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"Information": models, code, and papers

Force-based Algorithm for Motion Planning of Large Agent Teams

Sep 10, 2019
Samaneh Hosseini Semnani, Anton de Ruiter, Hugh Liu

This paper presents a distributed, efficient, scalable and real-time motion planning algorithm for a large group of agents moving in 2 or 3-dimensional spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: collision avoidance and navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm is able to find collision-free motions with lower transition time, free from velocity state information of neighbouring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2D and 3D benchmark simulation scenarios, with results outperforming existing methods.


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SF-Net: Structured Feature Network for Continuous Sign Language Recognition

Aug 04, 2019
Zhaoyang Yang, Zhenmei Shi, Xiaoyong Shen, Yu-Wing Tai

Continuous sign language recognition (SLR) aims to translate a signing sequence into a sentence. It is very challenging as sign language is rich in vocabulary, while many among them contain similar gestures and motions. Moreover, it is weakly supervised as the alignment of signing glosses is not available. In this paper, we propose Structured Feature Network (SF-Net) to address these challenges by effectively learn multiple levels of semantic information in the data. The proposed SF-Net extracts features in a structured manner and gradually encodes information at the frame level, the gloss level and the sentence level into the feature representation. The proposed SF-Net can be trained end-to-end without the help of other models or pre-training. We tested the proposed SF-Net on two large scale public SLR datasets collected from different continuous SLR scenarios. Results show that the proposed SF-Net clearly outperforms previous sequence level supervision based methods in terms of both accuracy and adaptability.

* 12 pages, 8 figures 

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Asynchronous Network Formation in Unknown Unbounded Environments

Aug 02, 2019
Selim Engin, Volkan Isler

In this paper, we study the Online Network Formation Problem (ONFP) for a mobile multi-robot system. Consider a group of robots with a bounded communication range operating in a large open area. One of the robots has a piece of information which has to be propagated to all other robots. What strategy should the robots pursue to disseminate the information to the rest of the robots as quickly as possible? The initial locations of the robots are unknown to each other, therefore the problem must be solved in an online fashion. For this problem, we present an algorithm whose competitive ratio is $O(H \cdot \max\{M,\sqrt{M H}\})$ for arbitrary robot deployments, where $M$ is the largest edge length in the Euclidean minimum spanning tree on the initial robot configuration and $H$ is the height of the tree. We also study the case when the robot initial positions are chosen uniformly at random and improve the ratio to $O(M)$. Finally, we present simulation results to validate the performance in larger scales and demonstrate our algorithm using three robots in a field experiment.


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Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks

Jul 24, 2019
Georgios Katsimpras, Georgios Paliouras

This paper proposes a method to guide tensor factorization, using class labels. Furthermore, it shows the advantages of using the proposed method in identifying nodes that play a special role in multi-relational networks, e.g. spammers. Most complex systems involve multiple types of relationships and interactions among entities. Combining information from different relationships may be crucial for various prediction tasks. Instead of creating distinct prediction models for each type of relationship, in this paper we present a tensor factorization approach based on RESCAL, which collectively exploits all existing relations. We extend RESCAL to produce a semi-supervised factorization method that combines a classification error term with the standard factor optimization process. The coupled optimization approach, models the tensorial data assimilating observed information from all the relations, while also taking into account classification performance. Our evaluation on real-world social network data shows that incorporating supervision, when available, leads to models that are more accurate.

* Presented at the Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks 2018 

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Next-Generation Inertial Navigation Computation Based on Functional Iteration

May 28, 2019
Yuanxin Wu

Inertial navigation computation is to acquire the attitude, velocity and position information of a moving body by integrating inertial measurements from gyroscopes and accelerometers. Over half a century has witnessed great efforts in coping with the motion non-commutativity errors to accurately compute the navigation information as far as possible, so as not to comprise the quality measurements of inertial sensors. Highly dynamic applications and the forthcoming cold-atom precision inertial navigation systems demand for even more accurate inertial navigation computation. The paper gives birth to an ultimate inertial navigation algorithm to fulfill that demand, named the iNavFIter, which is based on a brand new framework of functional iterative integration and Chebyshev polynomials. Remarkably, the proposed iNavFIter reduces the non-commutativity errors to almost machine precision, namely, the coning/sculling/scrolling errors that have perplexed the navigation community for long. Numerical results are provided to demonstrate its accuracy superiority over the-state-of-the-art inertial navigation algorithms at affordable computation cost.

* 14 pages, 10 figures 

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BVS Corpus: A Multilingual Parallel Corpus of Biomedical Scientific Texts

May 05, 2019
Felipe Soares, Martin Krallinger

The BVS database (Health Virtual Library) is a centralized source of biomedical information for Latin America and Carib, created in 1998 and coordinated by BIREME (Biblioteca Regional de Medicina) in agreement with the Pan American Health Organization (OPAS). Abstracts are available in English, Spanish, and Portuguese, with a subset in more than one language, thus being a possible source of parallel corpora. In this article, we present the development of parallel corpora from BVS in three languages: English, Portuguese, and Spanish. Sentences were automatically aligned using the Hunalign algorithm for EN/ES and EN/PT language pairs, and for a subset of trilingual articles also. We demonstrate the capabilities of our corpus by training a Neural Machine Translation (OpenNMT) system for each language pair, which outperformed related works on scientific biomedical articles. Sentence alignment was also manually evaluated, presenting an average 96% of correctly aligned sentences across all languages. Our parallel corpus is freely available, with complementary information regarding article metadata.

* Accepted at the Copora conference. arXiv admin note: text overlap with arXiv:1905.01715 

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Cross-Modal Message Passing for Two-stream Fusion

Apr 30, 2019
Dong Wang, Yuan Yuan, Qi Wang

Processing and fusing information among multi-modal is a very useful technique for achieving high performance in many computer vision problems. In order to tackle multi-modal information more effectively, we introduce a novel framework for multi-modal fusion: Cross-modal Message Passing (CMMP). Specifically, we propose a cross-modal message passing mechanism to fuse two-stream network for action recognition, which composes of an appearance modal network (RGB image) and a motion modal (optical flow image) network. The objectives of individual networks in this framework are two-fold: a standard classification objective and a competing objective. The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one. We quantitatively show that the proposed CMMP fuses the traditional two-stream network more effectively, and outperforms all existing two-stream fusion method on UCF-101 and HMDB-51 datasets.

* 2018 IEEE International Conference on Acoustics, Speech and Signal Processing 

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Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module

Apr 22, 2019
Yingchao Feng, Wenhui Diao, Zhonghan Chang, Menglong Yan, Xian Sun, Xin Gao

The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between objects of the same class. The SLC module applies a sequence of dilation convolution blocks to progressively learn multi-scale context information in the mask branch. Besides, we try to add SLC module to different locations in our framework and experiment with the effect of different parameter settings. Comparative experiments are conducted on remote sensing images acquired by QuickBird with a resolution of $0.5m-1m$ and the results show that the proposed method achieves state-of-the-art performance.

* 4 pages, 5 figures, IEEE Geoscience and Remote Sensing Society 2019 

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Learning Disentangled Representations of Satellite Image Time Series

Mar 21, 2019
Eduardo Sanchez, Mathieu Serrurier, Mathias Ortner

In this paper, we investigate how to learn a suitable representation of satellite image time series in an unsupervised manner by leveraging large amounts of unlabeled data. Additionally , we aim to disentangle the representation of time series into two representations: a shared representation that captures the common information between the images of a time series and an exclusive representation that contains the specific information of each image of the time series. To address these issues, we propose a model that combines a novel component called cross-domain autoencoders with the variational autoencoder (VAE) and generative ad-versarial network (GAN) methods. In order to learn disentangled representations of time series, our model learns the multimodal image-to-image translation task. We train our model using satellite image time series from the Sentinel-2 mission. Several experiments are carried out to evaluate the obtained representations. We show that these disentangled representations can be very useful to perform multiple tasks such as image classification, image retrieval, image segmentation and change detection.


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Random Pairwise Shapelets Forest

Mar 19, 2019
Mohan Shi, Zhihai Wang, Jodong Yuan, Haiyang Liu

Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limited information for only one branch of the decision tree, resulting in insufficient accuracy and interpretability. Third, randomized ensemble causes interpretability declining. For that, this paper presents Random Pairwise Shapelets Forest (RPSF). RPSF combines a pair of shapelets from different classes to construct random forest. It omits threshold searching to be more efficient, includes more information for each node of the forest to be more effective. Moreover, a discriminability metric, Decomposed Mean Decrease Impurity (DMDI), is proposed to identify influential region for every class. Extensive experiments show RPSF improves the accuracy and training speed of shapelet-based forest. Case studies demonstrate the interpretability of our method.

* PAKDD 2018: Advances in Knowledge Discovery and Data Mining 

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