Analysis of task decoupling characteristics of null space projector with uncertainty from modelling error

Methods for designing a multitask controller for redundant robots are often implemented by a null space projector. Although null-space projection-based controllers can achieve the task decoupling performance among prioritized tasks, it is vulnerable to uncertainty due to modeling errors in practice. Accordingly, this paper provides an analytical proof of the task coupling effect caused by the uncertainty and verifies it through a simulation

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.1114Nsciescopu

OCAM: Out-of-core coordinate descent algorithm for matrix completion

Recently, there are increasing reports that most datasets can be actually stored in disks of a single off-the-shelf workstation, and utilizing out-of-core methods is much cheaper and even faster than using a distributed system. For these reasons, out-of-core methods have been actively developed for machine learning and graph processing. The goal of this paper is to develop an efficient out-of-core matrix completion method based on coordinate descent approach. Coordinate descent-based matrix completion (CD-MC) has two strong benefits over other approaches: 1) it does not involve heavy computation such as matrix inversion and 2) it does not have step-size hyper-parameters, which reduces the effort for hyper-parameter tuning. Existing solutions for CD-MC have been developed and analyzed for in-memory setting and they do not take disk-I/O into account. Thus, we propose OCAM, a novel out-of-core coordinate descent algorithm for matrix completion. Our evaluation results and cost analyses provide sound evidences supporting the following benefits of OCAM: (1) Scalability - OCAM is a truly scalable out-of-core method and thus decomposes a matrix larger than the size of memory, (2) Efficiency - OCAM is super fast. OCAM is up to 10x faster than the state-of-the-art out-of-core method, and up to 4.1x faster than a competing distributed method when using eight machines. The source code of OCAM will be available for reproducibility. (C) 2019 Published by Elsevier Inc.11Nsciescopu

Efficient Feature Weighting Methods for Ranking

Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generalization performance of ranking functions in information retrieval. Due to fundamental differences between classification and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, has been recently proposed, which exploits importance of each feature and similarity between every pair of features. However, GAS must compute the similarity scores of all pairs of features, thus it is not scalable for high-dimensional data and its performance degrades on nonlinear ranking functions. This paper proposes novel algorithms, RankWrapper and RankFilter, which is scalable for high-dimensional data and also performs reasonably well on nonlinear ranking functions. RankWrapper and RankFilter are designed based on the key idea of Relief algorithm. Relief is a feature selection algorithm for classification, which exploits the notions of hits (data points within the same class) and misses (data points from different classes) for classification. However, there is no such notion of hits or misses in ranking. The proposed algorithms instead utilize the ranking distances of nearest data points in order to identify the key features for ranking. Our extensive experiments show that RankWrapper and RankFilter generate higher accuracy overall than the GAS and traditional Relief algorithms adapted for ranking, and run substantially faster than the GAS on high dimensional data.11scopu

RecTime: Real-Time recommender system for online broadcasting

Recommender systems for online broadcasting become important as the number of channels has been increasing. In online broadcasting, to provide accurate recommendation, recommender systems should take time factors as well as users' condition into account, but the conventional systems don't. This paper proposes a real-time recommender system for online broadcasting called RecTime which considers time factors and preferences simultaneously. Specifically, RecTime employs a 4-d tensor factorization, which considers two more dimensions regarding the time factors, while typical collaboriative filtering methods only consider two dimensions, users and items. By factorizing the 4-d tensor, the system naturally identifies the recommendation time and the items at the same time. In our experiments on real-world data, RecTime properly models users' watching patterns and significantly outperforms previous methods in terms of the accuracy on the recommendation time as well as the items. (C) 2017 Elsevier Inc. All rights reserved.1141sciescopu

Residual-Based External Torque Estimation in Series Elastic Actuators Over a Wide Stiffness Range: Frequency Domain Approach

This letter presents an enhanced external torque estimation algorithm for series elastic actuators (SEAs) expanding the usability of the residual-based technique. Although the residual method demonstrates online torque estimation capability in diverse applications and thus becomes popular, it is practically challenging to achieve accurate estimation performance in a wide stiffness range of SEAs. The performance degradation is mainly induced by inaccurate transmission torque information, which is stemmed from unexpected errors in the spring deflection based torque sensing. To overcome the issue, this letter analyses the limitation of the conventional approach in frequency domain and proposes the enhanced residual to be used over a wide stiffness range of SEAs. The performance of proposed method is comparatively verified with conventional method in the both of simulations and experiments.1

Scalable disk-based topic modeling for memory limited devices

Disk-based algorithms have the ability to process large-scale data which do not fit into the memory, so they provide good scalability to a mobile device with limited memory resources. In general, the speed of disk I/O is much slower than that of memory access, the total amount of disk I/O is the most crucial factor which determines the efficiency of disk-based algorithms. This paper proposes BlockLDA, an efficient disk-based Latent Dirichlet Allocation (LDA) inference algorithm which can efficiently infer an LDA model when both of the data and model do not fit into the memory. BlockLDA manages the data and model as a set of small blocks so that it can support efficient disk I/O as well as process the LDA inference in a block-wise manner. In addition, it utilizes advanced techniques which help to minimize the amount of disk I/O, including 1) a space reduction algorithm to dynamically manage the block-wise model considering its changing sparsity and 2) a local scheduling algorithm to carefully select the next data blocks so that the number of page faults is minimized. Our experimental results demonstrate that BlockLDA shows better scalability and efficiency than its disk-based and in-memory competitors under the memory-limited environment. (C) 2019 Elsevier Inc. All rights reserved.11Nsciescopu

Robust Elastic Structure Preserving Control for High Impedance Rendering of Series Elastic Actuator

In this paper, a new robust approach is proposed to address the limitation of impedance rendering for Series Elastic Actuators (SEA). The concept of Elastic Structure Preserving (ESP) control allows for the attachment of desired load-side dynamics to the SEA while maintaining a passivity condition, regardless of the parameters for the attached dynamics. The characteristics of ESP control are revisited and translated in the frequency domain, which grants a new perspective to identify its advantages compared to conventional impedance control in terms of passivity. Additionally, we analyze the degradation of performance due to unwanted disturbance and uncertainties in spring stiffness and motor inertia, and a new form of the robust ESP method is proposed by endowing disturbance rejection capability and robustness against uncertainty