Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a hard clustering with convergence guarantees similar to those of the k-means algorithm. Empirical results from a synthetic test with moving Gaussian clusters and a test with real ADS-B aircraft trajectory data demonstrate that the algorithm requires orders of magnitude less computational time than contemporary probabilistic and hard clustering algorithms, while providing higher accuracy on the examined datasets.Comment: This paper is from NIPS 2013. Please use the following BibTeX citation: @inproceedings{Campbell13_NIPS, Author = {Trevor Campbell and Miao Liu and Brian Kulis and Jonathan P. How and Lawrence Carin}, Title = {Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2013}

Behaviour-based identification of student communities in virtual worlds

VirtualWorlds (VW) have gained popularity in the last years in domains like training or education mainly due to their highly immersive and interactive 3D characteristics. In these platforms, the user (represented by an avatar) can move and interact in an artificial world with a high degree of freedom. They can talk, chat, build and design objects, program and compile their own developed programs, or move (flying, teleporting, walking or running) to different parts of the world. Although these environments provide an interesting working place for students and educators, VW platforms (such as OpenCobalt or OpenSim amongst others) rarely provide mechanisms to facilitate the automatic (or semi-automatic) behaviour analysis of users interactions. Using a VW platform called VirtUAM, the information extracted from different experiments are used to analyse and define students communities based on their behaviour. To define the individual student behaviour, different characteristics are extracted from the system, such as the avatar position (in form of GPS coordinates) and the set of actions (interactions) performed by students within the VW. Later this information is used to automatically detect behavioural patterns. This paper shows how this information can be used to group students in different communities based on their behaviour. Experimental results show how community identification can be successfully perform using K-Means algorithm and Normalized Compression Distance. Resulting communities contains users working in near places or with similar behaviours inside the virtual world.This work has been funded by the Spanish Ministry of Science and Innovation under the project ABANT (TIN2010-19872/TSI)

Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics

生体分子の動きを効率的に捉えるシミュレーション技術を開発 --超高周波超音波照射によってタンパク質と医薬品の結合計算を加速--. 京都大学プレスリリース. 2021-05-28.Capturing the dynamic processes of biomolecular systems in atomistic detail remains difficult despite recent experimental advances. Although molecular dynamics (MD) techniques enable atomic-level observations, simulations of “slow” biomolecular processes (with timescales longer than submilliseconds) are challenging because of current computer speed limitations. Therefore, we developed a method to accelerate MD simulations by high-frequency ultrasound perturbation. The binding events between the protein CDK2 and its small-molecule inhibitors were nearly undetectable in 100-ns conventional MD, but the method successfully accelerated their slow binding rates by up to 10–20 times. Hypersound-accelerated MD simulations revealed a variety of microscopic kinetic features of the inhibitors on the protein surface, such as the existence of different binding pathways to the active site. Moreover, the simulations allowed the estimation of the corresponding kinetic parameters and exploring other druggable pockets. This method can thus provide deeper insight into the microscopic interactions controlling biomolecular processes

New methods for clustering district heating users based on consumption patterns

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordUnderstanding energy users’ consumption patterns benefits both utility companies and consumers as it can support improving energy management and usage strategies. The rapid deployment of smart metering facilities has enabled the analysis of consumption patterns based on high-precision real usage data. This paper investigates data-driven unsupervised learning techniques to partition district heating users into separate clusters such that users in the same cluster possess similar consumption pattern. Taking into account the characteristics of heat usage, three new approaches of extracting pattern features from consumption data are proposed. Clustering algorithms with these features are executed on a real-world district heating consumption dataset. The results can reveal typical daily consumption patterns when the consumption linearly related to ambient temperature is removed. Users with heat usages that are highly imbalanced within a certain period of time or are highly consistent with the utility heat production load can also be grouped together. Our methods can facilitate gaining better knowledge regarding the behaviors of district heating users and hence can potentially be used to formulate new pricing and energy reduction solutions.European Commissio

MoBiSea: a binary search algorithm for product clustering in Industry 4.0

Proprietary systems used to modernize Industry 4.0 usually involve high financial costs. Consequently, using low-cost devices with the same functionalities, capable of replacing these proprietary systems but at a lower cost, has become an incipient trend. However, these low-cost devices usually come with electromagnetic interference problems as they are encapsulated in electrical panels, sitting alongside electromechanical devices. In this article, we present Mode Binary Search, an algorithm specifically designed for use in a low-cost automated-industrial-productivity-data-collection system. Specifically, productivity data are obtained from the availability and sealing signals of the thermoplastic sealing machines in production lines belonging to the agri-food industry. Mode Binary Search was designed to cluster sealing signals, thus enabling us to identify which products have been made. Furthermore, the algorithm determines when the manufacturing of each product starts and ends, in other words, the exact moment a product change occurs and all this without the need for operator supervision or intervention. Finally, we compared our algorithm, based on binary search, with three clustering mechanisms: k-means, k-rms and x-means. Out of all the cases we analyzed, the maximum error committed by Mode Binary Search is limited to 2.69%, thereby outperforming all others

Multiagent planning with Bayesian nonparametric asymptotics

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 95-105).Autonomous multiagent systems are beginning to see use in complex, changing environments that cannot be completely specified a priori. In order to be adaptive to these environments and avoid the fragility associated with making too many a priori assumptions, autonomous systems must incorporate some form of learning. However, learning techniques themselves often require structural assumptions to be made about the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has prevented the widespread use of Bayesian nonparametrics in realtime autonomous planning systems. This thesis provides a suite of algorithms for tractable, realtime, multiagent planning under uncertainty using Bayesian nonparametrics. The first contribution is a multiagent task allocation framework for tasks specified as Markov decision processes. This framework extends past work in multiagent allocation under uncertainty by allowing exact distribution propagation instead of sampling, and provides an analytic solution time/quality tradeoff for system designers. The second contribution is the Dynamic Means algorithm, a novel clustering method based upon Bayesian nonparametrics for realtime, lifelong learning on batch-sequential data containing temporally evolving clusters. The relationship with previous clustering models yields a modelling scheme that is as fast as typical classical clustering approaches while possessing the flexibility and representational power of Bayesian nonparametrics. The final contribution is Simultaneous Clustering on Representation Expansion (SCORE), which is a tractable model-based reinforcement learning algorithm for multimodel planning problems, and serves as a link between the aforementioned task allocation framework and the Dynamic Means algorithmby Trevor D. J. Campbell.S.M