OpenRaiser/Intern-Atlas huggingface dataset:
On quasi-cyclic subspace codes
Venue: arXiv cs.IT 2016
Year: 2016

Construction of subspace codes with good parameters is one of the most important problems in random network coding. In this paper we present first a generalization of the concept of cyclic subspaces codes and further we show that the usual methods for constructing cyclic subspace codes over finite fields works for m-quasi cyclic codes, namely the subspaces polynomials and Frobenius mappings.
Nominal Cellular Automata
Venue: arXiv cs.FL 2016
Year: 2016

The emerging field of Nominal Computation Theory is concerned with the theory of Nominal Sets and its applications to Computer Science. We investigate here the impact of nominal sets on the definition of Cellular Automata and on their computational capabilities, with a special focus on the emergent behavioural properties of this new model and their significance in the context of computation-oriented interpretations of physical phenomena. A preliminary investigation of the relations between Nominal Cellular Automata and Wolfram's Elementary Cellular Automata is also carried out.
Temporal Learning and Sequence Modeling for a Job Recommender System
Venue: arXiv cs.LG 2016
Year: 2016

We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.
Close Encounters of the Higher Kind Emulating Constructor Classes in Standard ML
Venue: arXiv cs.PL 2016
Year: 2016

We implement a library for encoding constructor classes in Standard ML, including elaboration from minimal definitions, and automatic instantiation of superclasses.
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms
Venue: arXiv cs.LG 2016
Year: 2016

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabeled data for training. We investigate inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabeled data. We then apply our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
Intuitionistic Logic Programming for SQL (Extended Abstract)
Venue: arXiv cs.PL 2016
Year: 2016

Intuitionistic logic programming provides the notion of embedded implication in rule bodies, which can be used to reason about a current database modified by the antecedent. This can be applied to a system that translates SQL to Datalog to solve SQL WITH queries, for which relations are locally defined and can therefore be understood as added to the current database. In addition, assumptions in SQL queries as either adding or removing data can be modelled in this way as well, which is an interesting feature for decision-support scenarios. This work suggests a way to apply intuitionistic logic programming to SQL, and provides a pointer to a working system implementing this idea.
An OpenSHMEM Implementation for the Adapteva Epiphany Coprocessor
Venue: arXiv cs.DC 2016
Year: 2016

This paper reports the implementation and performance evaluation of the OpenSHMEM 1.3 specification for the Adapteva Epiphany architecture within the Parallella single-board computer. The Epiphany architecture exhibits massive many-core scalability with a physically compact 2D array of RISC CPU cores and a fast network-on-chip (NoC). While fully capable of MPMD execution, the physical topology and memory-mapped capabilities of the core and network translate well to Partitioned Global Address Space (PGAS) programming models and SPMD execution with SHMEM.
Interactive Exploration of the Employment Situation Report: From Fixed Tables to Dynamic Discovery
Venue: arXiv cs.HC 2016
Year: 2016

The monthly Bureau of Labor Statistics Employment Situation Report is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States. The report has broad impact on public and corporate economic confidence; however, the online access to this data employs outdated techniques, using a PDF format containing solely text and fixed tabular information. Creating an interactive interface for dynamic discovery on the BLS website could elicit more dialogue between the public and government spheres, drawing more traffic to government websites and triggering greater civic engagement. Our work suggests that the implementation of interactive visual analysis techniques to enable dynamic discovery leads to rapid interpretation of data as well as provides the means to explore the data for further insights. This paper presents two inspirational prototypes: a dashboard of interactive visualizations and an interactive time series explorer, allowing for temporal and spatial analyses and enabling users to combine data sets to create their own customized visualization.
Clockwork Convnets for Video Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Recent years have seen tremendous progress in still-image segmentation; however the naïve application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.
An approach of IR-Based short-range correspondence systems for swarm robot balanced requisitions and communications
Venue: arXiv cs.RO 2016
Year: 2016

This paper exhibits a short-run correspondence method appropriate for swarm versatile robots application. Infrared is utilized for transmitting and accepting information and obstruction location. The infrared correspondence code based swarm signaling is utilized for an independent versatile robot communication system in this research. A code based signaling system is developed for transmitting information between different entities of robot. The reflected infrared sign is additionally utilized for separation estimation for obstruction evasion. Investigation of robot demonstrates the possibility of utilizing infrared signs to get a solid nearby correspondence between swarm portable robots. This paper exhibits a basic decentralized control for swarm of self-collecting robots. Every robot in the code based swarm signaling is completely self-governing and controlled utilizing a conduct based methodology with just infrared-based nearby detecting and correspondences. The viability of the methodology has been checked with simulation, for a set of swarm robots.
Optimal steering of a linear stochastic system to a final probability distribution, Part III
Venue: arXiv cs.SY 2016
Year: 2016

The subject of this work has its roots in the so called Schroedginer Bridge Problem (SBP) which asks for the most likely distribution of Brownian particles in their passage between observed empirical marginal distributions at two distinct points in time. Renewed interest in this problem was sparked by a reformulation in the language of stochastic control. In earlier works, presented as Part I and Part II, we explored a generalization of the original SBP that amounts to optimal steering of linear stochastic dynamical systems between state-distributions, at two points in time, under full state feedback. In these works the cost was quadratic in the control input. The purpose of the present work is to detail the technical steps in extending the framework to the case where a quadratic cost in the state is also present. In the zero-noise limit, we obtain the solution of a (deterministic) mass transport problem with general quadratic cost.
Distributed-memory large deformation diffeomorphic 3D image registration
Venue: arXiv cs.DC 2016
Year: 2016

We present a parallel distributed-memory algorithm for large deformation diffeomorphic registration of volumetric images that produces large isochoric deformations (locally volume preserving). Image registration is a key technology in medical image analysis. Our algorithm uses a partial differential equation constrained optimal control formulation. Finding the optimal deformation map requires the solution of a highly nonlinear problem that involves pseudo-differential operators, biharmonic operators, and pure advection operators both forward and back- ward in time. A key issue is the time to solution, which poses the demand for efficient optimization methods as well as an effective utilization of high performance computing resources. To address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov solver. Our algorithm integrates several components: a spectral discretization in space, a semi-Lagrangian formulation in time, analytic adjoints, different regularization functionals (including volume-preserving ones), a spectral preconditioner, a highly optimized distributed Fast Fourier Transform, and a cubic interpolation scheme for the semi-Lagrangian time-stepping. We demonstrate the scalability of our algorithm on images with resolution of up to $1024^3$ on the "Maverick" and "Stampede" systems at the Texas Advanced Computing Center (TACC). The critical problem in the medical imaging application domain is strong scaling, that is, solving registration problems of a moderate size of $256^3$---a typical resolution for medical images. We are able to solve the registration problem for images of this size in less than five seconds on 64 x86 nodes of TACC's "Maverick" system.
To Prolong the Lifetime of Wireless Sensor Networks: The Min-max Fair Scheduling in a Multi-access Contra-Polymatroid
Venue: arXiv cs.IT 2016
Year: 2016

From an information-theoretic point of view, we investigate the min-max power scheduling problem in multi-access transmission. We prove that the min-max optimal vector in a contrapolymatroid is the base with the minimal distance to the equal allocation vector. Because we can realize any base of the contra-polymatroid by time sharing among the vertices, the problem searching for the min-max optimal vector is converted to a convex optimization problem solving the time sharing coefficients. Different from the traditional algorithms exploiting bottleneck link or waterfilling, the proposed method simplifies the computation of the min-max optimal vector and directly outputs the system parameters to realize the min-max fair scheduling. By adopting the proposed method and applying the acquired min-max optimal scheduling to multi-access transmission, the network lifetime of the wireless sensor network is prolonged.
The $(1|1)_R$-Centroid Problem on the Plane
Venue: arXiv cs.CG 2016
Year: 2016

In 1982, Drezner proposed the (1|1)-centroid problem on the plane, in which two players, called the leader and the follower, open facilities to provide service to customers in a competitive manner. The leader opens the first facility, and then the follower opens the second. Each customer will patronize the facility closest to him (ties broken in favor of the leader's one), thereby decides the market share of the two players. The goal is to find the best position for the leader's facility so that his market share is maximized. The best algorithm for this problem is an $O(n^2 \log n)$-time parametric search approach, which searches over the space of possible market share values. In the same paper, Drezner also proposed a general version of (1|1)-centroid problem by introducing a minimal distance constraint $R$, such that the follower's facility is not allowed to be located within a distance $R$ from the leader's. He proposed an $O(n^5 \log n)$-time algorithm for this general version by identifying $O(n^4)$ points as the candidates of the optimal solution and checking the market share for each of them. In this paper, we develop a new parametric search approach searching over the $O(n^4)$ candidate points, and present an $O(n^2 \log n)$-time algorithm for the general version, thereby close the $O(n^3)$ gap between the two bounds.
Some connections between BCK algebras and n ary block codes
Venue: arXiv cs.IT 2016
Year: 2016

In the last time some papers were devoted to the study of the con- nections between binary block codes and BCK-algebras. In this paper, we try to generalize these results to n-ary block codes, providing an algorithm which allows us to construct a BCK-algebra from a given n-ary block code.
A Comparison of Approaches for Traffic Engineering in IP and MPLS Networks
Venue: arXiv cs.NI 2016
Year: 2016

As IP / MPLS network service providers drive for improved network efficiencies, traffic engineering is becoming increasingly important. In this article we review the approaches for traffic engineering in IP / MPLS networks and present the result of a study, which compares their performance in operational networks
Compositional Distributional Cognition
Venue: arXiv cs.AI 2016
Year: 2016

We accommodate the Integrated Connectionist/Symbolic Architecture (ICS) of [32] within the categorical compositional semantics (CatCo) of [13], forming a model of categorical compositional cognition (CatCog). This resolves intrinsic problems with ICS such as the fact that representations inhabit an unbounded space and that sentences with differing tree structures cannot be directly compared. We do so in a way that makes the most of the grammatical structure available, in contrast to strategies like circular convolution. Using the CatCo model also allows us to make use of tools developed for CatCo such as the representation of ambiguity and logical reasoning via density matrices, structural meanings for words such as relative pronouns, and addressing over- and under-extension, all of which are present in cognitive processes. Moreover the CatCog framework is sufficiently flexible to allow for entirely different representations of meaning, such as conceptual spaces. Interestingly, since the CatCo model was largely inspired by categorical quantum mechanics, so is CatCog.
Curvature transformation
Venue: arXiv cs.GR 2016
Year: 2016

A transformation based on mean curvature is introduced which morphs triangulated surfaces into round spheres.
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering
Venue: arXiv cs.IR 2016
Year: 2016

We propose a document retrieval method for question answering that represents documents and questions as weighted centroids of word embeddings and reranks the retrieved documents with a relaxation of Word Mover's Distance. Using biomedical questions and documents from BIOASQ, we show that our method is competitive with PUBMED. With a top-k approximation, our method is fast, and easily portable to other domains and languages.
A Hiking Trip Through the Orders of Magnitude: Deriving Efficient Generators for Closed Simply-Typed Lambda Terms and Normal Forms
Venue: arXiv cs.PL 2016
Year: 2016

Contrary to several other families of lambda terms, no closed formula or generating function is known and none of the sophisticated techniques devised in analytic combinatorics can currently help with counting or generating the set of {\em simply-typed closed lambda terms} of a given size. Moreover, their asymptotic scarcity among the set of closed lambda terms makes counting them via brute force generation and type inference quickly intractable, with previous published work showing counts for them only up to size 10. By taking advantage of the synergy between logic variables, unification with occurs check and efficient backtracking in today's Prolog systems, we climb 4 orders of magnitude above previously known counts by deriving progressively faster Horn Clause programs that generate and/or count the set of closed simply-typed lambda terms of sizes up to 14. A similar count for {\em closed simply-typed normal forms} is also derived up to size 14. {\em {\bf Keywords:} logic programming transformations, type inference, combinatorics of lambda terms, simply-typed lambda calculus, simply-typed normal forms. }
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Venue: arXiv cs.CV 2016
Year: 2016

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise (AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
An approach to dealing with missing values in heterogeneous data using k-nearest neighbors
Venue: arXiv cs.LG 2016
Year: 2016

Techniques such as clusterization, neural networks and decision making usually rely on algorithms that are not well suited to deal with missing values. However, real world data frequently contains such cases. The simplest solution is to either substitute them by a best guess value or completely disregard the missing values. Unfortunately, both approaches can lead to biased results. In this paper, we propose a technique for dealing with missing values in heterogeneous data using imputation based on the k-nearest neighbors algorithm. It can handle real (which we refer to as crisp henceforward), interval and fuzzy data. The effectiveness of the algorithm is tested on several datasets and the numerical results are promising.
Bayesian Model Selection Methods for Mutual and Symmetric $k$-Nearest Neighbor Classification
Venue: arXiv cs.LG 2016
Year: 2016

The $k$-nearest neighbor classification method ($k$-NNC) is one of the simplest nonparametric classification methods. The mutual $k$-NN classification method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We propose another variant of $k$-NNC, the symmetric $k$-NN classification method (S$k$NNC) based on both mutual neighborship and one-sided neighborship. The performance of M$k$NNC and S$k$NNC depends on the parameter $k$ as the one of $k$-NNC does. We propose the ways how M$k$NN and S$k$NN classification can be performed based on Bayesian mutual and symmetric $k$-NN regression methods with the selection schemes for the parameter $k$. Bayesian mutual and symmetric $k$-NN regression methods are based on Gaussian process models, and it turns out that they can do M$k$NN and S$k$NN classification with new encodings of target values (class labels). The simulation results show that the proposed methods are better than or comparable to $k$-NNC, M$k$NNC and S$k$NNC with the parameter $k$ selected by the leave-one-out cross validation method not only for an artificial data set but also for real world data sets.
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Gesture recognition is a very essential technology for many wearable devices. While previous algorithms are mostly based on statistical methods including the hidden Markov model, we develop two dynamic hand gesture recognition techniques using low complexity recurrent neural network (RNN) algorithms. One is based on video signal and employs a combined structure of a convolutional neural network (CNN) and an RNN. The other uses accelerometer data and only requires an RNN. Fixed-point optimization that quantizes most of the weights into two bits is conducted to optimize the amount of memory size for weight storage and reduce the power consumption in hardware and software based implementations.
Security and Performance Comparison of Different Secure Channel Protocols for Avionics Wireless Networks
Venue: arXiv cs.CR 2016
Year: 2016

The notion of Integrated Modular Avionics (IMA) refers to inter-connected pieces of avionics equipment supported by a wired technology, with stringent reliability and safety requirements. If the inter-connecting wires are physically secured so that a malicious user cannot access them directly, then this enforces (at least partially) the security of the network. However, substituting the wired network with a wireless network - which in this context is referred to as an Avionics Wireless Network (AWN) - brings a number of new challenges related to assurance, reliability, and security. The AWN thus has to ensure that it provides at least the required security and safety levels offered by the equivalent wired network. Providing a wired-equivalent security for a communication channel requires the setting up of a strong, secure (encrypted) channel between the entities that are connected to the AWN. In this paper, we propose three approaches to establish such a secure channel based on (i) pre-shared keys, (ii) trusted key distribution, and (iii) key-sharing protocols. For each of these approaches, we present two representative protocol variants. These protocols are then implemented as part of a demo AWN and they are then compared based on performance measurements. Most importantly, we have evaluated these protocols based on security and operational requirements that we define in this paper for an AWN.