OpenRaiser/Intern-Atlas huggingface dataset:
iGPSe: A Visual Analytic System for Integrative Genomic Based Cancer Patient Stratification
Venue: arXiv cs.GR 2014
Year: 2014

Background: Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Results: In this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction. Conclusions: Visualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.
Visualization of gene expression information within the context of the mouse anatomy
Venue: arXiv cs.HC 2014
Year: 2014

Background: The eMouse Atlas of Gene Expression (EMAGE) is an online resource that publishes the results of in situ gene expression experiments on the developmental mouse. The resource provides comprehensive search facilities, but few analytical tools or visual mechanisms for navigating the data set. To deal with the missing visual navigation, this paper explores the application of sunburst and icicle visualizations within EMAGE. Results: A prototype solution delivered a simple user interface that helps the user query EMAGE and generate a sunburst/icicle diagram. An evaluation featuring test subjects from the EMAGE staff studied the visualizations and provided a range of suggested improvements. Moreover the evaluation discovered that in addition to providing a visual means of walking through the data, when grouped, the sunburst delivers an interactive overview that assists with analysing sets of related genes. Conclusions: The sunburst and icicle visualizations have been shown to be effective tools for summarising gene expression data. The sunburst with its space saving radial layout was found especially useful for providing an overview of gene families or pathways. Work is ongoing to integrate these visualizations into EMAGE.
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Venue: arXiv cs.MA 2014
Year: 2014

Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context.
Is Fortran Still Relevant? Comparing Fortran with Java and C++
Venue: arXiv cs.PL 2014
Year: 2014

This paper presents a comparative study to evaluate and compare Fortran with the two most popular programming languages Java and C++. Fortran has gone through major and minor extensions in the years 2003 and 2008. (1) How much have these extensions made Fortran comparable to Java and C++? (2) What are the differences and similarities, in supporting features like: Templates, object constructors and destructors, abstract data types and dynamic binding? These are the main questions we are trying to answer in this study. An object-oriented ray tracing application is implemented in these three languages to compare them. By using only one program we ensured there was only one set of requirements thus making the comparison homogeneous. Based on our literature survey this is the first study carried out to compare these languages by applying software metrics to the ray tracing application and comparing these results with the similarities and differences found in practice. We motivate the language implementers and compiler developers, by providing binary analysis and profiling of the application, to improve Fortran object handling and processing, and hence making it more prolific and general. This study facilitates and encourages the reader to further explore, study and use these languages more effectively and productively, especially Fortran.
Energy-Delay Tradeoffs of Virtual Base Stations With a Computational-Resource-Aware Energy Consumption Model
Venue: arXiv cs.NI 2014
Year: 2014

The next generation (5G) cellular network faces the challenges of efficiency, flexibility, and sustainability to support data traffic in the mobile Internet era. To tackle these challenges, cloud-based cellular architectures have been proposed where virtual base stations (VBSs) play a key role. VBSs bring further energy savings but also demands a new energy consumption model as well as the optimization of computational resources. This paper studies the energy-delay tradeoffs of VBSs with delay tolerant traffic. We propose a computational-resource-aware energy consumption model to capture the total energy consumption of a VBS and reflect the dynamic allocation of computational resources including the number of CPU cores and the CPU speed. Based on the model, we analyze the energy-delay tradeoffs of a VBS considering BS sleeping and state switching cost to minimize the weighted sum of power consumption and average delay. We derive the explicit form of the optimal data transmission rate and find the condition under which the energy optimal rate exists and is unique. Opportunities to reduce the average delay and achieve energy savings simultaneously are observed. We further propose an efficient algorithm to jointly optimize the data rate and the number of CPU cores. Numerical results validate our theoretical analyses and under a typical simulation setting we find more than 60% energy savings can be achieved by VBSs compared with conventional base stations under the EARTH model, which demonstrates the great potential of VBSs in 5G cellular systems.
XRay: Enhancing the Web's Transparency with Differential Correlation
Venue: arXiv cs.NI 2014
Year: 2014

Today's Web services - such as Google, Amazon, and Facebook - leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay, the first fine-grained, robust, and scalable personal data tracking system for the Web. XRay predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay's core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.
Hands-Off Control as Green Control
Venue: arXiv cs.SY 2014
Year: 2014

In this article, we introduce a new paradigm of control, called hands-off control, which can save energy and reduce CO2 emissions in control systems. A hands-off control is defined as a control that has a much shorter support than the horizon length. The maximum hands-off control is the minimum support (or sparsest) control among all admissible controls. With maximum hands-off control, actuators in the feedback control system can be stopped during time intervals over which the control values are zero. We show the maximum hands-off control is given by L1 optimal control, for which we also show numerical computation formulas.
Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model
Venue: arXiv cs.CV 2014
Year: 2014

The work describes the development of Online Assamese Stroke & Akshara Recognizer based on a set of language rules. In handwriting literature strokes are composed of two coordinate trace in between pen down and pen up labels. The Assamese aksharas are combination of a number of strokes, the maximum number of strokes taken to make a combination being eight. Based on these combinations eight language rule models have been made which are used to test if a set of strokes form a valid akshara. A Hidden Markov Model is used to train 181 different stroke patterns which generates a model used during stroke level testing. Akshara level testing is performed by integrating a GUI (provided by CDAC-Pune) with the Binaries of HTK toolkit classifier, HMM train model and the language rules using a dynamic linked library (dll). We have got a stroke level performance of 94.14% and akshara level performance of 84.2%.
Review of monitoring tools for e-learning platforms
Venue: arXiv cs.CY 2014
Year: 2014

The advancement of e-learning technologies has made it viable for developments in education and technology to be combined in order to fulfil educational needs worldwide. E-learning consists of informal learning approaches and emerging technologies to support the delivery of learning skills, materials, collaboration and knowledge sharing. E-learning is a holistic approach that covers a wide range of courses, technologies and infrastructures to provide an effective learning environment. The Learning Management System (LMS) is the core of the entire e-learning process along with technology, content, and services. This paper investigates the role of model-driven personalisation support modalities in providing enhanced levels of learning and trusted assimilation in an e-learning delivery context. We present an analysis of the impact of an integrated learning path that an e-learning system may employ to track activities and evaluate the performance of learners.
Almost Disjunctive List-Decoding Codes
Venue: arXiv cs.IT 2014
Year: 2014

A binary code is said to be a disjunctive list-decoding $s_L$-code, $s\ge1$, $L\ge1$, (briefly, LD $s_L$-code) if the code is identified by the incidence matrix of a family of finite sets in which the union of any $s$ sets can cover not more than $L-1$ other sets of the family. In this paper, we introduce a natural {\em probabilistic} generalization of LD $s_L$-code when the code is said to be an almost disjunctive LD $s_L$-code if the unions of {\em almost all} $s$ sets satisfy the given condition. We develop a random coding method based on the ensemble of binary constant-weight codes to obtain lower bounds on the capacity and error probability exponent of such codes. For the considered ensemble our lower bounds are asymptotically tight.
Sampling Strategies for Path Planning under Kinematic Constraints
Venue: arXiv cs.RO 2014
Year: 2014

A well-known weakness of the probabilistic path planners is the so-called narrow passage problem, where a region with a relatively low probability of being sampled must be explored to find a solution path. Many strategies have been proposed to alleviate this problem, most of them based on biasing the sampling distribution. When kinematic constraints appear in the problem, the configuration space typically becomes a non-parametrizable, implicit manifold. Unfortunately, this invalidates most of the existing sampling bias approaches, which rely on an explicit parametrization of the space to explore. In this paper, we propose and evaluate three novel strategies to bias the sampling under the presence of narrow passages in kinematically-constrained systems.
Plurality Consensus in the Gossip Model
Venue: arXiv cs.DC 2014
Year: 2014

We study Plurality Consensus in the Gossip Model over a network of $n$ anonymous agents. Each agent supports an initial opinion or color. We assume that at the onset, the number of agents supporting the plurality color exceeds that of the agents supporting any other color by a sufficiently-large bias. The goal is to provide a protocol that, with high probability, brings the system into the configuration in which all agents support the (initial) plurality color. We consider the Undecided-State Dynamics, a well-known protocol which uses just one more state (the undecided one) than those necessary to store colors. We show that the speed of convergence of this protocol depends on the initial color configuration as a whole, not just on the gap between the plurality and the second largest color community. This dependence is best captured by a novel notion we introduce, namely, the monochromatic distance ${md}(\bar{\mathbf{c}})$ which measures the distance of the initial color configuration $\bar{ \mathbf {c}}$ from the closest monochromatic one. In the complete graph, we prove that, for a wide range of the input parameters, this dynamics converges within $O({md}(\bar {\mathbf {c}}) \log {n})$ rounds. We prove that this upper bound is almost tight in the strong sense: Starting from any color configuration $\bar {\mathbf {c}}$, the convergence time is $Ω({md}(\bar {\mathbf {c}}))$. Finally, we adapt the Undecided-State Dynamics to obtain a fast, random walk-based protocol for plurality consensus on regular expanders. This protocol converges in $O({md}(\bar {\mathbf {c}}) \mathrm{polylog}(n))$ rounds using only $\mathrm{polylog}(n)$ local memory. A key-ingredient to achieve the above bounds is a new analysis of the maximum node congestion that results from performing $n$ parallel random walks on regular expanders. All our bounds hold with high probability.
Genetic Algorithm in Audio Steganography
Venue: arXiv cs.MM 2014
Year: 2014

With the advancement of communication technology,data is exchanged digitally over the network. At the other side the technology is also proven as a tool for unauthorized access to attackers. Thus the security of data to be transmitted digitally should get prime focus. Data hiding is the common approach to secure data. In steganography technique, the existence of data is concealed. GA is an emerging component of AI to provide suboptimal solutions. In this paper the use of GA in Steganography is explored to find future scope of research.
Improving energy efficiency in MANET's for healthcare environments
Venue: arXiv cs.NI 2014
Year: 2014

Now a day ad hoc mobile networks (MANETs) have lots of routing protocols, but no one can meet maximum performance. Some are good in a small network; some are suitable in large networks, and some give better performance in location or global networks. Today modern and innovative applications for health care environments based on a wireless network are being developed in the commercial sectors. The emerging wireless networks are rapidly becoming a fundamental part of every single field of life. Our proposed DEERP framework gives a better performance as compared to other routing protocol.
Rigorous uniform approximation of D-finite functions using Chebyshev expansions
Venue: arXiv cs.SC 2014
Year: 2014

A wide range of numerical methods exists for computing polynomial approximations of solutions of ordinary differential equations based on Chebyshev series expansions or Chebyshev interpolation polynomials. We consider the application of such methods in the context of rigorous computing (where we need guarantees on the accuracy of the result), and from the complexity point of view. It is well-known that the order-n truncation of the Chebyshev expansion of a function over a given interval is a near-best uniform polynomial approximation of the function on that interval. In the case of solutions of linear differential equations with polynomial coefficients, the coefficients of the expansions obey linear recurrence relations with polynomial coefficients. Unfortunately, these recurrences do not lend themselves to a direct recursive computation of the coefficients, owing among other things to a lack of initial conditions. We show how they can nevertheless be used, as part of a validated process, to compute good uniform approximations of D-finite functions together with rigorous error bounds, and we study the complexity of the resulting algorithms. Our approach is based on a new view of a classical numerical method going back to Clenshaw, combined with a functional enclosure method.
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View
Venue: arXiv cs.IR 2014
Year: 2014

Intelligent recommendation technology has been playing an increasingly important role in various industry applications such as e-commerce product promotion and Internet advertisement display. Besides users' feedbacks (e.g., numerical ratings) on items as usually exploited by some typical recommendation algorithms, there are often some additional data such as users' social circles and other behaviors. Such auxiliary data are usually related to users' preferences on items behind the numerical ratings. Collaborative recommendation with auxiliary data (CRAD) aims to leverage such additional information so as to improve the personalization services, which have received much attention from both researchers and practitioners. Transfer learning (TL) is proposed to extract and transfer knowledge from some auxiliary data in order to assist the learning task on some target data. In this paper, we consider the CRAD problem from a transfer learning view, especially on how to achieve knowledge transfer from some auxiliary data. First, we give a formal definition of transfer learning for CRAD (TL-CRAD). Second, we extend the existing categorization of TL techniques (i.e., adaptive, collective and integrative knowledge transfer algorithm styles) with three knowledge transfer strategies (i.e., prediction rule, regularization and constraint). Third, we propose a novel generic knowledge transfer framework for TL-CRAD. Fourth, we describe some representative works of each specific knowledge transfer strategy of each algorithm style in detail, which are expected to inspire further works. Finally, we conclude the paper with some summary discussions and several future directions.
h-index Research in Scientometrics: A Summary
Venue: arXiv cs.DL 2014
Year: 2014

A Letter to the Editor shortly summing up ten or so years of research into the h-index.
On the Convergence of the Mean Shift Algorithm in the One-Dimensional Space
Venue: arXiv cs.CV 2014
Year: 2014

The mean shift algorithm is a non-parametric and iterative technique that has been used for finding modes of an estimated probability density function. It has been successfully employed in many applications in specific areas of machine vision, pattern recognition, and image processing. Although the mean shift algorithm has been used in many applications, a rigorous proof of its convergence is still missing in the literature. In this paper we address the convergence of the mean shift algorithm in the one-dimensional space and prove that the sequence generated by the mean shift algorithm is a monotone and convergent sequence.
Achieving the Uniform Rate Region of General Multiple Access Channels by Polar Coding
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of polar coding for transmission over $m$-user multiple access channels. In the proposed scheme, all users encode their messages using a polar encoder, while a joint successive cancellation decoder is deployed at the receiver. The encoding is done separately across the users and is independent of the target achievable rate, in the sense that the encoder core is the regular Arıkan's polarization matrix. For the code construction, the positions of information bits and frozen bits for each of the users are decided jointly. This is done by treating the whole polar transformation across all the $m$ users as a single polar transformation with a certain base code. We prove that the covering radius of the dominant face of the uniform rate region is upper bounded by $r = \frac{(m-1)\sqrt{m}}{L}$, where $L$ represents the length of the base code. We then prove that the proposed polar coding scheme achieves the whole uniform rate region, with small enough resolution characterized by $r$, by changing the decoding order in the joint successive cancellation decoder. The encoding and decoding complexities are $O(N \log N)$, where $N$ is the code block length, and the asymptotic block error probability of $O(2^{-N^{0.5 - ε}})$ is guaranteed. Examples of achievable rates for the case of $3$-user multiple access channel are provided.
Nearly Linear-Work Algorithms for Mixed Packing/Covering and Facility-Location Linear Programs
Venue: arXiv cs.DS 2014
Year: 2014

We describe the first nearly linear-time approximation algorithms for explicitly given mixed packing/covering linear programs, and for (non-metric) fractional facility location. We also describe the first parallel algorithms requiring only near-linear total work and finishing in polylog time. The algorithms compute $(1+ε)$-approximate solutions in time (and work) $O^*(N/ε^2)$, where $N$ is the number of non-zeros in the constraint matrix. For facility location, $N$ is the number of eligible client/facility pairs.
An SVM Based Approach for Cardiac View Planning
Venue: arXiv cs.LG 2014
Year: 2014

We consider the problem of automatically prescribing oblique planes (short axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging (MRI). A concern with technologist-driven acquisitions of these planes is the quality and time taken for the total examination. We propose an automated solution incorporating anatomical features external to the cardiac region. The solution uses support vector machine regression models wherein complexity and feature selection are optimized using multi-objective genetic algorithms. Additionally, we examine the robustness of our approach by training our models on images with additive Rician-Gaussian mixtures at varying Signal to Noise (SNR) levels. Our approach has shown promising results, with an angular deviation of less than 15 degrees on 90% cases across oblique planes, measured in terms of average 6-fold cross validation performance -- this is generally within acceptable bounds of variation as specified by clinicians.
Distributed Compressed Sensing off the Grid
Venue: arXiv cs.IT 2014
Year: 2014

This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in $\left\lbrack 0,1 \right\rbrack$. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.