Machado
2021 Electronics  
HTM is based on the biological functions of the brain as well as its learning mechanism.  ...  Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society.  ...  Acknowledgments: We thank the administrative staff of the University of Minho for their availability. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10141630 fatcat:dkzwmy5mpnft3os5jsmdf2t2r4

TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [article]

Vida Adeli, Mahsa Ehsanpour, Ian Reid, Juan Carlos Niebles, Silvio Savarese, Ehsan Adeli, Hamid Rezatofighi
2021 arXiv   pre-print
In this paper, we propose a novel TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph attentional networks to model the human-human and human-object interactions both in the input space  ...  Finally, we introduce a new benchmark for this joint task based on two challenging datasets (PoseTrack and 3DPW) and propose evaluation metrics to measure the effectiveness of predictions in the global  ...  Error rate for ablation study on 3DPW dataset (in cm) using a sparse or dense graph as input skeleton representation.  ... 
arXiv:2104.04029v2 fatcat:pbsnyjefv5c2hhubsacxcmoo4u

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

Ruofei Ouyang, Bryan Kian Hsiang Low
2019 Autonomous Robots  
Gao, liang Zhao* Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method Jinyue Su, Jiacheng Xu, Xipeng Qiu*, Xuanjing Huang Incorporating GAN for Negative Sampling in Knowledge Representation  ...  Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification Mang Ye, Xiangyuan Lan, Jiawei Li, Pong c Yuen* Hierarchical LSTM for Sign Language Translation Dan Guo*, wengang zhou  ... 
doi:10.1007/s10514-018-09826-z fatcat:67yqhwmgozccxni56rxmuapjgm

Designing Decision Support Systems for Emergency Response: Challenges and Opportunities [article]

Geoffrey Pettet and Hunter Baxter and Sayyed Mohsen Vazirizade and Hemant Purohit and Meiyi Ma and Ayan Mukhopadhyay and Abhishek Dubey
2022 arXiv   pre-print
This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies.  ...  In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.  ...  ACKNOWLEDGEMENTS This work is sponsored by the National Science Foundation under award numbers CNS1640624, CNS1818901, IIS1814958, IIS-1815459 and a grant from the Tennessee Department of Transportation  ... 
arXiv:2202.11268v2 fatcat:ck7xsflwr5hixdjdekckcvoxdm

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

Mohamed Massaoudi, Ines Chihi, Haitham Abu-Rub, Shady S. Refaat, Fakhreddine S. Oueslati
2021 IEEE Access  
Non-deep learning methods comprise one to three operational layers, whereas DL methods stacks multiple layers (more than three) of simple modules hierarchically.  ...  In [30] , a comprehensive review of RE forecasting methods has been conducted with a particular emphasis on wind and solar energy.  ... 
doi:10.1109/access.2021.3117004 fatcat:nxgyb5e4rvbynpmgzppv7ecbre

Forecasting with Many Predictors Using Message Passing Algorithms

Dimitris Korobilis
2017 Social Science Research Network  
In a forecasting exercise involving a large set of orthogonal macroeconomic predictors, I show that Bayesian shrinkage estimators based on GAMP perform very well compared to a large set of alternatives  ...  Using Monte Carlo simulations I establish that in certain scenarios GAMP can achieve estimation accuracy comparable to traditional Markov chain Monte Carlo methods, at a tiny fraction of the computing  ...  Here, I build on Rangan (2011) and present an intuitive derivation of generalized approximate message passing based on graphical methods.  ... 
doi:10.2139/ssrn.2977838 fatcat:tifrxophf5alfkzcqmxuc2wz74

Predicting Future Agent Motions for Dynamic Environments

Fabio Previtali, Alejandro Bordallo, Luca Iocchi, Subramanian Ramamoorthy
2016 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)  
We address this by combining a novel multi-camera tracking method, efficient multi-resolution representations of state and a standard Inverse Reinforcement Learning (IRL) technique, to demonstrate performance  ...  We present empirical experiments using data gathered in our own lab and external corpora (VIRAT), based on which we find that our algorithm is not only efficiently implementable on a resource constrained  ...  These methods include those based on classifiers with structured outputs, which directly discriminate at the level of trajectories albeit with richer representations of the same.  ... 
doi:10.1109/icmla.2016.0024 dblp:conf/icmla/PrevitaliBIR16 fatcat:pbifal5yt5gfbjmpfc2dv657ra

Bayesian demography 250 years after Bayes

Jakub Bijak, John Bryant
2016 Population Studies  
We focus on three applications: demographic forecasts, limited data, and highly structured or complex models.  ...  The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies.  ...  All the views and interpretations reported in this paper are those of the authors, and should not be attributed to any institution with which they are or were affiliated.  ... 
doi:10.1080/00324728.2015.1122826 pmid:26902889 pmcid:PMC4867874 fatcat:44i4xfn2infznatxt3ger536oy

Short-Term Load Forecasting Using a Novel Deep Learning Framework

Xiaoyu Zhang, Rui Wang, Tao Zhang, Yajie Liu, Yabing Zha
2018 Energies  
This novel framework is used for short-term load forecasting based on the historical power load data of a town in the UK.  ...  In this study, a novel deep-learning framework based on a restricted Boltzmann machine (RBM) and an Elman neural network is presented.  ...  In order to further demonstrate the forecast performance of our proposed method, we decomposed the dataset based on different seasons.  ... 
doi:10.3390/en11061554 fatcat:sl3bn4uqknfdjhixbypm6pwzzy

A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos [article]

Xianlin Zeng, Yalong Jiang, Wenrui Ding, Hongguang Li, Yafeng Hao, Zifeng Qiu
2021 arXiv   pre-print
In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different  ...  levels of graph representations.  ...  Based on the hierarchical graph representation inference introduced in Section III-B, three independent branches are built to provide predictions on the level of anomalies.  ... 
arXiv:2112.04294v2 fatcat:vi26nkpf2jhc7b2sjseo4b4rii

Biomedical literature classification with a CNNs-based hybrid learning network

Yan Yan, Xu-Cheng Yin, Chun Yang, Sujian Li, Bo-Wen Zhang, Constantino Carlos Reyes-Aldasoro
2018 PLoS ONE  
OPEN ACCESS Citation: Yan Y, Yin X-C, Yang C, Li S, Zhang B-W (2018) Biomedical literature classification with a CNNs-based hybrid learning network. PLoS ONE 13(7): e0197933.  ...  Additionally, we have designed a hierarchical coarse to fine style indexing structure for learning and classifying documents, and a novel feature extension approach with word sequence embedding and Wikipedia  ...  Shen [50] proposed a new latent semantic representation for web searches based on CNN.  ... 
doi:10.1371/journal.pone.0197933 pmid:30048461 pmcid:PMC6061982 fatcat:yarhcljdyna35hcnekwhcin7uu

Kernel Spectral Clustering and applications [article]

Rocco Langone, Raghvendra Mall, Carlos Alzate, Johan A. K. Suykens
2015 arXiv   pre-print
Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented.  ...  Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing.  ...  In the next Sections two different methods to obtain a sparse KSC model, based on the Incomplete Cholesky Decomposition (ICD) and L 1 and L 0 penalties respectively, are discussed.  ... 
arXiv:1505.00477v1 fatcat:msx7kebdxzgnjcgfk4ydmdknmy
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