OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Venue: arXiv cs.CV 2016
Year: 2016

Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
Partial Membership Latent Dirichlet Allocation
Venue: arXiv cs.CV 2016
Year: 2016

Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership latent Dirichlet allocation (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.
Robust Stabilization of Linear Plants Under Uncertainties and High-Frequency Measurement Noises
Venue: arXiv cs.SY 2016
Year: 2016

The paper describes the robust algorithm for linear time-invariant plants under parametric uncertainties, external disturbances and high-frequency noises in measurements. The proposed algorithm allows one to reduce the noise impact on the output variable of the plant and to compensate parametric uncertainties and external disturbances independently. The modeling results illustrate the effectiveness of the algorithm.
Condensedly: comprehending article contents through condensed texts
Venue: arXiv cs.IR 2016
Year: 2016

Summary: Abstracts in biomedical articles can provide a quick overview of the articles but detailed information cannot be obtained without reading full-text contents. Full-text articles certainly generate more information and contents; however, accessing full-text documents is usually time consuming. Condensedly is a web-based application, which provides readers an easy and efficient way to access full-text paragraphs using sentences in abstracts as fishing bait to retrieve the big fish reside in full-text. Condensedly is based on the paragraph ranking algorithm, which evaluates and ranks full-text paragraphs based on their association scores with sentences in abstracts. Availability: http://140.116.247.185/~research/Condensedly
The interplay between system identification and machine learning
Venue: arXiv cs.SY 2016
Year: 2016

Learning from examples is one of the key problems in science and engineering. It deals with function reconstruction from a finite set of direct and noisy samples. Regularization in reproducing kernel Hilbert spaces (RKHSs) is widely used to solve this task and includes powerful estimators such as regularization networks. Recent achievements include the proof of the statistical consistency of these kernel- based approaches. Parallel to this, many different system identification techniques have been developed but the interaction with machine learning does not appear so strong yet. One reason is that the RKHSs usually employed in machine learning do not embed the information available on dynamic systems, e.g. BIBO stability. In addition, in system identification the independent data assumptions routinely adopted in machine learning are never satisfied in practice. This paper provides new results which strengthen the connection between system identification and machine learning. Our starting point is the introduction of RKHSs of dynamic systems. They contain functionals over spaces defined by system inputs and allow to interpret system identification as learning from examples. In both linear and nonlinear settings, it is shown that this perspective permits to derive in a relatively simple way conditions on RKHS stability (i.e. the property of containing only BIBO stable systems or predictors), also facilitating the design of new kernels for system identification. Furthermore, we prove the convergence of the regularized estimator to the optimal predictor under conditions typical of dynamic systems.
Verifying Heaps' law using Google Books Ngram data
Venue: arXiv cs.CL 2016
Year: 2016

This article is devoted to the verification of the empirical Heaps law in European languages using Google Books Ngram corpus data. The connection between word distribution frequency and expected dependence of individual word number on text size is analysed in terms of a simple probability model of text generation. It is shown that the Heaps exponent varies significantly within characteristic time intervals of 60-100 years.
Joint Channel Estimation and Nonlinear Distortion Compensation in OFDM Receivers
Venue: arXiv cs.IT 2016
Year: 2016

Nonlinear distortion in power amplifiers (PA) can significantly degrade performance of orthogonal frequency division multiplexed (OFDM) communication systems. This paper presents a joint maximum-likelihood channel frequency response and nonlinear PA model estimator for OFDM signals. Derivation of the estimator is based on Taylor-series representation of power amplifier nonlinearity and is suitable for wide range of memoryless PA models. A sub-optimal decision-aided algorithm for adaptive compensation of nonlinear distortion effects at the receiver-side is also presented. It is shown that the proposed algorithms can be used in IEEE 802.11a/g/p/ac compliant wireless LAN receivers without any modifications at the transmitter side. The performance of the proposed algorithms is studied by means of computer simulation.
Parallel Algorithms for Core Maintenance in Dynamic Graphs
Venue: arXiv cs.DS 2016
Year: 2016

This paper initiates the studies of parallel algorithms for core maintenance in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which are widely used in large-scale graph analytics. The core maintenance problem requires to update the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. We investigate the parallelism in the core update process when multiple edges and vertices are inserted or deleted. Specifically, we discover a structure called superior edge set, the insertion or deletion of edges in which can be processed in parallel. Based on the structure of superior edge set, efficient parallel algorithms are then devised for incremental and decremental core maintenance respectively. To the best of our knowledge, the proposed algorithms are the first parallel ones for the fundamental core maintenance problem. The algorithms show a significant speedup in the processing time compared with previous results that sequentially handle edge and vertex insertions/deletions. Finally, extensive experiments are conducted on different types of real-world and synthetic datasets, and the results illustrate the efficiency, stability and scalability of the proposed algorithms.
Set membership with non-adaptive bit probes
Venue: arXiv cs.DS 2016
Year: 2016

We consider the non-adaptive bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form "Is x in S?" by non-adaptively probing the bit vector at t places. Let s_N(m,n,t) be the minimum number of bits of storage needed for such a scheme. In this work, we show existence of non-adaptive and adaptive schemes for a range of t that improves an upper bound of Buhrman, Miltersen, Radhakrishnan and Srinivasan (2002) on s_N(m,n,t). For three non-adaptive probes, we improve the previous best lower bound on s_N(m,n,3) by Alon and Feige (2009).
Automatically generating features for learning program analysis heuristics
Venue: arXiv cs.PL 2016
Year: 2016

We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been proposed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the analysis designers, but they do not remove it completely; they still leave the highly nontrivial task of designing so called features to the hands of the designers. Our technique automates this feature design process. The idea is to use programs as features after reducing and abstracting them. Our technique goes through selected program-query pairs in codebases, and it reduces and abstracts the program in each pair to a few lines of code, while ensuring that the analysis behaves similarly for the original and the new programs with respect to the query. Each reduced program serves as a boolean feature for program-query pairs. This feature evaluates to true for a given program-query pair when (as a program) it is included in the program part of the pair. We have implemented our approach for three real-world program analyses. Our experimental evaluation shows that these analyses with automatically-generated features perform comparably to those with manually crafted features.
Two factor authentication using EEG augmented passwords
Venue: arXiv cs.CR 2016
Year: 2016

The current research with EEG devices in the user authentication context has some deficiencies that address expensive equipment, the requirement of laboratory conditions and applicability. In this paper we address this issue by using widely available and inexpensive EEG device to verify its capability for authentication. As a part of this research, we developed two phase authentication that enables users to enhance their password with the mental state by breaking the password into smaller, marry them with mental state, and generate one time pad for a secure session.
Posting with credibility in Micro-blogging systems using Digital Signatures and Watermarks: A case study on Twitter
Venue: arXiv cs.SI 2016
Year: 2016

Micro-blogs are contemporary broadcasting services for exchanging small elements of content, including video and images. Despite its popularity, micro-blogging is not without issues. So far, various security concerns, such as: privacy and confidentiality of micro-blogging systems have attracted the interest of the scientific community. Nevertheless, in this document we refer to a security issue that is concerned with the posting and circulation of fake messages. With the aim to make micro-blogs a credible source of information, in this work we propose a protocol, as a solution, that can be easily adapted to existing services such as: Twitter and Facebook. With the main keys of the solution being the employment of digital signatures and watermarks, the protocol can serve postings of various types of content. Finally, the solution serves the dual purpose of proofing the fake messages as well as the repudiation of postings.
The $(n,m,k,λ)$-Strong External Difference Family with $m \geq 5$ Exists
Venue: arXiv cs.IT 2016
Year: 2016

The notion of strong external difference family (SEDF) in a finite abelian group $(G,+)$ is raised by M. B. Paterson and D. R. Stinson [5] in 2016 and motivated by its application in communication theory to construct $R$-optimal regular algebraic manipulation detection code. A series of $(n,m,k,λ)$-SEDF's have been constructed in [5, 4, 2, 1] with $m=2$. In this note we present an example of (243, 11, 22, 20)-SEDF in finite field $\mathbb{F}_q$ $(q=3^5=243).$ This is an answer for the following problem raised in [5] and continuously asked in [4, 2, 1]: if there exists an $(n,m,k,λ)$-SEDF for $m\geq 5$.
Optimal Transmission Policies for Multi-hop Energy Harvesting Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider a multi-hop energy harvesting (EH) communication system in a full-duplex mode, where arrival data and harvested energy curves in the source and the relays are modeled as general functions. This model includes the EH system with discrete arrival processes as a special case. We investigate the throughput maximization problem considering minimum utilized energy in the source and relays and find the optimal offline algorithm. We show that the optimal solution of the two-hop transmission problem have three main steps: (i) Solving a point-to-point throughput maximization problem at the source; (ii) Solving a point-to-point throughput maximization problem at the relay (after applying the solution of first step as the input of this second problem); (iii) Minimizing utilized energy in the source. In addition, we show that how the optimal algorithm for the completion time minimization problem can be derived from the proposed algorithm for throughput maximization problem. Also, for the throughput maximization problem, we propose an online algorithm and show that it is more efficient than the benchmark one (which is a direct application of an existing point-to-point online algorithm to the multi-hop system).
Technical Report: Optimal Surveillance of Dynamic Parades using Teams of Aerial Robots
Venue: arXiv cs.RO 2016
Year: 2016

This technical report addresses the problem of optimal surveillance of the route followed by a dynamic parade using a team of aerial robots. The dynamic parade is considered to take place within an urban environment, it is discretized and at every iteration, the algorithm computes the best possible placing of the aerial robotic team members, subject to their camera model and the occlusions arising from the environment. As the parade route is only as well covered as its least-covered point, the optimization objective is to place the aerial robots such that they maximize the minimum coverage over the points in the route at every time instant of it. A set of simulation studies is used to demonstrate the operation and performance characteristics of the approach, while computational analysis is also provided and verifies the good scalability properties of the contributed algorithm regarding the size of the aerial robotics team.
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
Venue: arXiv cs.CV 2016
Year: 2016

Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual-reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a new automatically annotated and augmented dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes.
Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge
Venue: arXiv cs.LG 2016
Year: 2016

This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.
Bounds on parameters of minimally non-linear patterns
Venue: arXiv cs.DM 2016
Year: 2016

Let $ex(n, P)$ be the maximum possible number of ones in any 0-1 matrix of dimensions $n \times n$ that avoids $P$. Matrix $P$ is called minimally non-linear if $ex(n, P) = ω(n)$ but $ex(n, P') = O(n)$ for every strict subpattern $P'$ of $P$. We prove that the ratio between the length and width of any minimally non-linear 0-1 matrix is at most $4$, and that a minimally non-linear 0-1 matrix with $k$ rows has at most $5k-3$ ones. We also obtain an upper bound on the number of minimally non-linear 0-1 matrices with $k$ rows. In addition, we prove corresponding bounds for minimally non-linear ordered graphs. The minimal non-linearity that we investigate for ordered graphs is for the extremal function $ex_{<}(n, G)$, which is the maximum possible number of edges in any ordered graph on $n$ vertices with no ordered subgraph isomorphic to $G$.
The Dem@Care Experiments and Datasets: a Technical Report
Venue: arXiv cs.CV 2016
Year: 2016

The objective of Dem@Care is the development of a complete system providing personal health services to people with dementia, as well as medical professionals and caregivers, by using a multitude of sensors, for context-aware, multi-parametric monitoring of lifestyle, ambient environment, and health parameters. Multi-sensor data analysis, combined with intelligent decision making mechanisms, will allow an accurate representation of the person's current status and will provide the appropriate feedback, both to the person and the associated caregivers, enhancing the standard clinical workflow. Within the project framework, several data collection activities have taken place to assist technical development and evaluation tasks. In all these activities, particular attention has been paid to adhere to ethical guidelines and preserve the participants' privacy. This technical report describes shorty the (a) the main objectives of the project, (b) the main ethical principles and (c) the datasets that have been already created.
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
Venue: arXiv cs.SE 2016
Year: 2016

Analogy Based Effort Estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation, and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before, therefore we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle Swarm Optimization Algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures
Automated capture and delivery of assistive task guidance with an eyewear computer: The GlaciAR system
Venue: arXiv cs.HC 2016
Year: 2016

In this paper we describe and evaluate a mixed reality system that aims to augment users in task guidance applications by combining automated and unsupervised information collection with minimally invasive video guides. The result is a self-contained system that we call GlaciAR (Glass-enabled Contextual Interactions for Augmented Reality), that operates by extracting contextual interactions from observing users performing actions. GlaciAR is able to i) automatically determine moments of relevance based on a head motion attention model, ii) automatically produce video guidance information, iii) trigger these video guides based on an object detection method, iv) learn without supervision from observing multiple users and v) operate fully on-board a current eyewear computer (Google Glass). We describe the components of GlaciAR together with evaluations on how users are able to use the system to achieve three tasks. We see this work as a first step toward the development of systems that aim to scale up the notoriously difficult authoring problem in guidance systems and where people's natural abilities are enhanced via minimally invasive visual guidance.
Guaranteeing QoS using Unlicensed TV White Spaces for Smart Grid Applications
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we consider the utilization of TV White Spaces (TVWS) by small Cognitive Radio (CR) network operators to support the communication needs of various smart grid applications. We first propose a multi-tier communication network architecture for smart metering applications in dense urban environments. Our measurement campaign, without any competition from other CR operators, reveals that the communication architecture can achieve more than 1Mbps data rates using the free unlicensed TVWS spectrum. However, anticipating stiff competition for the unlicensed TVWS spectrum among CR operators and to support smart grid applications with stringent Quality of Service (QoS) requirements, we further exploit the novel idea of high priority channels (HPC) that the CR operator can temporarily lease by paying a small fee. This poses several new challenges for the CR operators, such as, their economic viability while providing QoS guarantees. We develop a real-time decision support framework with several adjustable parameters for the CR operators that enables them to tradeoff HPC leasing cost and QoS. The developed algorithms are simple rules that provide significant opportunities to the CR operators to maintain a balance between spectrum cost and QoS depending on dynamic spectrum availability and smart grid application requirements.