OpenRaiser/Intern-Atlas huggingface dataset:
Learning Opposites Using Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.
The Option-Critic Architecture
Venue: arXiv cs.AI 2016
Year: 2016

Temporal abstraction is key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions is well understood, creating such abstractions autonomously from data has remained challenging. We tackle this problem in the framework of options [Sutton, Precup & Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options and propose a new option-critic architecture capable of learning both the internal policies and the termination conditions of options, in tandem with the policy over options, and without the need to provide any additional rewards or subgoals. Experimental results in both discrete and continuous environments showcase the flexibility and efficiency of the framework.
Implementation of the SWIM Mobility Model in OMNeT++
Venue: arXiv cs.NI 2016
Year: 2016

The Internet of Things (IoT) is expected to grow into billions of devices in the near future. Evaluating mechanisms such as networking architectures for communications in the IoT require the use of simulators due to the scale of the size of networks. Mobility is one of the key aspects that need to be evaluated when considering the different scenarios of the IoT. Small Worlds in Motion (SWIM) is a mobility model that mathematically characterises the movement patterns of humans. The work presented in this paper details the development and verification of the SWIM mobility model in the OMNeT++ simulator.
High-Dimensional Data Visualization by Interactive Construction of Low-Dimensional Parallel Coordinate Plots
Venue: arXiv cs.HC 2016
Year: 2016

Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.
Lower bounds for sensitivity of graph properties
Venue: arXiv cs.CC 2016
Year: 2016

We prove that the sensitivity of any non-trivial graph property on $n$ vertices is at least $\lfloor \frac{1}{2}n \rfloor$ , provided $n$ is sufficiently large.
Reachability for dynamic parametric processes
Venue: arXiv cs.LO 2016
Year: 2016

In a dynamic parametric process every subprocess may spawn arbitrarily many, identical child processes, that may communicate either over global variables, or over local variables that are shared with their parent. We show that reachability for dynamic parametric processes is decidable under mild assumptions. These assumptions are e.g. met if individual processes are realized by pushdown systems, or even higher-order pushdown systems. We also provide algorithms for subclasses of pushdown dynamic parametric processes, with complexity ranging between NP and DEXPTIME.
Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016
Venue: arXiv cs.LG 2016
Year: 2016

Predicting the prices of stocks at any stock market remains a quest for many investors and researchers. Those who trade at the stock market tend to use technical, fundamental or time series analysis in their predictions. These methods usually guide on trends and not the exact likely prices. It is for this reason that Artificial Intelligence systems, such as Artificial Neural Network, that is feedforward multi-layer perceptron with error backpropagation, can be used for such predictions. A difficulty in neural network application is the determination of suitable network parameters. A previous research by the author already determined the network parameters as 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction. This model has been put to the test in predicting selected Shanghai Stock Exchange stocks in the future period of 21-Sep-2016 to 11-Oct-2016, about one week after the publication of these predictions. The research aims at confirming that simple neural network systems can be quite powerful in typical stock market predictions.
A Deep Metric for Multimodal Registration
Venue: arXiv cs.CV 2016
Year: 2016

Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.
Patterns of authors contribution in scientific manuscripts
Venue: arXiv cs.DL 2016
Year: 2016

Science is becoming increasingly more interdisciplinary, giving rise to more diversity in the areas of expertise within research labs and groups. This also have brought changes to the role researchers in scientific works. As a consequence, multi-authored scientific papers have now became a norm for high quality research. Unfortunately, such a phenomenon induces bias to existing metrics employed to evaluate the productivity and success of researchers. While some metrics were adapted to account for the rank of authors in a paper, many journals are now requiring a description of the specific roles of each author in a publication. Surprisingly, the investigation of the relationship between the rank of authors and their contributions has been limited to a few studies. By analyzing such kind of data, here we show, quantitatively, that the regularity in the authorship contributions decreases with the number of authors in a paper. Furthermore, we found that the rank of authors and their roles in papers follows three general patterns according to the nature of their contributions, such as writing, data analysis, and the conduction of experiments. This was accomplished by collecting and analyzing the data retrieved from PLoS ONE and by devising an entropy-based measurement to quantify the effective number of authors in a paper according to their contributions. The analysis of such patterns confirms that some aspects of the author ranking are in accordance with the expected convention, such as the fact that the first and last authors are more likely to contribute more in a scientific work. Conversely, such analysis also revealed that authors in the intermediary positions of the rank contribute more in certain specific roles, such as the task of collecting data. This indicates that the an unbiased evaluation of researchers must take into account the distinct types of scientific contributions.
Assigning Creative Commons Licenses to Research Metadata: Issues and Cases
Venue: arXiv cs.CY 2016
Year: 2016

This paper discusses the problem of lack of clear licensing and transparency of usage terms and conditions for research metadata. Making research data connected, discoverable and reusable are the key enablers of the new data revolution in research. We discuss how the lack of transparency hinders discovery of research data and make it disconnected from the publication and other trusted research outcomes. In addition, we discuss the application of Creative Commons licenses for research metadata, and provide some examples of the applicability of this approach to internationally known data infrastructures.
Reducing energy consumption of network infrastructure using spectral approach
Venue: arXiv cs.NI 2016
Year: 2016

The energy consumption by ICT (Information and Communication Technology) equipment is rapidly increasing which causes a significant economic and environmental problem. At present, the network infrastructure is becoming a large portion of the energy footprint in ICT. Thus, the concept of energy efficient or green networking has been introduced. Now one of the main concerns of network industry is to minimize energy consumption of network infrastructure because of the potential economic benefits, ethical responsibility, and its environmental impact. In this paper, the energy management strategies to reduce the energy consumed by network switches in LAN (Local Area Network) have been developed. According to the life-cycle assessment of network switches, the highest amount of energy is consumed during usage phase. The study considers bandwidth, link load and traffic matrices as input parameters which have the highest contribution to energy footprints of network switches during usage phase and energy consumption as output. Then with the objective of reducing energy usage of network infrastructure, the feasibility of putting Ethernet switches hibernate or sleep mode was investigated. After that, the network topology was reorganized using clustering method based on the spectral approach for putting network switches to hibernate or switched off mode considering the time and communications among them. Experimental results show the interest in this approach in terms of energy consumption. .
A 3-CNF-SAT descriptor algebra and the solution of the P=NP conjecture
Venue: arXiv cs.CC 2016
Year: 2016

The relationship between the complexity classes P and NP is an unsolved question in the field of theoretical computer science. In this paper, we investigate a descriptor approach based on lattice properties. This paper proposes a new way to decide the satisfiability of any 3-CNF-SAT problem. The analysis of this exact [non heuristical] algorithm shows a strictly bounded exponential complexity. The complexity of any 3-CNF-SAT solution is bounded by O(2^490). This over-estimated bound is reached by an algorithm working on the smallest description (via descriptor functions) of the evolving set of solutions in function of the already considered clauses, without exploring these solutions. Any remark about this paper is warmly welcome.
Straightness of rectilinear vs. radio-concentric networks: modeling simulation and comparison
Venue: arXiv cs.SI 2016
Year: 2016

This paper proposes a comparison between rectilinear and radio-concentric networks. Indeed, those networks are often observed in urban areas, in several cities all over the world. One of the interesting properties of such networks is described by the \textit{straightness} measure from graph theory, which assesses how much moving from one node to another along the network links departs from the network-independent straightforward path. We study this property in both rectilinear and radio-concentric networks, first by analyzing mathematically routes from the center to peripheral locations in a theoretical framework with perfect topology, then using simulations for multiple origin-destination paths. We show that in most of the cases, radio-concentric networks have a better straightness than rectilinear ones. How may this property be used in the future for urban networks?
Infinite Server Queueing Networks with Deadline Based Routing
Venue: arXiv cs.PF 2016
Year: 2016

Motivated by timeouts in Internet services, we consider networks of infinite server queues in which routing decisions are based on deadlines. Specifically, at each node in the network, the total service time equals the minimum of several independent service times (e.g. the minimum of the amount of time required to complete a transaction and a deadline). Furthermore, routing decisions depend on which of the independent service times achieves the minimum (e.g. exceeding a deadline will require the customer to be routed so they can re-attempt the transaction). Because current routing decisions are dependent on past service times, much of the existing theory on product-form queueing networks does not apply. In spite of this, we are able to show that such networks have product-form equilibrium distributions. We verify our analytic characterization with a simulation of a simple network. We also discuss extensions of this work to more general settings.
Toward a Science of Autonomy for Physical Systems: Aerial Earth Science
Venue: arXiv cs.CY 2016
Year: 2016

Unmanned Aerial Vehicles (UAVs) equipped with LiDAR, electro-optical and infrared cameras, SAR and atmospheric sensors have transformed the way we acquire high spatio-temporal resolution data. For example, UAVs equipped with these sensors have been able to obtain topography at resolutions of less than one meter, revolutionizing earth sciences. Surface processes act at spatial scales on the order of a meter to produce intricate landforms and UAVs equipped with these sensors are able to measure the three dimensional spatio-temporal geometry of the earths surface and overlying anthropogenic features and vegetation at resolutions appropriate to document these processes. In addition, surface changes due to erosion, transport and sedimentation, as well as earthquakes, landslides, volcanoes can be quantified with this data.
Toward a Science of Autonomy for Physical Science: Healthcare
Venue: arXiv cs.CY 2016
Year: 2016

In Star Wars Episode V, we see Luke Skywalker being repaired by a surgical robot. In the context of the movie, this doesn't seem surprising or disturbing. After all, it is a long, long time ago, in a galaxy far, far away. It would never happen here. Or could it? Would we accept a robot as our doctor, our surgeon, or our in-home care specialist? Imagine walking into an operating room and no one was there. You are instructed to lie down on the operating table, and the OR system takes over. Would you feel comfortable with this possible future world?
Correlation-Aware Distributed Caching and Coded Delivery
Venue: arXiv cs.IT 2016
Year: 2016

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.
A simple algorithm for sampling colourings of $G(n,d/n)$ up to Gibbs Uniqueness Threshold
Venue: arXiv cs.DM 2016
Year: 2016

Approximate random $k$-colouring of a graph $G$ is a well studied problem in computer science and statistical physics. It amounts to constructing a $k$-colouring of $G$ which is distributed close to {\em Gibbs distribution} in polynomial time. Here, we deal with the problem when the underlying graph is an instance of Erdős-Rényi random graph $G(n,d/n)$, where $d$ is a sufficiently large constant. We propose a novel efficient algorithm for approximate random $k$-colouring $G(n,d/n)$ for any $k\geq (1+ε)d$. To be more specific, with probability at least $1-n^{-Ω(1)}$ over the input instances $G(n,d/n)$ and for $k\geq (1+ε)d$, the algorithm returns a $k$-colouring which is distributed within total variation distance $n^{-Ω(1)}$ from the Gibbs distribution of the input graph instance. The algorithm we propose is neither a MCMC one nor inspired by the message passing algorithms proposed by statistical physicists. Roughly the idea is as follows: Initially we remove sufficiently many edges of the input graph. This results in a "simple graph" which can be $k$-coloured randomly efficiently. The algorithm colours randomly this simple graph. Then it puts back the removed edges one by one. Every time a new edge is put back the algorithm updates the colouring of the graph so that the colouring remains random. The performance of the algorithm depends heavily on certain spatial correlation decay properties of the Gibbs distribution.
On the relative power of reduction notions in arithmetic circuit complexity
Venue: arXiv cs.CC 2016
Year: 2016

We show that the two main reduction notions in arithmetic circuit complexity, p-projections and c-reductions, differ in power. We do so by showing unconditionally that there are polynomials that are VNP-complete under c-reductions but not under p-projections. We also show that the question of which polynomials are VNP-complete under which type of reductions depends on the underlying field.
Complete classification of $(δ+αu^2)$-constacyclic codes over $\mathbb{F}_{2^m}[u]/\langle u^4\rangle$ of oddly even length
Venue: arXiv cs.IT 2016
Year: 2016

Let $\mathbb{F}_{2^m}$ be a finite field of cardinality $2^m$, $R=\mathbb{F}_{2^m}[u]/\langle u^4\rangle)$ and $n$ is an odd positive integer. For any $δ,α\in \mathbb{F}_{2^m}^{\times}$, ideals of the ring $R[x]/\langle x^{2n}-(δ+αu^2)\rangle$ are identified as $(δ+αu^2)$-constacyclic codes of length $2n$ over $R$. In this paper, an explicit representation and enumeration for all distinct $(δ+αu^2)$-constacyclic codes of length $2n$ over $R$ are presented.
FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification
Venue: arXiv cs.LG 2016
Year: 2016

Stochastic gradient-boosted decision trees are widely employed for multivariate classification and regression tasks. This paper presents a speed-optimized and cache-friendly implementation for multivariate classification called FastBDT. FastBDT is one order of magnitude faster during the fitting-phase and application-phase, in comparison with popular implementations in software frameworks like TMVA, scikit-learn and XGBoost. The concepts used to optimize the execution time and performance studies are discussed in detail in this paper. The key ideas include: An equal-frequency binning on the input data, which allows replacing expensive floating-point with integer operations, while at the same time increasing the quality of the classification; a cache-friendly linear access pattern to the input data, in contrast to usual implementations, which exhibit a random access pattern. FastBDT provides interfaces to C/C++, Python and TMVA. It is extensively used in the field of high energy physics by the Belle II experiment.
Uncertainty Analysis of Simple Macroeconomic Models Using Angel-Daemon Games
Venue: arXiv cs.CE 2016
Year: 2016

We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models with exogenous components. An uncertainty profile $\mathcal U$ is a short and macroscopic description of a potentially perturbed situation. The angel-daemon framework uses $\mathcal U$ to define a strategic game where two agents, the angel and the daemon, act selfishly having different goals. The Nash equilibria of those games provide the stable strategies in perturbed situations, giving a natural estimation of uncertainty. In this initial work we apply the framework in order to get an uncertainty analysis of linear versions of the IS-LM and the IS-MP models. In those models, by considering uncertainty profiles, we can capture different economical situations. Some of them can be described in terms of macroeconomic policy coordination. In other cases we just analyse the results of the system under some possible perturbation level. Besides providing examples of application we analyse the structure of the Nash equilibria in some particular cases of interest.
Hands-Free Segmentation of Medical Volumes via Binary Inputs
Venue: arXiv cs.CV 2016
Year: 2016

We propose a novel hands-free method to interactively segment 3D medical volumes. In our scenario, a human user progressively segments an organ by answering a series of questions of the form "Is this voxel inside the object to segment?". At each iteration, the chosen question is defined as the one halving a set of candidate segmentations given the answered questions. For a quick and efficient exploration, these segmentations are sampled according to the Metropolis-Hastings algorithm. Our sampling technique relies on a combination of relaxed shape prior, learnt probability map and consistency with previous answers. We demonstrate the potential of our strategy on a prostate segmentation MRI dataset. Through the study of failure cases with synthetic examples, we demonstrate the adaptation potential of our method. We also show that our method outperforms two intuitive baselines: one based on random questions, the other one being the thresholded probability map.