Growing Neural Gas with Different Topologies for 3D Space Perception

Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

New Foundation in the Sciences: Physics without sweeping infinities under the rug

It is widely known among the Frontiers of physics, that “sweeping under the rug” practice has been quite the norm rather than exception. In other words, the leading paradigms have strong tendency to be hailed as the only game in town. For example, renormalization group theory was hailed as cure in order to solve infinity problem in QED theory. For instance, a quote from Richard Feynman goes as follows: “What the three Nobel Prize winners did, in the words of Feynman, was to get rid of the infinities in the calculations. The infinities are still there, but now they can be skirted around . . . We have designed a method for sweeping them under the rug. [1] And Paul Dirac himself also wrote with similar tune: “Hence most physicists are very satisfied with the situation. They say: Quantum electrodynamics is a good theory, and we do not have to worry about it any more. I must say that I am very dissatisfied with the situation, because this so-called good theory does involve neglecting infinities which appear in its equations, neglecting them in an arbitrary way. This is just not sensible mathematics. Sensible mathematics involves neglecting a quantity when it turns out to be small—not neglecting it just because it is infinitely great and you do not want it!”[2] Similarly, dark matter and dark energy were elevated as plausible way to solve the crisis in prevalent Big Bang cosmology. That is why we choose a theme here: New Foundations in the Sciences, in order to emphasize the necessity to introduce a new set of approaches in the Sciences, be it Physics, Cosmology, Consciousness etc

Strong continuity of life and mind: the free energy framework, predictive processing and ecological psychology

Located at the intersection of philosophy of cognitive science and philosophy of biology, this thesis aims to provide a novel approach to understanding the strong continuity between life and mind. This thesis applies the Free Energy Framework, predictive processing and the conceptual apparatus from ecological psychology to reveal different manners in which the organizational processes and principles underlying life have been enriched so as to result in cognitive processes. By using these anticipatory cognitive frameworks this thesis unveils different forms of cognition at work in surprising places and considers how such expressions of cognition are ultimately driven by various forms of environmental complexity. Importing the concepts of affordances, environmental information and perceptual medium from ecological psychology into predictive processing and the Free Energy Framework, an empirically grounded account of cognition as an anticipatory process that allows living systems to adapt to various degrees of uncertainty in their environments at distinct and yet overlapping timescales is argued for. In doing so, this thesis attempts to identify both the explanatory limits of ecological coupling accounts of perception and action, and the possible environmental conditions under which the predictive brain evolved from its decentralized non-neural predecessors as a solution to uncertainty. In contributing to a novel approach to constraining the mind, the various concepts deployed in both philosophy and cognitive science are sharpened, furthering the current debate on what cognition is and how it is related to life

Efficient Learning Machines

Computer scienc

ニューロコンピューティングに基づく時系列データのための構造化学習

近年，在宅高齢者の健康管理のためのライフログ計測など，大規模なデータから特徴や知識を抽出するための研究としてデータマイニングが注目されている．データマイニングでは，特徴抽出のために各種設定パラメタを調整しながら，適応的に状態を推定する方法論が必要とされる．つまり，状態推定の精度を向上させるためには知識表現に必要となる特徴量を更新しなければならず，特徴量を評価するためには状態推定の結果が必要となる．特徴抽出や状態推定をおこなうための方法論の一つとしてニューロコンピューティングがある．ニューロコンピューティングでは，各ニューロンの発火の閾値やニューロン間の結合係数を調整することで，目標とする入出力関係を学習することができ，様々な種類のネットワーク構造や学習手法が提案されている．しかしながら，入出力関係が時々刻々と変化するデータに対しては，学習が困難な場合が多い．できるだけ汎化性を維持しつつ，適応性を実現するための方法論として構造化学習という概念がある．構造化学習では，学習構造が特徴抽出や状態推定などの機能をもった複数のモジュールから構成され，それぞれが相互依存的な学習をおこなうことができる．したがって，本研究では，「ニューロコンピューティングに基づく構造化学習を提案し，特徴抽出と状態推定を相互依存的におこなうための方法論を確立すること」を目的とする．具体的には，対象とする時系列データの特徴に合わせて，スパイキングニューラルネットワーク（SNN），ファジィ・スパイキングニューラルネットワーク（FSNN），Growing Neural Gas（GNG）などを拡張することにより，新しい構造化学習の方法論を提案する．また，本研究では，時々刻々と変化する人の状態，動作や行動の計測に焦点をあてる．そして，利用可能な計測データの性質と学習手法の観点から，教師あり学習，半教師あり学習，教師なし学習の各方法論において，特徴抽出や学習構造における問題点を明確にし，3つの構造化学習手法を提案する．さらに，本提案手法を適用し，人の状態や動作，行動を対象とした計測実験をおこなう．実験結果より，本提案手法を適用することで，ニューラルネットワークの構造を変化させながら，ニューロン間の結合係数を調整することにより，特徴抽出と状態推定を相互依存的かつ同時に実現できることを示す．以下，本論文の概要を述べる．本論文は5章から構成されている．第1章では，前述の背景について詳細に述べ，研究の目的を明確にした．第2章では，時々刻々と変化する時系列データに対する情報処理を実現するための方法論として，ニューロコンピューティング，ファジィコンピューティング，進化計算に基づく知能化技術の方法論について述べ，従来手法の長所や短所を明確にした．第3章では，時系列データから特徴抽出と状態推定を相互依存的におこなうために，ニューロコンピューティングに基づく構造化学習の方法論を提案した．まず，ニューロコンピューティングの基本的な機能や特徴，応用における諸問題などについて述べた．次に，本提案手法である構造化学習の方法論として，（1）教師あり学習，（2）半教師あり学習，（3）教師なし学習への適用の観点から定式化をおこなった．まず，（1）教師あり学習では，閾値を用いて入力値を分類し，教師データとの誤差に基づき閾値を調整する適応型FSNNを提案した．次に，（2）半教師あり学習では，時系列データから特徴抽出としてクラスタリングをおこない，クラスタ間の遷移関係を学習することで，予測的に状態推定が可能な階層型SNN を提案した．最後に，（3）教師なし学習では，GNG を用いて時系列データを写像し，学習されたノード間の時空間的な相関関係を特徴量として抽出する自己増殖型ニューラルネットワーク（GNN）を提案した．第4章では，人の状態や動作，行動の計測を対象とした実験をおこない，ニューロコンピューティングに基づく構造化学習の検証をおこなった．まず，（1）教師あり学習の有効性を示すため，ベッド上での状態推定実験をおこなった．実験では，適応型FSNNを適用し，閾値調整の有効性を示した．次に，（2）半教師あり学習の有効性を示すため，生活空間内における行動推定実験をおこなった．実験では，階層型SNNを適用し，予測に基づく行動推定の有用性を示した．さらに，（3）教師なし学習の有効性を示すため，リハビリテーションにおける患者の特徴抽出実験をおこなった．実験では，健常者と半側空間無視患者を想定した比較実験をおこない，GNNを適用することで視線運動および上肢運動の軌道から特徴量および相関関係の抽出ができることを示した．第5章では，本論文の結論として結果を総括するとともに提案手法について今後取り組むべき課題と研究の展望について述べた．工学的な観点からまとめると，本研究では，ニューロコンピューティングに基づく構造化学習を適用することで，時々刻々と入出力関係が変化する時系列データに対し，汎化性を維持しつつ，適応性を実現できることを示した．首都大学東京, 2014-03-25, 博士(工学), 甲第442号首都大学東

SOCNET 2018 - Proceedings of the “Second International Workshop on Modeling, Analysis, and Management of Social Networks and Their Applications”

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.Die Modellierung, Analyse, Steuerung und das Management komplexer sozialer Netzwerke repräsentiert einen bedeutsamen Bereich interdisziplinärer Forschung in einer modernen digitalisierten Welt. Im letzten Jahrzehnt haben soziale Netzwerke wichtige Online Anwendungen hervorgebracht, die auf einer modernen Internet-Infrastruktur ablaufen und als eine Hauptquelle des rasant anwachsenden Internetverkehrs identifiziert wurden. Der zweite internationale Workshop "Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) wurde am 28. Februar 2018 an der Friedrich-Alexander-Universität Erlangen-Nürnberg abgehalten und stellte Forschungsergebnisse zu sozialen Netzwerken in einer modernen Informationsgesellschaft vor. Die SOCNET 2018 Proceedings stellen die Themen eines Tutoriums "Network Analysis in Python" heraus, präsentieren einen eingeladenen Beitrag "From the Age of Emperors to the Age of Empathy" und fassen die Ergebnisse von acht begutachteten wissenschaftlichen Beiträgen zusammen. Die abgedeckten Themen reichen von theoretisch ausgerichteten Studien zur Strukturanalyse thematischer Netzwerke, der Modellierung von Gruppendynamik sowie der Netzwerkanalyse von Rettungseinsätzen bis zu den Anwendungsbereichen sozialer Netzwerke, z.B. der Nutzung sozialer Medien in Organisationen sowie der Wirkungsanalyse sozialer Netzwerkanwendungen in modernen Informationsgesellschaften. Die SOCNET 2018 Proceedings sollen die Leser zu neuen Forschungen im Bereich der Messung, Modellierung und Analyse sozialer Netzwerke anregen und sie zur Entwicklung neuer sozialer Netzwerkapplikationen der nächsten Generation auffordern