May 7, 2019

3045 words 15 mins read

Paper Group ANR 19

Design Mining Microbial Fuel Cell Cascades. Why Artificial Intelligence Needs a Task Theory — And What It Might Look Like. Minimax-optimal semi-supervised regression on unknown manifolds. Generating Chinese Classical Poems with RNN Encoder-Decoder. Higher Order Mutual Information Approximation for Feature Selection. Automatic Environmental Sound …

Design Mining Microbial Fuel Cell Cascades


Title	Design Mining Microbial Fuel Cell Cascades
Authors	Richard J. Preen, Jiseon You, Larry Bull, Ioannis A. Ieropoulos
Abstract	Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging multiple MFC units into physical stacks in a cascade with feedstock flowing sequentially between units. In this paper, we investigate the use of computational intelligence to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e. without simulation. Conductive structures are 3-D printed and inserted into the anodic chamber of each MFC unit, augmenting a carbon fibre veil anode and affecting the hydrodynamics, including the feedstock volume and hydraulic retention time, as well as providing unique habitats for microbial colonisation. We show that it is possible to use design mining to identify new conductive inserts that increase both the cascade power output and power density.
Tasks
Published	2016-10-18
URL	http://arxiv.org/abs/1610.05716v2
PDF	http://arxiv.org/pdf/1610.05716v2.pdf
PWC	https://paperswithcode.com/paper/design-mining-microbial-fuel-cell-cascades
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Why Artificial Intelligence Needs a Task Theory — And What It Might Look Like


Title	Why Artificial Intelligence Needs a Task Theory — And What It Might Look Like
Authors	Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink
Abstract	The concept of “task” is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane’s performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial general intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A task theory would enable addressing tasks at the class level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
Tasks	Board Games, Question Answering
Published	2016-04-15
URL	http://arxiv.org/abs/1604.04660v2
PDF	http://arxiv.org/pdf/1604.04660v2.pdf
PWC	https://paperswithcode.com/paper/why-artificial-intelligence-needs-a-task
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Minimax-optimal semi-supervised regression on unknown manifolds


Title	Minimax-optimal semi-supervised regression on unknown manifolds
Authors	Amit Moscovich, Ariel Jaffe, Boaz Nadler
Abstract	We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the labeled and unlabeled instances; and (ii) apply a k nearest neighbor regressor based on these distance estimates. We prove that given sufficiently many unlabeled points, this simple method of geodesic kNN regression achieves the optimal finite-sample minimax bound on the mean squared error, as if the manifold were known. Furthermore, we show how this approach can be efficiently implemented, requiring only O(k N log N) operations to estimate the regression function at all N labeled and unlabeled points. We illustrate this approach on two datasets with a manifold structure: indoor localization using WiFi fingerprints and facial pose estimation. In both cases, geodesic kNN is more accurate and much faster than the popular Laplacian eigenvector regressor.
Tasks	Pose Estimation
Published	2016-11-07
URL	http://arxiv.org/abs/1611.02221v2
PDF	http://arxiv.org/pdf/1611.02221v2.pdf
PWC	https://paperswithcode.com/paper/minimax-optimal-semi-supervised-regression-on
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Generating Chinese Classical Poems with RNN Encoder-Decoder


Title	Generating Chinese Classical Poems with RNN Encoder-Decoder
Authors	Xiaoyuan Yi, Ruoyu Li, Maosong Sun
Abstract	We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input. Our system can jointly learn semantic meaning within a single line, semantic relevance among lines in a poem, and the use of structural, rhythmical and tonal patterns, without utilizing any constraint templates. Experimental results show that our system outperforms other competitive systems. We also find that the attention mechanism can capture the word associations in Chinese classical poetry and inverting target lines in training can improve performance.
Tasks
Published	2016-04-06
URL	http://arxiv.org/abs/1604.01537v1
PDF	http://arxiv.org/pdf/1604.01537v1.pdf
PWC	https://paperswithcode.com/paper/generating-chinese-classical-poems-with-rnn
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Higher Order Mutual Information Approximation for Feature Selection


Title	Higher Order Mutual Information Approximation for Feature Selection
Authors	Jilin Wu, Soumyajit Gupta, Chandrajit Bajaj
Abstract	Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual Information (MI) between subsets of features and class labels. The prior methods use a lower order approximation, by treating the joint entropy as a summation of several single variable entropies. This leads to locally optimal selections and misses multi-way feature combinations. We present a higher order MI based approximation technique called Higher Order Feature Selection (HOFS). Instead of producing a single list of features, our method produces a ranked collection of feature subsets that maximizes MI, giving better comprehension (feature ranking) as to which features work best together when selected, due to their underlying interdependent structure. Our experiments demonstrate that the proposed method performs better than existing feature selection approaches while keeping similar running times and computational complexity.
Tasks	Feature Selection
Published	2016-12-02
URL	http://arxiv.org/abs/1612.00554v1
PDF	http://arxiv.org/pdf/1612.00554v1.pdf
PWC	https://paperswithcode.com/paper/higher-order-mutual-information-approximation
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Automatic Environmental Sound Recognition: Performance versus Computational Cost


Title	Automatic Environmental Sound Recognition: Performance versus Computational Cost
Authors	Siddharth Sigtia, Adam M. Stark, Sacha Krstulovic, Mark D. Plumbley
Abstract	In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost.
Tasks
Published	2016-07-15
URL	http://arxiv.org/abs/1607.04589v1
PDF	http://arxiv.org/pdf/1607.04589v1.pdf
PWC	https://paperswithcode.com/paper/automatic-environmental-sound-recognition
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A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras


Title	A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras
Authors	Jules Hedges, Mehrnoosh Sadrzadeh
Abstract	Categorical compositional distributional semantics is a model of natural language; it combines the statistical vector space models of words with the compositional models of grammar. We formalise in this model the generalised quantifier theory of natural language, due to Barwise and Cooper. The underlying setting is a compact closed category with bialgebras. We start from a generative grammar formalisation and develop an abstract categorical compositional semantics for it, then instantiate the abstract setting to sets and relations and to finite dimensional vector spaces and linear maps. We prove the equivalence of the relational instantiation to the truth theoretic semantics of generalised quantifiers. The vector space instantiation formalises the statistical usages of words and enables us to, for the first time, reason about quantified phrases and sentences compositionally in distributional semantics.
Tasks
Published	2016-02-04
URL	http://arxiv.org/abs/1602.01635v2
PDF	http://arxiv.org/pdf/1602.01635v2.pdf
PWC	https://paperswithcode.com/paper/a-generalised-quantifier-theory-of-natural
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Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis


Title	Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Authors	Jimei Yang, Scott Reed, Ming-Hsuan Yang, Honglak Lee
Abstract	An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.
Tasks
Published	2016-01-05
URL	http://arxiv.org/abs/1601.00706v1
PDF	http://arxiv.org/pdf/1601.00706v1.pdf
PWC	https://paperswithcode.com/paper/weakly-supervised-disentangling-with
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Light Field Stitching for Extended Synthetic Aperture


Title	Light Field Stitching for Extended Synthetic Aperture
Authors	M. Umair Mukati, Bahadir K. Gunturk
Abstract	Through capturing spatial and angular radiance distribution, light field cameras introduce new capabilities that are not possible with conventional cameras. So far in the light field imaging literature, the focus has been on the theory and applications of single light field capture. By combining multiple light fields, it is possible to obtain new capabilities and enhancements, and even exceed physical limitations, such as spatial resolution and aperture size of the imaging device. In this paper, we present an algorithm to register and stitch multiple light fields. We utilize the regularity of the spatial and angular sampling in light field data, and extend some techniques developed for stereo vision systems to light field data. Such an extension is not straightforward for a micro-lens array (MLA) based light field camera due to extremely small baseline and low spatial resolution. By merging multiple light fields captured by an MLA based camera, we obtain larger synthetic aperture, which results in improvements in light field capabilities, such as increased depth estimation range/accuracy and wider perspective shift range.
Tasks	Depth Estimation
Published	2016-11-15
URL	http://arxiv.org/abs/1611.05003v1
PDF	http://arxiv.org/pdf/1611.05003v1.pdf
PWC	https://paperswithcode.com/paper/light-field-stitching-for-extended-synthetic
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Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements


Title	Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements
Authors	Rohan Kar, Rishin Haldar
Abstract	Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with internet. It also presents various opportunities for intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Literature surveys have looked into various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are being adopted in greater numbers due to major strides in development of platforms and frameworks. The novelty of this paper lies in the specific integration of Chatbots in the IoT scenario. We analyzed the shortcomings of existing IoT systems and put forward ways to tackle them by incorporating chatbots. A general architecture is proposed for implementing such a system, as well as platforms and frameworks, both commercial and open source, which allow for implementation of such systems. Identification of the newer challenges and possible future directions with this new integration, have also been addressed.
Tasks
Published	2016-11-11
URL	http://arxiv.org/abs/1611.03799v1
PDF	http://arxiv.org/pdf/1611.03799v1.pdf
PWC	https://paperswithcode.com/paper/applying-chatbots-to-the-internet-of-things
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Multi-Band Image Fusion Based on Spectral Unmixing


Title	Multi-Band Image Fusion Based on Spectral Unmixing
Authors	Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret, Marcus Chen, Simon Godsill
Abstract	This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is then formulated as maximizing the joint posterior distribution with respect to the endmember signatures and abundance maps, This optimization problem is attacked with an alternating optimization strategy. The two resulting sub-problems are convex and are solved efficiently using the alternating direction method of multipliers. Experiments are conducted for both synthetic and semi-real data. Simulation results show that the proposed unmixing based fusion scheme improves both the abundance and endmember estimation comparing with the state-of-the-art joint fusion and unmixing algorithms.
Tasks
Published	2016-03-29
URL	http://arxiv.org/abs/1603.08720v1
PDF	http://arxiv.org/pdf/1603.08720v1.pdf
PWC	https://paperswithcode.com/paper/multi-band-image-fusion-based-on-spectral
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Multi-Task Learning with Labeled and Unlabeled Tasks


Title	Multi-Task Learning with Labeled and Unlabeled Tasks
Authors	Anastasia Pentina, Christoph H. Lampert
Abstract	In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided. Consequently, to solve all tasks, information must be transferred between tasks with labels and tasks without labels. Focusing on an instance-based transfer method we analyze two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. We state and prove a generalization bound that covers both scenarios and derive from it an algorithm for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. We also illustrate the effectiveness of the algorithm by experiments on synthetic and real data.
Tasks	Multi-Task Learning
Published	2016-02-21
URL	http://arxiv.org/abs/1602.06518v4
PDF	http://arxiv.org/pdf/1602.06518v4.pdf
PWC	https://paperswithcode.com/paper/multi-task-learning-with-labeled-and
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Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off


Title	Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off
Authors	Jaakko Peltonen, Ziyuan Lin
Abstract	Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges (“local bundles”), or also between the weakly bundled edges occurring globally over the graph (“global bundles”); we allow a user-set global-local tradeoff. We call the technique “peacock bundles”. Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.
Tasks	Dimensionality Reduction
Published	2016-09-02
URL	http://arxiv.org/abs/1609.00719v1
PDF	http://arxiv.org/pdf/1609.00719v1.pdf
PWC	https://paperswithcode.com/paper/peacock-bundles-bundle-coloring-for-graphs
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Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence


Title	Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
Authors	Makoto Naruse, Martin Berthel, Aurélien Drezet, Serge Huant, Hirokazu Hori, Song-Ju Kim
Abstract	Understanding and using natural processes for intelligent functionalities, referred to as natural intelligence, has recently attracted interest from a variety of fields, including post-silicon computing for artificial intelligence and decision making in the behavioural sciences. In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making. In this study, we propose and confirm a hierarchical architecture for single-photon-based reinforcement learning and decision making that verifies the scalability of the principle. Specifically, the four-armed bandit problem is solved given zero prior knowledge in a two-layer hierarchical architecture, where polarization is autonomously adapted in order to effect adequate decision making using single-photon measurements. In the hierarchical structure, the notion of layer-dependent decisions emerges. The optimal solutions in the coarse layer and in the fine layer, however, conflict with each other in some contradictive problems. We show that while what we call a tournament strategy resolves such contradictions, the probabilistic nature of single photons allows for the direct location of the optimal solution even for contradictive problems, hence manifesting the exploration ability of single photons. This study provides insights into photon intelligence in hierarchical architectures for future artificial intelligence as well as the potential of natural processes for intelligent functionalities.
Tasks	Decision Making
Published	2016-09-01
URL	http://arxiv.org/abs/1609.00686v1
PDF	http://arxiv.org/pdf/1609.00686v1.pdf
PWC	https://paperswithcode.com/paper/single-photon-in-hierarchical-architecture
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Title	Blind image separation based on exponentiated transmuted Weibull distribution
Authors	A. M. Adam, R. M. Farouk, M. E. Abd El-aziz
Abstract	In recent years the processing of blind image separation has been investigated. As a result, a number of feature extraction algorithms for direct application of such image structures have been developed. For example, separation of mixed fingerprints found in any crime scene, in which a mixture of two or more fingerprints may be obtained, for identification, we have to separate them. In this paper, we have proposed a new technique for separating a multiple mixed images based on exponentiated transmuted Weibull distribution. To adaptively estimate the parameters of such score functions, an efficient method based on maximum likelihood and genetic algorithm will be used. We also calculate the accuracy of this proposed distribution and compare the algorithmic performance using the efficient approach with other previous generalized distributions. We find from the numerical results that the proposed distribution has flexibility and an efficient result
Tasks
Published	2016-05-11
URL	http://arxiv.org/abs/1605.03624v1
PDF	http://arxiv.org/pdf/1605.03624v1.pdf
PWC	https://paperswithcode.com/paper/blind-image-separation-based-on-exponentiated
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