OpenRaiser/Intern-Atlas huggingface dataset:
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.
Understanding the Factors that Impact the Popularity of GitHub Repositories
Venue: arXiv cs.SE 2016
Year: 2016

Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which can help them on building and evolving systems in a competitive software market.
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Venue: arXiv cs.LG 2016
Year: 2016

We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple parallel processors to operate on a randomly chosen subset of blocks of the feature vector. We call the algorithm stochastic because processors choose training subsets uniformly at random. Algorithms that are parallel in either of these dimensions exist, but RAPSA is the first attempt at a methodology that is parallel in both the selection of blocks and the selection of elements of the training set. In RAPSA, processors utilize the randomly chosen functions to compute the stochastic gradient component associated with a randomly chosen block. The technical contribution of this paper is to show that this minimally coordinated algorithm converges to the optimal classifier when the training objective is convex. Moreover, we present an accelerated version of RAPSA (ARAPSA) that incorporates the objective function curvature information by premultiplying the descent direction by a Hessian approximation matrix. We further extend the results for asynchronous settings and show that if the processors perform their updates without any coordination the algorithms are still convergent to the optimal argument. RAPSA and its extensions are then numerically evaluated on a linear estimation problem and a binary image classification task using the MNIST handwritten digit dataset.
On a class of left metacyclic codes
Venue: arXiv cs.IT 2016
Year: 2016

Let $G_{(m,3,r)}=\langle x,y\mid x^m=1, y^3=1,yx=x^ry\rangle$ be a metacyclic group of order $3m$, where ${\rm gcd}(m,r)=1$, $1<r<m$ and $r^3\equiv 1$ (mod $m$). Then left ideals of the group algebra $\mathbb{F}_q[G_{(m,3,r)}]$ are called left metacyclic codes over $\mathbb{F}_q$ of length $3m$, and abbreviated as left $G_{(m,3,r)}$-codes. A system theory for left $G_{(m,3,r)}$-codes is developed for the case of ${\rm gcd}(m,q)=1$ and $r\equiv q^ε$ for some positive integer $ε$, only using finite field theory and basic theory of cyclic codes and skew cyclic codes. The fact that any left $G_{(m,3,r)}$-code is a direct sum of concatenated codes with inner codes ${\cal A}_i$ and outer codes $C_i$ is proved, where ${\cal A}_i$ is a minimal cyclic code over $\mathbb{F}_q$ of length $m$ and $C_i$ is a skew cyclic code of length $3$ over an extension field of $\mathbb{F}_q$. Then an explicit expression for each outer code in any concatenated code is provided. Moreover, the dual code of each left $G_{(m,3,r)}$-code is given and self-orthogonal left $G_{(m,3,r)}$-codes are determined.
Large Antenna Analysis of Multi-Cell Full-Duplex Networks
Venue: arXiv cs.IT 2016
Year: 2016

We study a multi-cell multi-user MIMO full-duplex network, where each base station (BS) has multiple antennas with full-duplex capability supporting single-antenna users with either full-duplex or half-duplex radios. We characterize the up- and downlink ergodic achievable rates for the case of linear precoders and receivers. The rate analysis includes practical constraints such as imperfect self- interference cancellation, channel estimation error, training overhead and pilot contamination. We show that the 2X gain of full-duplex over half-duplex system remains in the asymptotic regime where the number of BS antennas grows infinitely large. We numerically evaluate the finite SNR and antenna performance, which reveals that full-duplex networks can use significantly fewer antennas to achieve spectral efficiency gain over the half-duplex counterparts. In addition, the overall full-duplex gains can be achieved under realistic 3GPP multi-cell network settings despite the increased interference introduced in the full-duplex networks.
Inscribed Matter Communication: Part II
Venue: arXiv cs.ET 2016
Year: 2016

This paper is Part II of a two-paper set which develops a finest-grain per-molecule timing treatment of molecular communication. We first consider a simple one-molecule timing channel with a molecule launch deadline, similar to but different from previous work ("Bits Through Queues") where the constraint was mean launch time. We also derive a number of results related to the {\em ordering entropy}, a key quantity which undergirds the capacity bounds for the molecular timing channel, both with and without token data payloads. We then conclude with an upper bound on molecular timing-channel capacity.
Automatic quality evaluation and (semi-) automatic improvement of OCR models for historical printings
Venue: arXiv cs.DL 2016
Year: 2016

Good OCR results for historical printings rely on the availability of recognition models trained on diplomatic transcriptions as ground truth, which is both a scarce resource and time-consuming to generate. Instead of having to train a separate model for each historical typeface, we propose a strategy to start from models trained on a combined set of available transcriptions in a variety of fonts. These \emph{mixed models} result in character accuracy rates over 90\% on a test set of printings from the same period of time, but without any representation in the training data, demonstrating the possibility to overcome the typography barrier by generalizing from a few typefaces to a larger set of (similar) fonts in use over a period of time. The output of these mixed models is then used as a baseline to be further improved by both fully automatic methods and semi-automatic methods involving a minimal amount of manual transcriptions. In order to evaluate the recognition quality of each model in a series of models generated during the training process in the absence of any ground truth, we introduce two readily observable quantities that correlate well with true accuracy. These quantities are \emph{mean character confidence C} (as given by the OCR engine OCRopus) and \emph{mean token lexicality L} (a distance measure of OCR tokens from modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine). Whereas the fully automatic method is able to improve upon the result of a mixed model by only 1-2 percentage points, already 100-200 hand-corrected lines lead to much better OCR results with character error rates of only a few percent. This procedure minimizes the amount of ground truth production and does not depend on the previous construction of a specific typographic model.
Deep Reinforcement Learning Discovers Internal Models
Venue: arXiv cs.AI 2016
Year: 2016

Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still lacking the tools to analayze their performance. In this work we present the Semi-Aggregated MDP (SAMDP) model. A model best suited to describe policies exhibiting both spatial and temporal hierarchies. We describe its advantages for analyzing trained policies over other modeling approaches, and show that under the right state representation, like that of DQN agents, SAMDP can help to identify skills. We detail the automatic process of creating it from recorded trajectories, up to presenting it on t-SNE maps. We explain how to evaluate its fitness and show surprising results indicating high compatibility with the policy at hand. We conclude by showing how using the SAMDP model, an extra performance gain can be squeezed from the agent.
Adaptive Cross-Packet HARQ
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we investigate a coding strategy devised to increase the throughput in hybrid ARQ (HARQ) transmission over block fading channel. In our approach, the transmitter jointly encodes a variable number of bits for each round of HARQ. The parameters (rates) of this joint coding can vary and may be based on the negative acknowledgment (NACK) provided by the receiver or, on the past (outdated) information about the channel states. These new degrees of freedom allow us to improve the match between the codebook and the channel states experienced by the receiver. The results indicate that significant gains can be obtained using the proposed coding strategy, particularly notable when the conventional HARQ fails to offer throughput improvement even if the number of transmission rounds is increased. The new cross-packet HARQ is also implemented using turbo codes where we show that the theoretically predicted throughput gains materialize in practice, and we discuss the implementation challenges.
Guarded Cubical Type Theory: Path Equality for Guarded Recursion
Venue: arXiv cs.LO 2016
Year: 2016

This paper improves the treatment of equality in guarded dependent type theory (GDTT), by combining it with cubical type theory (CTT). GDTT is an extensional type theory with guarded recursive types, which are useful for building models of program logics, and for programming and reasoning with coinductive types. We wish to implement GDTT with decidable type-checking, while still supporting non-trivial equality proofs that reason about the extensions of guarded recursive constructions. CTT is a variation of Martin-Löf type theory in which the identity type is replaced by abstract paths between terms. CTT provides a computational interpretation of functional extensionality, is conjectured to have decidable type checking, and has an implemented type-checker. Our new type theory, called guarded cubical type theory, provides a computational interpretation of extensionality for guarded recursive types. This further expands the foundations of CTT as a basis for formalisation in mathematics and computer science. We present examples to demonstrate the expressivity of our type theory, all of which have been checked using a prototype type-checker implementation, and present semantics in a presheaf category.
A Primer on the Differential Calculus of 3D Orientations
Venue: arXiv cs.RO 2016
Year: 2016

The proper handling of 3D orientations is a central element in many optimization problems in engineering. Unfortunately many researchers and engineers struggle with the formulation of such problems and often fall back to suboptimal solutions. The existence of many different conventions further complicates this issue, especially when interfacing multiple differing implementations. This document discusses an alternative approach which makes use of a more abstract notion of 3D orientations. The relative orientation between two coordinate systems is primarily identified by the coordinate mapping it induces. This is combined with the standard exponential map in order to introduce representation-independent and minimal differentials, which are very convenient in optimization based methods.
Spectral decomposition method of dialog state tracking via collective matrix factorization
Venue: arXiv cs.CL 2016
Year: 2016

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.
Sampling Method for Fast Training of Support Vector Data Description
Venue: arXiv cs.LG 2016
Year: 2016

Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .
DecomposeMe: Simplifying ConvNets for End-to-End Learning
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community. However, these networks are computationally demanding and not suitable for embedded devices where memory and time consumption are relevant. In this paper, we propose DecomposeMe, a simple but effective technique to learn features using 1D convolutions. The proposed architecture enables both simplicity and filter sharing leading to increased learning capacity. A comprehensive set of large-scale experiments on ImageNet and Places2 demonstrates the ability of our method to improve performance while significantly reducing the number of parameters required. Notably, on Places2, we obtain an improvement in relative top-1 classification accuracy of 7.7\% with an architecture that requires 92% fewer parameters compared to VGG-B. The proposed network is also demonstrated to generalize to other tasks by converting existing networks.
Explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time
Venue: arXiv cs.DS 2016
Year: 2016

We derandomize G. Valiant's [J. ACM 62 (2015) Art. 13] subquadratic-time algorithm for finding outlier correlations in binary data. Our derandomized algorithm gives deterministic subquadratic scaling essentially for the same parameter range as Valiant's randomized algorithm, but the precise constants we save over quadratic scaling are more modest. Our main technical tool for derandomization is an explicit family of correlation amplifiers built via a family of zigzag-product expanders in Reingold, Vadhan, and Wigderson [Ann. of Math. 155 (2002) 157--187]. We say that a function $f:\{-1,1\}^d\rightarrow\{-1,1\}^D$ is a correlation amplifier with threshold $0\leqτ\leq 1$, error $γ\geq 1$, and strength $p$ an even positive integer if for all pairs of vectors $x,y\in\{-1,1\}^d$ it holds that (i) $|\langle x,y\rangle|<τd$ implies $|\langle f(x),f(y)\rangle|\leq(τγ)^pD$; and (ii) $|\langle x,y\rangle|\geqτd$ implies $\bigl(\frac{\langle x,y\rangle}{γd}\bigr)^pD \leq\langle f(x),f(y)\rangle\leq \bigl(\frac{γ\langle x,y\rangle}{d}\bigr)^pD$.
High-resolution LIDAR-based Depth Mapping using Bilateral Filter
Venue: arXiv cs.CV 2016
Year: 2016

High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection. Upsampling is often based on combining data from a monocular camera to compensate the low-resolution of a LIDAR. This paper, on the other hand, introduces a novel framework to obtain dense depth-map solely from a single LIDAR point cloud; which is a research direction that has been barely explored. The formulation behind the proposed depth-mapping process relies on local spatial interpolation, using sliding-window (mask) technique, and on the Bilateral Filter (BF) where the variable of interest, the distance from the sensor, is considered in the interpolation problem. In particular, the BF is conveniently modified to perform depth-map upsampling such that the edges (foreground-background discontinuities) are better preserved by means of a proposed method which influences the range-based weighting term. Other methods for spatial upsampling are discussed, evaluated and compared in terms of different error measures. This paper also researches the role of the mask's size in the performance of the implemented methods. Quantitative and qualitative results from experiments on the KITTI Database, using LIDAR point clouds only, show very satisfactory performance of the approach introduced in this work.
Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon
Venue: arXiv cs.CL 2016
Year: 2016

We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make predictions on sentence- or segment-level. We automatically construct a socially-tuned lexicon that is bootstrapped from existing general-purpose sentiment lexicons to further improve the performance. We evaluate our agreement and disagreement tagging model on two disparate online discussion corpora -- Wikipedia Talk pages and online debates. Our model is shown to outperform the state-of-the-art approaches in both datasets. For example, the isotonic CRF model achieves F1 scores of 0.74 and 0.67 for agreement and disagreement detection, when a linear chain CRF obtains 0.58 and 0.56 for the discussions on Wikipedia Talk pages.
AMPF: Application-aware Multipath Packet Forwarding using Machine Learning and SDN
Venue: arXiv cs.NI 2016
Year: 2016

This paper proposes an application-aware multipath packet forwarding framework that integrates Machine Learning Techniques (MLT) and Software Defined Networks (SDN). As the Internet provides a variety of services and their performance requirement has become heterogeneous, it is common to come across the scenario of multiple flows competing for a constrained resource such as bandwidth, less jitter or low latency path. Such factors are application specific requirement that is beyond the knowledge of a simple combination of protocol type and port number. Better overall performance could be achieved if the network is able to prioritize the flows and assign resources based on their application specific requirement. Our system prioritizes each of the flows using MLT and routes it through a path according to the flow priority and network state using SDN. The proof of concept implementation has been done on OpenvSwitch and evaluation results involving a large number of flows exhibited a significant improvement over the traditional network setup. We also report that the port number and protocol are not contributing to determine the application in the decision-making process of Machine Learning (ML).
Device-to-Device Communication in Cellular Networks: A Survey
Venue: arXiv cs.NI 2016
Year: 2016

A constant need to increase the network capacity for meeting the growing demands of the subscribers has led to the evolution of cellular communication networks from the first generation (1G) to the fifth generation (5G). There will be billions of connected devices in the near future. Such a large number of connections are expected to be heterogeneous in nature, demanding higher data rates, lesser delays, enhanced system capacity and superior throughput. The available spectrum resources are limited and need to be flexibly used by the mobile network operators (MNOs) to cope with the rising demands. An emerging facilitator of the upcoming high data rate demanding next generation networks (NGNs) is device-to-device (D2D) communication. An extensive survey on device-to-device (D2D) communication has been presented in this paper, including the plus points it offers, the key open issues associated with it like peer discovery, resource allocation etc, demanding special attention of the research community, some of its integrant technologies like millimeter wave D2D (mmWave), ultra dense networks (UDNs), cognitive D2D, handover procedure in D2D and its numerous use cases. Architecture is suggested aiming to fulfill all the subscriber demands in an optimal manner. The Appendix mentions some ongoing standardization activities and research projects of D2D communication.
A Conceptual Design of Mobile Learning Applications for Preschool Children
Venue: arXiv cs.CY 2016
Year: 2016

This article focuses on the possibilities of using mobile learning in the Bulgarian preschool education of young children. The state preschool educational regulations are presented and discussed. The problem concerning the children's safety when using mobile devices in terms of access to information on the Internet is revealed and analyzed. Two conceptual models of applications for mobile learning aimed at preschool children are designed. Their advantages and disadvantages are outlined and discussed. Keywords - mobile applications for children, mobile learning, design of mobile learning component, software engineering