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On-orbit measurements of the ISS atmosphere by the vehicle cabin atmosphere monitor

On-orbit measurements of the ISS atmosphere by the vehicle c...

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41st International Conference on Environmental Systems 2011, ICES 2011

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Haemmerle, V.R. Hofman, J. Heinrichs, W.M. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. MacAskill, J.A. Madzunkov, S.M. Mandrake, L. Rust, T.M. Schaefer, R.T. Thomas, J.L. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech United States Instrument Autonomy Group JPL/Caltech United States MicroDevices Group JPL/CalTech United States Optical Communications Group JPL/Caltech United States Processing Algorithms and Calibration Engineering JPL/Caltech United States Inst System Engineering JPL/Caltech United States Instruments and Science Data Systems JPL/Caltech United States Instrument Integration and Test JPL/Caltech United States Planetary Chemistry and Astrobiology JPL/Caltech United States High Capacity Computing and Modeling JPL/Caltech United States Machine Learning and Instrument Autonomy Group JPL/Caltech United States SpaceX 1 Rocket Rd Hawthorne CA 90250 United States Advanced Computer Systems and Technology JPL/Caltech United States Advanced Instrument Concepts JPL/Caltech United States

ISBN: (纸本)9781600869488

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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Self-adaptive fuzzification in fuzzy decision tree induction

Self-adaptive fuzzification in fuzzy decision tree induction

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International Conference on machine learning and Cybernetics

作者： Dai, Xiao-Dong Gao, Lin-Qing Dong, Chun-Ru Department of Mathematics and Computer Science Hebei University Baoding 071002 China Graduate School Hebei University Baoding 071002 China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology 510006 Guangzhou China

ISBN: (纸本)9781424465262

One of the most important issues in fuzzy decision tree learning is the fuzzification of input data. This paper proposes a self-adaptive data fuzzification algorithm based on the self-organizing map (SOM) technology, which can automatically determine the number and coordinates of centers in triangular membership functions. Then the membership degree of each sample to all fuzzy subsets can be calculated. Finally, a fuzzy decision tree can be learned from the fuzzified training samples by any selected fuzzy decision tree heuristic algorithm. Experimental results on UCI data set iris show that the new approach outperform the traditional fuzzification methods. © 2010 IEEE.

关键词： Self organizing maps

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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

引用

International Conference on machine learning and Cybernetics

作者： Li, Wen-Liagn Yu, Rui-Hua Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine learning Repository. © 2010 IEEE.

关键词： Decision trees

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Combining rule induction and reinforcement learning: An agent-based vehicle routing

Combining rule induction and reinforcement learning: An agen...

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9th International Conference on machine learning and Applications, ICMLA 2010

作者： Śniezyński, Bartlomiej Wójcik, Wojciech Gehrke, Jan D. Wojtusiak, Janusz Department of Computer Science AGH University of Science and Technology Krakow Poland Center for Computing and Communication Technologies - TZI Universität Bremen Bremen Germany Machine Learning and Inference Laboratory George Mason University Fairfax VA United States

ISBN: (纸本)9780769543000

Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning time. The method is particularly suitable for agents operating in dynamically changing environments, in which fast response to changes is required. The method has been tested in transportation logistics domain in which agents represent vehicles being routed in a simple road network. Experimental results indicate that in this domain the method performs better than traditional Q-learning, as indicated by statistical comparison. © 2010 IEEE.

关键词： Intelligent agents

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Wireless Sensor Networks for Home Appliance Energy Management based on ZigBee technology

Wireless Sensor Networks for Home Appliance Energy Managemen...

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International Conference on machine learning and Cybernetics

作者： Wang, Jian-Jian Wang, Shuo Department of Electronics and Communication Engineering North China Electric Power University Baoding 071003 China Machine Learning Center Faculty of Mathematics and Computer Science HeBei University Baoding 071002 China

ISBN: (纸本)9781424465262

Wireless Sensor Networks for Home Appliance Energy Management based on ZigBee technology is introduced in this paper. The aim of this research is to develop a real-time, low-cost, low power consumption and better reliable system for monitoring home appliance. Based on system analysis, hardware architecture design and underlying software development are presented. Experiment testing result demonstrates that this system is suitable for home appliance energy management and it makes electric scheduling and management more convenient and flexible. Moreover, it can be extended to a whole building or community. © 2010 IEEE.

关键词： Wireless sensor networks

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Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification 10

Object Bank: A High-Level Image Representation for Scene Cla...

引用

Annual Conference on Neural Information Processing Systems

作者： Li-Jia Li Hao Su Eric P. Xing Li Fei-Fei Computer Science Department Stanford University Machine Learning Department Carnegie Mellon University

ISBN: (纸本)9781617823800

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high level visual tasks, such low-level image representations are potentially not enough. In this paper, we propose a high-level image representation, called the Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on the Object Bank representation, superior performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. Sparsity algorithms make our representation more efficient and scalable for large scene datasets, and reveal semantically meaningful feature patterns.

关键词： image representation image patch image feature Semantics object recognition Scalability pixel

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The group Dantzig selector

The group Dantzig selector

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13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010

作者： Liu, Han Zhang, Jian Jiang, Xiaoye Liu, Jun Machine Learning Department Carnegie Mellon University United States Statistics Department Purdue University United States Computational Mathematics Stanford University United States Computer Science Department Arizona State University United States

We introduce a new method - the group Dantzig selector - for high dimensional sparse regression with group structure, which has a convincing theory about why utilizing the group structure can be beneficial. Under a group restricted isometry condition, we obtain a significantly improved nonasymptotic 2-norm bound over the basis pursuit or the Dantzig selector which ignores the group structure. To gain more insight, we also introduce a surprisingly simple and intuitive sparsity oracle condition to obtain a block 1-norm bound, which is easily accessible to a broad audience in machine learning community. Encouraging numerical results are also provided to support our theory.

关键词： learning systems

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Inference and communication in the game of Password 10

Inference and communication in the game of Password

引用

Annual Conference on Neural Information Processing Systems

作者： Yang Xu Charles Kemp Machine Learning Department School of Computer Science Department of Psychology Carnegie Mellon University

ISBN: (纸本)9781617823800

Communication between a speaker and hearer will be most efficient when both parties make accurate inferences about the other. We study inference and communication in a television game called Password, where speakers must convey secret words to hearers by providing one-word clues. Our working hypothesis is that human communication is relatively efficient, and we use game show data to examine three predictions. First, we predict that speakers and hearers are both considerate, and that both take the other's perspective into account. Second, we predict that speakers and hearers are calibrated, and that both make accurate assumptions about the strategy used by the other. Finally, we predict that speakers and hearers are collaborative, and that they tend to share the cognitive burden of communication equally. We find evidence in support of all three predictions, and demonstrate in addition that efficient communication tends to break down when speakers and hearers are placed under time pressure.

关键词： Loudspeakers Communication Research Passwords Personal Communication

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Moving Average crossovers for short-term equity investment with L-GEM based RBFNN

Moving Average crossovers for short-term equity investment w...

引用

International Conference on machine learning and Cybernetics

作者： Cai, Gao-Yang Ng, Wing W. Y. Chan, Patrick P. K. Firth, Michael Yeung, Daniel S. Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology 510006 Guangzhou China Department of Finance and Insurance Lingnan University Hong Kong Hong Kong

ISBN: (纸本)9781424465262

The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy. © 2010 IEEE.

关键词： Financial markets

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Pairwise interaction tensor factorization for personalized tag recommendation 10

Pairwise interaction tensor factorization for personalized t...

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3rd ACM International Conference on Web Search and data Mining, WSDM 2010

作者： Rendle, Steffen Schmidt-Thieme, Lars Department of Reasoning for Intelligence Institute of Scientific and Industrial Research Osaka University Osaka Japan Institute for Computer Science University of Hildesheim Hildesheim Germany Machine Learning Lab. University of Hildesheim Germany

ISBN: (纸本)9781605588896

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. Copyright 2010 ACM.

关键词： Recommender systems

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