OpenRaiser/Intern-Atlas huggingface dataset:
Learning under Distributed Weak Supervision
Venue: arXiv cs.CV 2016
Year: 2016

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research.
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
Venue: arXiv cs.NE 2016
Year: 2016

The final version of this paper has been published in IEEEXplore available at http://ieeexplore.ieee.org/document/7727213. Please cite this paper as: Amirhossein Tavanaei, Timothee Masquelier, and Anthony Maida, Acquisition of visual features through probabilistic spike-timing-dependent plasticity. IEEE International Joint Conference on Neural Networks. pp. 307-314, IJCNN 2016. This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational Biology, 3(2), 247-257]. The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery. The discovered features where interpretable and could effectively be used to perform rapid binary decisions in a classifier. In order to study the robustness of the previous results, the present research examines the effects of modifying some of the components of the original model. For improved biological realism, we replace the original non-leaky integrate-and-fire neurons with Izhikevich-like neurons. We also replace the original STDP rule with a novel rule that has a probabilistic interpretation. The probabilistic STDP slightly but significantly improves the performance for both types of model neurons. Use of the Izhikevich-like neuron was not found to improve performance although performance was still comparable to the IF neuron. This shows that the model is robust enough to handle more biologically realistic neurons. We also conclude that the underlying reasons for stable performance in the model are preserved despite the overt changes to the explicit components of the model.
Tripartite Unions
Venue: arXiv cs.LO 2016
Year: 2016

This note provides conditions under which the union of three well-founded binary relations is also well-founded.
Axodraw Version 2
Venue: arXiv cs.OH 2016
Year: 2016

We present version two of the Latex graphical style file Axodraw. It has a number of new drawing primitives and many extra options, and it can now work with \program{pdflatex} to directly produce output in PDF file format (but with the aid of an auxiliary program).
Distributed stochastic optimization via matrix exponential learning
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications. The proposed algorithm relies on the method of matrix exponential learning (MXL) and only requires locally computable gradient observations that are possibly imperfect and/or obsolete. To analyze it, we introduce the notion of a stable Nash equilibrium and we show that the algorithm is globally convergent to such equilibria - or locally convergent when an equilibrium is only locally stable. We also derive an explicit linear bound for the algorithm's convergence speed, which remains valid under measurement errors and uncertainty of arbitrarily high variance. To validate our theoretical analysis, we test the algorithm in realistic multi-carrier/multiple-antenna wireless scenarios where several users seek to maximize their energy efficiency. Our results show that learning allows users to attain a net increase between 100% and 500% in energy efficiency, even under very high uncertainty.
Learning Stylometric Representations for Authorship Analysis
Venue: arXiv cs.CL 2016
Year: 2016

Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations.
Grid-like structure is optimal for path integration
Venue: arXiv cs.NE 2016
Year: 2016

Grid cells in medial entorhinal cortex are believed to play a key role in path integration. However, the relation between path integration and the grid-like arrangement of their firing field remains unclear. We provide theoretical evidence that grid-like structure and path integration are closely related. In one dimension, the grid-like structure provides the optimal solution for path integration assuming that the noise correlation structure is Gaussian. In two dimensions, assuming that the noise is Gaussian, rectangular grid-like structure is the optimal solution provided that 1- both noise correlation and receptive field structures of the neurons can be multiplicatively decomposed into orthogonal components and 2- the eigenvalues of the decomposed correlation matrices decrease faster than the square of the frequency of the corresponding eigenvectors. We will also address the decoding mechanism and show that the problem of decoding reduces to the problem of extracting task relevant information in the presence of task irrelevant information. Change-based Population Coding provides the optimal solution for this problem.
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Venue: arXiv cs.LG 2016
Year: 2016

We study a general version of the adversarial online learning problem. We are given a decision set $\mathcal{X}$ in a reflexive Banach space $X$ and a sequence of reward vectors in the dual space of $X$. At each iteration, we choose an action from $\mathcal{X}$, based on the observed sequence of previous rewards. Our goal is to minimize regret, defined as the gap between the realized reward and the reward of the best fixed action in hindsight. Using results from infinite dimensional convex analysis, we generalize the method of Dual Averaging (or Follow the Regularized Leader) to our setting and obtain general upper bounds on the worst-case regret that subsume a wide range of results from the literature. Under the assumption of uniformly continuous rewards, we obtain explicit anytime regret bounds in a setting where the decision set is the set of probability distributions on a compact metric space $S$ whose Radon-Nikodym derivatives are elements of $L^p(S)$ for some $p > 1$. Importantly, we make no convexity assumptions on either the set $S$ or the reward functions. We also prove a general lower bound on the worst-case regret for any online algorithm. We then apply these results to the problem of learning in repeated continuous two-player zero-sum games, in which players' strategy sets are compact metric spaces. In doing so, we first prove that if both players play a Hannan-consistent strategy, then with probability 1 the empirical distributions of play weakly converge to the set of Nash equilibria of the game. We then show that, under mild assumptions, Dual Averaging on the (infinite-dimensional) space of probability distributions indeed achieves Hannan-consistency. Finally, we illustrate our results through numerical examples.
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a model for end-to-end learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system developer of much of the manual feature engineering of dialog state. In addition, the developer can provide software that expresses business rules and provides access to programmatic APIs, enabling the LSTM to take actions in the real world on behalf of the user. The LSTM can be optimized using supervised learning (SL), where a domain expert provides example dialogs which the LSTM should imitate; or using reinforcement learning (RL), where the system improves by interacting directly with end users. Experiments show that SL and RL are complementary: SL alone can derive a reasonable initial policy from a small number of training dialogs; and starting RL optimization with a policy trained with SL substantially accelerates the learning rate of RL.
Analysis of Research in Healthcare Data Analytics
Venue: arXiv cs.CY 2016
Year: 2016

The main aim of this paper is to provide a deep analysis on the research field of healthcare data analytics. This paper is analyzing the previous studies and works in this research area, as well as highlighting some of guidelines and gaps. This study has used seven popular databases and selected most relevant papers, in order to conduct this paper. The paper has listed some data analytics tools and techniques that have been used to improve healthcare performance in many areas such as: medical operations, reports, decision making, and prediction and prevention system. Moreover, the systematic review has showed an interesting demographic of fields of publication, research approaches, as well as outlined some of the possible reasons and issues associated with healthcare data analytics, based on geographical distribution theme.
Towards knowledge sharing in disaster management: An agent oriented knowledge analysis framework
Venue: arXiv cs.CY 2016
Year: 2016

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES in NSW to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW.
Distance Metric Ensemble Learning and the Andrews-Curtis Conjecture
Venue: arXiv cs.AI 2016
Year: 2016

Motivated by the search for a counterexample to the Poincaré conjecture in three and four dimensions, the Andrews-Curtis conjecture was proposed in 1965. It is now generally suspected that the Andrews-Curtis conjecture is false, but small potential counterexamples are not so numerous, and previous work has attempted to eliminate some via combinatorial search. Progress has however been limited, with the most successful approach (breadth-first-search using secondary storage) being neither scalable nor heuristically-informed. A previous empirical analysis of problem structure examined several heuristic measures of search progress and determined that none of them provided any useful guidance for search. In this article, we induce new quality measures directly from the problem structure and combine them to produce a more effective search driver via ensemble machine learning. By this means, we eliminate 19 potential counterexamples, the status of which had been unknown for some years.
Thinking Out Loud and e-Health for Coordinated Care Lessons from User Requirements Gathering in the 4C Project
Venue: arXiv cs.CY 2016
Year: 2016

e-Health is a core part of Australias strategy to address rising costs and changing demands for healthcare services. With over $1bn spent and only 6% of Australians registered, the personally controlled electronic health record (PCEHR) suggests user challenges remain. While evidence confirms the benefits from involving users in systems development there is a need for more examples of how to engage effectively in healthcare settings. This research describes the use of an agile development methodology combined with the thinking out loud technique to deliver a solution that exceeded user requirements in supporting a new model of care. The 4C project solution connected Aged Care institutions with general practices, hospitals and specialist services in Tasmanias north-west region. It was underpinned by a model of Technology Mediated Social Participation (TMSP). As a trial project for the PCEHR it remains unclear why lessons learned appear not to have been deployed more explicitly in the National roll-out.
Evaluating the Utility of Research Articles for Teaching Information Security Management
Venue: arXiv cs.CY 2016
Year: 2016

Research articles can support teaching by introducing the latest expert thinking on relevant topics and trends and describing practical real-world case studies to encourage discussion and analysis. However, from the point of view of the instructor, a common challenge is identifying the most suitable papers for classroom teaching amongst a very large pool of potential candidates that are not typically written for teaching purposes. Further, even in practice-oriented disciplines such as Information Security Management (ISM), high-quality journals emphasise theoretical contribution and research method rather than relevance to practice. Our review of the relevant literature did not find a comprehensive set of criteria to assist instructors in evaluating the suitability of research articles to teaching. Therefore, this research-in-progress paper presents a framework to support academics in the process of evaluating the suitability of research articles for their teaching programs.
Maturity of the Internet of Things Research Field: Or Why Choose Rigorous Keywords
Venue: arXiv cs.DL 2016
Year: 2016

Periodically researchers examine their fields of research in order to test the fields maturity, to determine theories and methods used, and to uncover areas of research emphasis. Research articles for these studies are generally located through keyword search, then manually read and coded in order to summarise the research field. This research tests using metadata analysis alone, on the internet of things (IoT) research field, using data extracted from three academic databases. The metadata analysis was proposed to reduce time in analysis, and allow for testing of a much larger sample of articles. Findings indicated that the IoT research field was immature, with experimental methods dominating research outputs. However, difficulties with keyword metadata included synonyms, abbreviations, lack of accuracy, and method and theory not being included in metadata. A keyword schema is suggested in order to relieve these problems in future articles, and to assist researchers in locating relevant literature.
Optimal Auctions with Restricted Allocations
Venue: arXiv cs.GT 2016
Year: 2016

We study the problem of designing optimal auctions under restrictions on the set of permissible allocations. In addition to allowing us to restrict to deterministic mechanisms, we can also indirectly model non-additive valuations. We prove a strong duality result, extending a result due to Daskalakis et al. [2015], that guarantees the existence of a certificate of optimality for optimal restricted mechanisms. As a corollary of our result, we provide a new characterization of the set of allocations that the optimal mechanism may actually use. To illustrate our result we find and certify optimal mechanisms for four settings where previous frameworks do not apply, and provide new economic intuition about some of the tools that have previously been used to find optimal mechanisms.
Distance-based Control of K4 Formation with Almost Global Convergence
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we propose a distance-based formation control strategy that can enable four mobile agents, which are modelled by a group of single-integrators, to achieve the desired formation shape specified by using six consistent inter-agent distances in a 2-dimensional space. The control law is closely related to a gradient-based control law formed from a potential function reflecting the error between the actual inter-agent distances and the desired inter-agent distances. There are already control strategies achieving the same objective in a distance-based control manner in the literature, but the results do not yet include a global as opposed to local stability analysis. We propose a control strategy modified from the existing gradient-based control law so that we can achieve almost global convergence to the desired formation shape, and the control law uses known properties for an associated formation shape control problem involving a four-agent tetrahedron formation in 3-dimensional space. Simulation results verifying our analysis are also presented.
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
Venue: arXiv cs.LG 2016
Year: 2016

We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but where the recurrence is not to model sequential structure, only to perform computation. We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values. It means that after the feedforward initialization, the recurrent network is very close to a fixed point of the network dynamics, where the energy gradient is 0. The main condition is that consecutive layers form a good auto-encoder, or more generally that different groups of inputs into the unit (in particular, bottom-up inputs on one hand, top-down inputs on the other hand) are consistent with each other, producing the same contribution into the total weighted sum of inputs. In biological terms, this would correspond to having each dendritic branch correctly predicting the aggregate input from all the dendritic branches, i.e., the soma potential. This is consistent with the prediction that the synaptic weights into dendritic branches such as those of the apical and basal dendrites of pyramidal cells are trained to minimize the prediction error made by the dendritic branch when the target is the somatic activity. Whereas previous work has shown how to achieve fast negative phase inference (when the model is unclamped) in a predictive recurrent model, this contribution helps to achieve fast positive phase inference (when the target output is clamped) in such recurrent neural models.
Undecidability and Irreducibility Conditions for Open-Ended Evolution and Emergence
Venue: arXiv cs.OH 2016
Year: 2016

Is undecidability a requirement for open-ended evolution (OEE)? Using methods derived from algorithmic complexity theory, we propose robust computational definitions of open-ended evolution and the adaptability of computable dynamical systems. Within this framework, we show that decidability imposes absolute limits to the stable growth of complexity in computable dynamical systems. Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems. Complexity is assessed in terms of three measures: sophistication, coarse sophistication and busy beaver logical depth. These three complexity measures assign low complexity values to random (incompressible) objects. As time grows, the stated complexity measures allow for the existence of complex states during the evolution of a computable dynamical system. We show, however, that finding these states involves undecidable computations. We conjecture that for similar complexity measures that assign low complexity values, decidability imposes comparable limits to the stable growth of complexity, and that such behaviour is necessary for non-trivial evolutionary systems. We show that the undecidability of adapted states imposes novel and unpredictable behaviour on the individuals or populations being modelled. Such behaviour is irreducible. Finally, we offer an example of a system, first proposed by Chaitin, that exhibits strong OEE.
Window Parity Games: An Alternative Approach Toward Parity Games with Time Bounds
Venue: arXiv cs.LO 2016
Year: 2016

Classical objectives in two-player zero-sum games played on graphs often deal with limit behaviors of infinite plays: e.g., mean-payoff and total-payoff in the quantitative setting, or parity in the qualitative one (a canonical way to encode omega-regular properties). Those objectives offer powerful abstraction mechanisms and often yield nice properties such as memoryless determinacy. However, their very nature provides no guarantee on time bounds within which something good can be witnessed. In this work, we consider two approaches toward inclusion of time bounds in parity games. The first one, parity-response games, is based on the notion of finitary parity games [CHH09] and parity games with costs [FZ14,WZ16]. The second one, window parity games, is inspired by window mean-payoff games [CDRR15]. We compare the two approaches and show that while they prove to be equivalent in some contexts, window parity games offer a more tractable alternative when the time bound is given as a parameter (P-c. vs. PSPACE-c.). In particular, it provides a conservative approximation of parity games computable in polynomial time. Furthermore, we extend both approaches to the multi-dimension setting. We give the full picture for both types of games with regard to complexity and memory bounds. [CHH09] K. Chatterjee, T.A. Henzinger, F. Horn (2009): Finitary winning in omega-regular games. ACM Trans. Comput. Log. 11(1). [FZ14] N. Fijalkow, M. Zimmermann (2014): Parity and Streett Games with Costs. LMCS 10(2). [WZ16] A. Weinert, M. Zimmermann (2016): Easy to Win, Hard to Master: Optimal Strategies in Parity Games with Costs. Proc. of CSL, LIPIcs, Schloss Dagstuhl - LZI. To appear. [CDRR15] K. Chatterjee, L. Doyen, M. Randour, J.-F. Raskin (2015): Looking at mean-payoff and total-payoff through windows. Information and Computation 242, pp. 25-52.