OpenRaiser/Intern-Atlas huggingface dataset:
Fast Barcode Retrieval for Consensus Contouring
Venue: arXiv cs.CV 2017
Year: 2017

Marking tumors and organs is a challenging task suffering from both inter- and intra-observer variability. The literature quantifies observer variability by generating consensus among multiple experts when they mark the same image. Automatically building consensus contours to establish quality assurance for image segmentation is presently absent in the clinical practice. As the \emph{big data} becomes more and more available, techniques to access a large number of existing segments of multiple experts becomes possible. Fast algorithms are, hence, required to facilitate the search for similar cases. The present work puts forward a potential framework that tested with small datasets (both synthetic and real images) displays the reliability of finding similar images. In this paper, the idea of content-based barcodes is used to retrieve similar cases in order to build consensus contours in medical image segmentation. This approach may be regarded as an extension of the conventional atlas-based segmentation that generally works with rather small atlases due to required computational expenses. The fast segment-retrieval process via barcodes makes it possible to create and use large atlases, something that directly contributes to the quality of the consensus building. Because the accuracy of experts' contours must be measured, we first used 500 synthetic prostate images with their gold markers and delineations by 20 simulated users. The fast barcode-guided computed consensus delivered an average error of $8\%\!\pm\!5\%$ compared against the gold standard segments. Furthermore, we used magnetic resonance images of prostates from 15 patients delineated by 5 oncologists and selected the best delineations to serve as the gold-standard segments. The proposed barcode atlas achieved a Jaccard overlap of $87\%\!\pm\!9\%$ with the contours of the gold-standard segments.
Light Cascaded Convolutional Neural Networks for Accurate Player Detection
Venue: arXiv cs.CV 2017
Year: 2017

Vision based player detection is important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasting and automatic event classification. In this paper, we present a cascaded convolutional neural network (CNN) that satisfies all three of these requirements. Our method first trains a binary (player/non-player) classification network from labeled image patches. Then, our method efficiently applies the network to a whole image in testing. We conducted experiments on basketball and soccer games. Experimental results demonstrate that our method can accurately detect players under challenging conditions such as varying illumination, highly dynamic camera movements and motion blur. Comparing with conventional CNNs, our approach achieves state-of-the-art accuracy on both games with 1000x fewer parameters (i.e., it is light}.
Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations
Venue: arXiv cs.NA 2017
Year: 2017

We consider the problem of online subspace tracking of a partially observed high-dimensional data stream corrupted by noise, where we assume that the data lie in a low-dimensional linear subspace. This problem is cast as an online low-rank tensor completion problem. We propose a novel online tensor subspace tracking algorithm based on the CANDECOMP/PARAFAC (CP) decomposition, dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). The proposed algorithm especially addresses the case in which the subspace of interest is dynamically time-varying. To this end, we build up our proposed algorithm exploiting the recursive least squares (RLS), which is the second-order gradient algorithm. Numerical evaluations on synthetic datasets and real-world datasets such as communication network traffic, environmental data, and surveillance videos, show that the proposed OLSTEC algorithm outperforms state-of-the-art online algorithms in terms of the convergence rate per iteration.
Barrier Coverage with Non-uniform Lengths to Minimize Aggregate Movements
Venue: arXiv cs.DS 2017
Year: 2017

Given a line segment $I=[0,L]$, the so-called barrier, and a set of $n$ sensors with varying ranges positioned on the line containing $I$, the barrier coverage problem is to move the sensors so that they cover $I$, while minimising the total movement. In the case when all the sensors have the same radius the problem can be solved in $O(n \log n)$ time (Andrews and Wang, Algorithmica 2017). If the sensors have different radii the problem is known to be NP-hard to approximate within a constant factor (Czyzowicz et al., ADHOC-NOW 2009). We strengthen this result and prove that no polynomial time $ρ^{1-\varepsilon}$-approximation algorithm exists unless $P=NP$, where $ρ$ is the ratio between the largest radius and the smallest radius. Even when we restrict the number of sensors that are allowed to move by a parameter $k$, the problem turns out to be W[1]-hard. On the positive side we show that a $((2+\varepsilon)ρ+2/\varepsilon)$-approximation can be computed in $O(n^3/\varepsilon^2)$ time and we prove fixed-parameter tractability when parameterized by the total movement assuming all numbers in the input are integers.
The Quality of Equilibria for Set Packing Games
Venue: arXiv cs.GT 2017
Year: 2017

We introduce set packing games as an abstraction of situations in which $n$ selfish players select subsets of a finite set of indivisible items, and analyze the quality of several equilibria for this class of games. Assuming that players are able to approximately play equilibrium strategies, we show that the total quality of the resulting equilibrium solutions is only moderately suboptimal. Our results are tight bounds on the price of anarchy for three equilibrium concepts, namely Nash equilibria, subgame perfect equilibria, and an equilibrium concept that we refer to as $k$-collusion Nash equilibrium.
Non-approximability and Polylogarithmic Approximations of the Single-Sink Unsplittable and Confluent Dynamic Flow Problems
Venue: arXiv cs.DS 2017
Year: 2017

Dynamic Flows were introduced by Ford and Fulkerson in 1958 to model flows over time. They define edge capacities to be the total amount of flow that can enter an edge {\em in one time unit}. Each edge also has a length, representing the time needed to traverse it. Dynamic Flows have been used to model many problems including traffic congestion, hop-routing of packets and evacuation protocols in buildings. While the basic problem of moving the maximal amount of supplies from sources to sinks is polynomial time solvable, natural minor modifications can make it NP-hard. One such modification is that flows be confluent, i.e., all flows leaving a vertex must leave along the same edge. This corresponds to natural conditions in, e.g., evacuation planning and hop routing. We investigate the single-sink Confluent Quickest Flow problem. The input is a graph with edge capacities and lengths, sources with supplies and a sink. The problem is to find a confluent flow minimizing the time required to send supplies to the sink. Our main results include: a) Logarithmic Non-Approximability: Directed Confluent Quickest Flows cannot be approximated in polynomial time with an $O(\log n)$ approximation factor, unless $P=NP$. b) Polylogarithmic Bicriteria Approximations: Polynomial time $(O(\log^8 n), O(\log^2 κ))$ bicritera approximation algorithms for the Confluent Quickest Flow problem where $κ$ is the number of sinks, in both directed and undirected graphs. Corresponding results are also developed for the Confluent Maximum Flow over timeproblem. The techniques developed also improve recent approximation algorithms for static confluent flows.
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
Venue: arXiv cs.CL 2017
Year: 2017

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.
Optimisation of photometric stereo methods by non-convex variational minimisation
Venue: arXiv cs.CV 2017
Year: 2017

Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued. Among the various techniques available, PS is distinguished by the assumption that the underlying input images are taken from the same point of view but under different lighting conditions. The most common techniques provide the shape information in terms of surface normals. In this work, we instead propose to minimise a much more natural objective function, namely the reprojection error in terms of depth. Minimising the resulting non-trivial variational model for PS allows to recover the depth of the photographed scene directly. As a solving strategy, we follow an approach based on a recently published optimisation scheme for non-convex and non-smooth cost functions. The main contributions of our paper are of theoretical nature. A technical novelty in our framework is the usage of matrix differential calculus. We supplement our approach by a detailed convergence analysis of the resulting optimisation algorithm and discuss possibilities to ease the computational complexity. At hand of an experimental evaluation we discuss important properties of the method. Overall, our strategy achieves more accurate results than competing approaches. The experiments also highlights some practical aspects of the underlying optimisation algorithm that may be of interest in a more general context.
Improving image generative models with human interactions
Venue: arXiv cs.CV 2017
Year: 2017

GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train these generative models to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images. In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction. Here, we develop a system for efficiently improving a GAN to target an objective involving human interaction, specifically generating images that increase rates of positive user interactions. To improve the generative model, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model. We show that this system is successful at improving positive interaction rates, at least on simulated data, and characterize some of the factors that affect its performance.
A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT)
Venue: arXiv cs.CY 2017
Year: 2017

The current prominence and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT) are extensively reviewed and a summary survey report is presented. The analysis clearly distinguishes between IoT and IoE which are wrongly considered to be the same by many people. Upon examining the current advancement in the fields of IoT, IoE and IoNT, the paper presents scenarios for the possible future expansion of their applications.
Optimal Design of Hybrid AC/DC Microgrids
Venue: arXiv cs.SY 2017
Year: 2017

DC loads (such as computers, data centres, electric vehicle chargers, and LED lamps) and dc distributed energy resources (such as fuel cells, solar photovoltaics, and energy storages) are rapidly growing in electric power systems, so dc systems are being introduced as emerging practical investment solutions compared to traditional ac options. DC microgrids offer several advantages such as the elimination of need for synchronizing generators and easier supply of dc loads. Hybrid microgrids can benefit from the advantages of both ac and dc microgrid types. Moreover, there would be a huge reduction in the number of required power converters which would enhance the microgrid efficiency and reduce investment and operation costs. This paper introduces hybrid ac/dc microgrid as a viable solution compared to individual ac or dc microgrids and focuses on its planning. The objective of the hybrid microgrid planning is to minimize the microgrid total cost, including investment cost of distributed energy resources (DERs) and converters, operation cost of DERs, the cost of energy exchange with the utility grid, and the cost of unserved energy during the planning horizon. The economic viability of the microgrid planning is investigated in this paper and it is shown how the optimal DER generation mix, the type of feeders, as well as the point of connection of DERs to feeders can be determined by updating the traditional ac microgrid planning model. Numerical simulations on a test microgrid exhibit the merits of the proposed model.
PCANet-II: When PCANet Meets the Second Order Pooling
Venue: arXiv cs.CV 2017
Year: 2017

PCANet, as one noticeable shallow network, employs the histogram representation for feature pooling. However, there are three main problems about this kind of pooling method. First, the histogram-based pooling method binarizes the feature maps and leads to inevitable discriminative information loss. Second, it is difficult to effectively combine other visual cues into a compact representation, because the simple concatenation of various visual cues leads to feature representation inefficiency. Third, the dimensionality of histogram-based output grows exponentially with the number of feature maps used. In order to overcome these problems, we propose a novel shallow network model, named as PCANet-II. Compared with the histogram-based output, the second order pooling not only provides more discriminative information by preserving both the magnitude and sign of convolutional responses, but also dramatically reduces the size of output features. Thus we combine the second order statistical pooling method with the shallow network, i.e., PCANet. Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling. So we introduce the binary feature difference encoding scheme into our PCANet-II to further improve robustness. Experiments demonstrate the effectiveness and robustness of our proposed PCANet-II method.
Confirmation detection in human-agent interaction using non-lexical speech cues
Venue: arXiv cs.HC 2017
Year: 2017

Even if only the acoustic channel is considered, human communication is highly multi-modal. Non-lexical cues provide a variety of information such as emotion or agreement. The ability to process such cues is highly relevant for spoken dialog systems, especially in assistance systems. In this paper we focus on the recognition of non-lexical confirmations such as "mhm", as they enhance the system's ability to accurately interpret human intent in natural communication. The architecture uses a Support Vector Machine to detect confirmations based on acoustic features. In a systematic comparison, several feature sets were evaluated for their performance on a corpus of human-agent interaction in a setting with naive users including elderly and cognitively impaired people. Our results show that using stacked formants as features yield an accuracy of 84% outperforming regular formants and MFCC or pitch based features for online classification.
Diagramming the Class Diagram: Toward a Unified Modeling Methodology
Venue: arXiv cs.SE 2017
Year: 2017

The object-oriented class is, in general, the most utilized element in programming and modeling. It is employed throughout the software development process, from early domain analysis phases to later maintenance phases. A class diagram typically uses elements of graph theory, e.g., boxes, ovals, lines. Many researchers have examined the class diagram layout from different perspectives, including visibility, juxtaposability, and aesthetics. While software systems can be incredibly complex, class diagrams represent a very broad picture of the system as a whole. The key to understanding of such complexity is use of tools such as diagrams at various levels of representation. This paper develops a more elaborate diagrammatic description of the class diagram that includes flows of attributes, thus providing a basic representation for specifying behavior and control instead of merely listing methods.
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction
Venue: arXiv cs.CL 2017
Year: 2017

Vector-space models, from word embeddings to neural network parsers, have many advantages for NLP. But how to generalise from fixed-length word vectors to a vector space for arbitrary linguistic structures is still unclear. In this paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A bag-of-vector space is the minimal nonparametric extension of a vector space, allowing the representation to grow with the size of the graph, but not tying the representation to any specific tree or graph structure. We propose efficient training and inference algorithms based on tensor factorisation for embedding arbitrary graphs in a bag-of-vector space. We demonstrate the usefulness of this representation by training bag-of-vector embeddings of dependency graphs and evaluating them on unsupervised semantic induction for the Semantic Textual Similarity and Natural Language Inference tasks.
Two-way Two-tape Automata
Venue: arXiv cs.FL 2017
Year: 2017

In this article we consider two-way two-tape (alternating) automata accepting pairs of words and we study some closure properties of this model. Our main result is that such alternating automata are not closed under complementation for non-unary alphabets. This improves a similar result of Kari and Moore for picture languages. We also show that these deterministic, non-deterministic and alternating automata are not closed under composition.
Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF
Venue: arXiv cs.CV 2017
Year: 2017

Event learning is one of the most important problems in AI. However, notwithstanding significant research efforts, it is still a very complex task, especially when the events involve the interaction of humans or agents with other objects, as it requires modeling human kinematics and object movements. This study proposes a methodology for learning complex human-object interaction (HOI) events, involving the recording, annotation and classification of event interactions. For annotation, we allow multiple interpretations of a motion capture by slicing over its temporal span, for classification, we use Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field (CRF) for constraints of outputs. Using a setup involving captures of human-object interaction as three dimensional inputs, we argue that this approach could be used for event types involving complex spatio-temporal dynamics.
Image Dehazing using Bilinear Composition Loss Function
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we introduce a bilinear composition loss function to address the problem of image dehazing. Previous methods in image dehazing use a two-stage approach which first estimate the transmission map followed by clear image estimation. The drawback of a two-stage method is that it tends to boost local image artifacts such as noise, aliasing and blocking. This is especially the case for heavy haze images captured with a low quality device. Our method is based on convolutional neural networks. Unique in our method is the bilinear composition loss function which directly model the correlations between transmission map, clear image, and atmospheric light. This allows errors to be back-propagated to each sub-network concurrently, while maintaining the composition constraint to avoid overfitting of each sub-network. We evaluate the effectiveness of our proposed method using both synthetic and real world examples. Extensive experiments show that our method outperfoms state-of-the-art methods especially for haze images with severe noise level and compressions.
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
Venue: arXiv cs.IR 2017
Year: 2017

Our task is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate the quality of summaries without human-generated benchmarks, we define a measure called WESM based on word-embedding using Word Mover's Distance. We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.
A Novel Deformation Method for Higher Order Mesh Generation
Venue: arXiv cs.CG 2017
Year: 2017

The development of higher order finite elements methods has become an active research area. The deformation method for mesh generation has achieved a prescribed positive Jacobian determinant constraint and it has been a useful method for mesh adaptation. In this work, the deformation method is combined with local refinement to generate higher order meshes. 2D and 3D numerical examples of element order p=3 are shown to demonstrate the effectiveness of our new algorithm.