OpenRaiser/Intern-Atlas huggingface dataset:
Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs
Venue: arXiv cs.CV 2014
Year: 2014

Multiresolution Analysis (MRA) is a mathematical method that is based on working on a problem at different scales. One of its applications is medical imaging where processing at multiple scales, based on the concept of Gaussian and Laplacian image pyramids, is a well-known technique. It is often applied to reduce noise while preserving image detail on different levels of granularity without modifying the filter kernel. In scientific computing, multigrid methods are a popular choice, as they are asymptotically optimal solvers for elliptic Partial Differential Equations (PDEs). As such algorithms have a very high computational complexity that would overwhelm CPUs in the presence of real-time constraints, application-specific processors come into consideration for implementation. Despite of huge advancements in leveraging productivity in the respective fields, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. Recently, the HIPAcc framework was proposed as a means for automatic code generation of image processing algorithms, based on a Domain-Specific Language (DSL). From the same code base, it is possible to generate code for efficient implementations on several accelerator technologies including different types of Graphics Processing Units (GPUs) as well as reconfigurable logic (FPGAs). In this work, we demonstrate the ability of HIPAcc to generate code for the implementation of multiresolution applications on FPGAs and embedded GPUs.
Implementation of Program Part at Automated Workplace for a Teaching Department
Venue: arXiv cs.CY 2014
Year: 2014

The problem of improving the efficiency of the teaching department through the development of teaching department work area is described. Development of an automated workplace of a teaching department who allows to realize monitoring of progress of students, monitoring of mastering of disciplines by students, is synchronized with an automated workplace of the teacher of the higher school and autocompletes the report of movement of the contingent. Besides, the designed system allows to increase efficiency and efficiency of activities of employees of a teaching department.
Restpark: Minimal RESTful API for Retrieving RDF Triples
Venue: arXiv cs.DB 2014
Year: 2014

How do RDF datasets currently get published on the Web? They are either available as large RDF files, which need to be downloaded and processed locally, or they exist behind complex SPARQL endpoints. By providing a RESTful API that can access triple data, we allow users to query a dataset through a simple interface based on just a couple of HTTP parameters. If RDF resources were published this way we could quickly build applications that depend on these datasets, without having to download and process them locally. This is what Restpark is: a set of HTTP GET parameters that servers need to handle, and respond with JSON-LD.
Optimal Switching Synthesis for Jump Linear Systems with Gaussian initial state uncertainty
Venue: arXiv cs.SY 2014
Year: 2014

This paper provides a method to design an optimal switching sequence for jump linear systems with given Gaussian initial state uncertainty. In the practical perspective, the initial state contains some uncertainties that come from measurement errors or sensor inaccuracies and we assume that the type of this uncertainty has the form of Gaussian distribution. In order to cope with Gaussian initial state uncertainty and to measure the system performance, Wasserstein metric that defines the distance between probability density functions is used. Combining with the receding horizon framework, an optimal switching sequence for jump linear systems can be obtained by minimizing the objective function that is expressed in terms of Wasserstein distance. The proposed optimal switching synthesis also guarantees the mean square stability for jump linear systems. The validations of the proposed methods are verified by examples.
Balanced Circle Packings for Planar Graphs
Venue: arXiv cs.CG 2014
Year: 2014

We study balanced circle packings and circle-contact representations for planar graphs, where the ratio of the largest circle's diameter to the smallest circle's diameter is polynomial in the number of circles. We provide a number of positive and negative results for the existence of such balanced configurations.
Computing Multiplicative Order and Primitive Root in Finite Cyclic Group
Venue: arXiv cs.SC 2014
Year: 2014

Multiplicative order of an element $a$ of group $G$ is the least positive integer $n$ such that $a^n=e$, where $e$ is the identity element of $G$. If the order of an element is equal to $|G|$, it is called generator or primitive root. This paper describes the algorithms for computing multiplicative order and primitive root in $\mathbb{Z}^*_{p}$, we also present a logarithmic improvement over classical algorithms.
The Empirical Commit Frequency Distribution of Open Source Projects
Venue: arXiv cs.SE 2014
Year: 2014

A fundamental unit of work in programming is the code contribution ("commit") that a developer makes to the code base of the project in work. An author's commit frequency describes how often that author commits. Knowing the distribution of all commit frequencies is a fundamental part of understanding software development processes. This paper presents a detailed quantitative analysis of commit frequencies in open-source software development. The analysis is based on a large sample of open source projects, and presents the overall distribution of commit frequencies. We analyze the data to show the differences between authors and projects by project size; we also includes a comparison of successful and non successful projects and we derive an activity indicator from these analyses. By measuring a fundamental dimension of programming we help improve software development tools and our understanding of software development. We also validate some fundamental assumptions about software development.
Asymptotic bounds on the equilateral dimension of hypercubes
Venue: arXiv cs.DM 2014
Year: 2014

A subset of the finite dimensional hypercube is said to be equilateral if the distance of any two distinct points equals a fixed value. The equilateral dimension of the hypercube is defined as the maximal size of its equilateral subsets. We study asymptotic bounds on the latter quantity considered as a function of two variables, namely dimension and distance.
Upper Bounds and Duality Relations of the Linear Deterministic Sum Capacity for Cellular Systems
Venue: arXiv cs.IT 2014
Year: 2014

The MAC-BC duality of information theory and wireless communications is an intriguing concept for efficient algorithm design. However, no concept is known so far for the important cellular channel. To make progress on this front, we consider in this paper the linear deterministic cellular channel. In particular, we prove duality of a network with two interfering MACs in each cell and a network with two interfering BCs in each cell. The operational region is confined to the weak interference regime. First, achievable schemes as well as upper bounds will be provided. These bounds are the same for both channels. We will show, that for specific cases the upper bound corresponds to the achievable scheme and hence establishing a duality relationship between them.
Caffe: Convolutional Architecture for Fast Feature Embedding
Venue: arXiv cs.CV 2014
Year: 2014

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU ($\approx$ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.
Verifiable UML Artifact-Centric Business Process Models (Extended Version)
Venue: arXiv cs.DB 2014
Year: 2014

Artifact-centric business process models have gained increasing momentum recently due to their ability to combine structural (i.e., data related) with dynamical (i.e., process related) aspects. In particular, two main lines of research have been pursued so far: one tailored to business artefact modeling languages and methodologies, the other focused on the foundations for their formal verification. In this paper, we merge these two lines of research, by showing how recent theoretical decidability results for verification can be fruitfully transferred to a concrete UML-based modeling methodology. In particular, we identify additional steps in the methodology that, in significant cases, guarantee the possibility of verifying the resulting models against rich first-order temporal properties. Notably, our results can be seamlessly transferred to different languages for the specification of the artifact lifecycles.
Hybrid Decision Algorithm for Access Selection in Multi-operator Networks
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we propose a hybrid decision algorithm for the selection of the access in multi-operator networks environment, where competing operators share their radio access networks to meet traffic and data rate demands. The proposed algorithm guarantees the user satisfaction and a global gain for all cooperating operators. Simulation results prove the efficiency of the proposed scheme and show that the cooperation between operators achieves benefits to both users and operators; user acceptance as well as the operator resource utilization and the operator revenue increase.
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
Venue: arXiv cs.CV 2014
Year: 2014

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.
Evolving Modular Genetic Regulatory Networks with a Recursive, Top-Down Approach
Venue: arXiv cs.CE 2014
Year: 2014

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
High-Level Synthesis Case Study: Implementation of a Memcached Server
Venue: arXiv cs.OH 2014
Year: 2014

High-Level Synthesis (HLS) aspires to raise the level of abstraction in hardware design without sacrificing hardware efficiency. It has so far been successfully employed in signal and video processing but has found only limited use in other areas. This paper utilizes a commercial HLS tool, namely Vivado(R) HLS, to implement the processing of a common data center application, the Key-Value Store (KVS) application memcached, as a deeply pipelined dataflow architecture. We compared our results to a fully equivalent RTL implementation done previously in our group and found that it matches its performance, yields tangible improvements in latency (between 7-30%) and resource consumption (22% in LUTs and 35% in registers), all while requiring 3x less lines of code and 2x less development time. The implementation was validated in hardware on a Xilinx(R) VC709 development board, meeting timing requirements for 10Gbps line rate processing.
Computing Multi-Relational Sufficient Statistics for Large Databases
Venue: arXiv cs.LG 2014
Year: 2014

Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficient statistics that combine information from different database tables. Such statistics may involve any number of {\em positive and negative} relationships. With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases. We solve this problem with a new dynamic programming algorithm that performs a virtual join, where the requisite counts are computed without materializing join tables. Contingency table algebra is a new extension of relational algebra, that facilitates the efficient implementation of this Möbius virtual join operation. The Möbius Join scales to large datasets (over 1M tuples) with complex schemas. Empirical evaluation with seven benchmark datasets showed that information about the presence and absence of links can be exploited in feature selection, association rule mining, and Bayesian network learning.
Analysis of String Sorting using Heapsort
Venue: arXiv cs.DS 2014
Year: 2014

In this master thesis we analyze the complexity of sorting a set of strings. It was shown that the complexity of sorting strings can be naturally expressed in terms of the prefix trie induced by the set of strings. The model of computation takes into account symbol comparisons and not just comparisons between the strings. The analysis of upper and lower bounds for some classical algorithms such as Quicksort and Mergesort in terms of such a model was shown. Here we extend the analysis to another classical algorithm - Heapsort. We also give analysis for the version of the algorithm that uses Binomial heaps as a heap implementation.
Soft Neutrosophic Algebraic Structures and Their Generalization
Venue: arXiv cs.AI 2014
Year: 2014

Study of soft sets was first proposed by Molodtsov in 1999 to deal with uncertainty in a non-parametric manner. The researchers did not pay attention to soft set theory at that time but now the soft set theory has been developed in many areas of mathematics. Algebraic structures using soft set theory are very rapidly developed. In this book we developed soft neutrosophic algebraic structures by using soft sets and neutrosophic algebraic structures. In this book we study soft neutrosophic groups, soft neutrosophic semigroups, soft neutrosophic loops, soft neutrosophic LA-semigroups, and their generalizations respectively.
Validation of the development methodologies
Venue: arXiv cs.SE 2014
Year: 2014

This paper argues that modelling the development methodologies can improve the multi-agents systems software engineering. Such modelling allows applying methods, techniques and practices used in the software development to the methodologies themselves. The paper discusses then the advantages of the modelling of development methodologies. It describes a model of development methodologies, uses such a model to develop a system of their partial validation, and applies such a system to multi-agent methodologies. Several benefits can be gained from such modelling, such as the improvement of the works on the development, evaluation and comparison of multi-agent development methodologies.
Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation
Venue: arXiv cs.CV 2014
Year: 2014

Hierarchies allow feature sharing between objects at multiple levels of representation, can code exponential variability in a very compact way and enable fast inference. This makes them potentially suitable for learning and recognizing a higher number of object classes. However, the success of the hierarchical approaches so far has been hindered by the use of hand-crafted features or predetermined grouping rules. This paper presents a novel framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes. The approach takes simple contour fragments and learns their frequent spatial configurations. These are recursively combined into increasingly more complex and class-specific shape compositions, each exerting a high degree of shape variability. At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects. We learn the vocabulary layer after layer, by gradually increasing the size of the window of analysis and reducing the spatial resolution at which the shape configurations are learned. The lower layers are learned jointly on images of all classes, whereas the higher layers of the vocabulary are learned incrementally, by presenting the algorithm with one object class after another. The experimental results show that the learned multi-class object representation scales favorably with the number of object classes and achieves a state-of-the-art detection performance at both, faster inference as well as shorter training times.
Faster construction of asymptotically good unit-cost error correcting codes in the RAM model
Venue: arXiv cs.DS 2014
Year: 2014

Assuming we are in a Word-RAM model with word size $w$, we show that we can construct in $o(w)$ time an error correcting code with a constant relative positive distance that maps numbers of $w$ bits into $Θ(w)$-bit numbers, and such that the application of the error-correcting code on any given number $x\in[0,2^w-1]$ takes constant time. Our result improves on a previously proposed error-correcting code with the same properties whose construction time was exponential in $w$.
IPED2: Inheritance Path based Pedigree Reconstruction Algorithm for Complicated Pedigrees
Venue: arXiv cs.DS 2014
Year: 2014

Reconstruction of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. The problem is known to be NP-hard even for datasets known to only contain siblings. Some recent methods have been developed to accurately and efficiently reconstruct pedigrees. These methods, however, still consider relatively simple pedigrees, for example, they are not able to handle half-sibling situations where a pair of individuals only share one parent. In this work, we propose an efficient method, IPED2, based on our previous work, which specifically targets reconstruction of complicated pedigrees that include half-siblings. We note that the presence of half-siblings makes the reconstruction problem significantly more challenging which is why previous methods exclude the possibility of half-siblings. We proposed a novel model as well as an efficient graph algorithm and experiments show that our algorithm achieves relatively accurate reconstruction. To our knowledge, this is the first method that is able to handle pedigree reconstruction based on genotype data only when half-sibling exists in any generation of the pedigree.
Restricted Conformal Property of Compressive Sensing
Venue: arXiv cs.IT 2014
Year: 2014

Energy and direction are tow basic properties of a vector. A discrete signal is a vector in nature. RIP of compressive sensing can not show the direction information of a signal but show the energy information of a signal. Hence, RIP is not complete. Orthogonal matrices can preserve angles and lengths. Preservation of length can show energies of signals like RIP do; and preservation of angle can show directions of signals. Therefore, Restricted Conformal Property (RCP) is proposed according to preservation of angle. RCP can show the direction of a signal just as RIP shows the energy of a signal. RCP is important supplement and development of RIP. Tow different proofs of RCP are given, namely, RCP_JL and RCP_IP.
Fuzzy and entropy facial recognition
Venue: arXiv cs.CV 2014
Year: 2014

This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy eliminates the complication of other methods. Shannon entropy calculates the ratio of an element between faces, and fuzzy theory calculates the member ship of the entropy with 1. More details will be mentioned in Section 3. The learning performance is better than others as it is very simple, and only need two data per learning. By using factors that don't usually change during the life, the method will have a high accuracy.
Quantitative Analysis of Genealogy Using Digitised Family Trees
Venue: arXiv cs.SI 2014
Year: 2014

Driven by the popularity of television shows such as Who Do You Think You Are? many millions of users have uploaded their family tree to web projects such as WikiTree. Analysis of this corpus enables us to investigate genealogy computationally. The study of heritage in the social sciences has led to an increased understanding of ancestry and descent but such efforts are hampered by difficult to access data. Genealogical research is typically a tedious process involving trawling through sources such as birth and death certificates, wills, letters and land deeds. Decades of research have developed and examined hypotheses on population sex ratios, marriage trends, fertility, lifespan, and the frequency of twins and triplets. These can now be tested on vast datasets containing many billions of entries using machine learning tools. Here we survey the use of genealogy data mining using family trees dating back centuries and featuring profiles on nearly 7 million individuals based in over 160 countries. These data are not typically created by trained genealogists and so we verify them with reference to third party censuses. We present results on a range of aspects of population dynamics. Our approach extends the boundaries of genealogy inquiry to precise measurement of underlying human phenomena.