scholarly journals OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

2021 ◽  
Author(s):  
Yunyou Huang ◽  
Nana Wang ◽  
Suqin Tang ◽  
Li Ma ◽  
Tianshu Hao ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
The State ◽  
Clinical Settings ◽  
Medical Systems ◽  
Learning Framework ◽  
Personalized Diagnosis ◽  
Set Up ◽  
Art And State ◽  
Unnecessary Testing

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer's disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services.

scholarly journals Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

2020 ◽  
Author(s):  
Cong Fei ◽  
Bin Wang ◽  
Yuzheng Zhuang ◽  
Zongzhang Zhang ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
Data Augmentation ◽  
State Of The Art ◽  
Robot Learning ◽  
Imitation Learning ◽  
Proper Understanding ◽  
Learning Framework ◽  
Strategy Game ◽  
Real Time Strategy Game

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

scholarly journals Lifelong Spectral Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5867-5874
Author(s):  
Gan Sun ◽  
Yang Cong ◽  
Qianqian Wang ◽  
Jun Li ◽  
Yun Fu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Spectral Clustering ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Orthogonal Basis ◽  
Learning Framework ◽  
The Past ◽  
Benchmark Datasets ◽  
Over Time

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.

Model Testing for Vortex Induced Motions of Spar Platforms

2004 ◽  
Author(s):  
Mehernosh Irani ◽  
Lyle Finn
Keyword(s):  
Gulf Of Mexico ◽  
Model Test ◽  
State Of The Art ◽  
Model Testing ◽  
Model Tests ◽  
The State ◽  
Test Results ◽  
Test Set ◽  
Vortex Induced Vibrations ◽  
Set Up

The state-of-the art in model testing for Vortex Induced Vibrations (VIV) of Spars is presented. Important issues related to Spar VIV model testing are highlighted. The parameters that need to be modeled including hull geometry, strake configuration, mass and mooring properties and, considerations of test set-up and instrumentation are discussed. Results are presented from model tests of an as-built Spar deployed in the Gulf of Mexico. It is shown that the model test results compare well with the VIV responses measured in the field.

scholarly journals Scene text removal via cascaded text stroke detection and erasing

2021 ◽  
Vol 8 (2) ◽  
pp. 273-287
Author(s):  
Xuewei Bian ◽  
Chaoqun Wang ◽  
Weize Quan ◽  
Juntao Ye ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Processing Unit ◽  
Final Model ◽  
Scene Text ◽  
End To End

AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

scholarly journals Waste generation prediction under uncertainty in smart cities through deep neuroevolution

2019 ◽  
pp. 128-138 ◽  
Author(s):  
Andrés Camero ◽  
Jamal Toutouh ◽  
Javier Ferrer ◽  
Enrique Alba
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Smart Cities ◽  
Uncertain Data ◽  
Recurrent Network ◽  
The State ◽  
Waste Generation ◽  
Waste Collection ◽  
The Way

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

scholarly journals Seeking for the Maximum Symmetric Rank

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 247 ◽  
Author(s):  
Alessandro De Paris
Keyword(s):  
State Of The Art ◽  
Symmetric Tensor ◽  
Upper Bounds ◽  
The State ◽  
Tensor Rank ◽  
Symmetric Tensors ◽  
Lower And Upper Bounds ◽  
Symmetric Tensor Rank ◽  
Set Up ◽  
Divided Powers

We present the state-of-the-art on maximum symmetric tensor rank, for each given dimension and order. After a general discussion on the interplay between symmetric tensors, polynomials and divided powers, we introduce the technical environment and the methods that have been set up in recent times to find new lower and upper bounds.

scholarly journals A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450018 ◽  
Author(s):  
XIN LIU ◽  
WEICHU LIU ◽  
TSUYOSHI MURATA ◽  
KEN WAKITA
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
General Structure ◽  
The State ◽  
New Method ◽  
World Systems ◽  
Relational Networks ◽  
Relational Network ◽  
Art Techniques

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

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