OpenRaiser/Intern-Atlas huggingface dataset:
The Effects of Data Quality on the Analysis of Corporate Board Interlock Networks
Venue: arXiv cs.SI 2016
Year: 2016

Nowadays, social networks of ever increasing size are studied by researchers from a range of disciplines. The data underlying these networks is often automatically gathered from API's, websites or existing databases. As a result, the quality of this data is typically not manually validated, and the resulting networks may be based on false, biased or incomplete data. In this paper, we investigate the effect of data quality issues on the analysis of large networks. We focus on the global board interlock network, in which nodes represent firms across the globe, and edges model social ties between firms -- shared board members holding a position at both firms. First, we demonstrate how we can automatically assess the completeness of a large dataset of 160 million firms, in which data is missing not at random. Second, we present a novel method to increase the accuracy of the entries in our data. By comparing the expected and empirical characteristics of the resulting network topology, we develop a technique that automatically prunes and merges duplicate nodes and edges. Third, we use a case study of the board interlock network of Sweden to show how poor quality data results in incorrect network topologies, biased centrality values and abnormal influence spread under a well-known diffusion model. Finally, we demonstrate how our data quality assessment methods help restore the correct network structure, ultimately allowing us to derive meaningful and correct results from analyzing the network.
Matrix multiplication algorithms from group orbits
Venue: arXiv cs.CC 2016
Year: 2016

We show how to construct highly symmetric algorithms for matrix multiplication. In particular, we consider algorithms which decompose the matrix multiplication tensor into a sum of rank-1 tensors, where the decomposition itself consists of orbits under some finite group action. We show how to use the representation theory of the corresponding group to derive simple constraints on the decomposition, which we solve by hand for n=2,3,4,5, recovering Strassen's algorithm (in a particularly symmetric form) and new algorithms for larger n. While these new algorithms do not improve the known upper bounds on tensor rank or the matrix multiplication exponent, they are beautiful in their own right, and we point out modifications of this idea that could plausibly lead to further improvements. Our constructions also suggest further patterns that could be mined for new algorithms, including a tantalizing connection with lattices. In particular, using lattices we give the most transparent proof to date of Strassen's algorithm; the same proof works for all n, to yield a decomposition with $n^3 - n + 1$ terms.
Privacy Patterns
Venue: arXiv cs.SE 2016
Year: 2016

Inspired by the design patterns of object-oriented software architecture, we offer an initial set of "privacy patterns". Our intent is to describe the most important ways in which software systems can offer privacy to their stakeholders. We express our privacy patterns as class diagrams in the UML (Universal Modelling Language), because this is a commonly-used language for expressing the high-level architecture of an object-oriented system. In this initial set of privacy patterns, we sketch how each of Westin's four states of privacy can be implemented in a software system. In addition to Westin's states of Solitude, Intimacy, Anonymity, and Reserve, we develop a privacy pattern for an institutionalised form of Intimacy which we call Confidence.
AI Researchers, Video Games Are Your Friends!
Venue: arXiv cs.AI 2016
Year: 2016

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.
"Boring formal methods" or "Sherlock Holmes deduction methods"?
Venue: arXiv cs.LO 2016
Year: 2016

This paper provides an overview of common challenges in teaching of logic and formal methods to Computer Science and IT students. We discuss our experiences from the course IN3050: Applied Logic in Engineering, introduced as a "logic for everybody" elective course at at TU Munich, Germany, to engage pupils studying Computer Science, IT and engineering subjects on Bachelor and Master levels. Our goal was to overcome the bias that logic and formal methods are not only very complicated but also very boring to study and to apply. In this paper, we present the core structure of the course, provide examples of exercises and evaluate the course based on the students' surveys.
FoCUS: Fourier-based Coded Ultrasound
Venue: arXiv cs.IT 2016
Year: 2016

Modern imaging systems typically use single-carrier short pulses for transducer excitation. Coded signals together with pulse compression are successfully used in radar and communication to increase the amount of transmitted energy. Previous research verified significant improvement in SNR and imaging depth for ultrasound imaging with coded signals. Since pulse compression needs to be applied at each transducer element, the implementation of coded excitation (CE) in array imaging is computationally complex. Applying pulse compression on the beamformer output reduces the computational load but also degrades both the axial and lateral point spread function (PSF) compromising image quality. In this work we present an approach for efficient implementation of pulse compression by integrating it into frequency domain beamforming. This method leads to significant reduction in the amount of computations without affecting axial resolution. The lateral resolution is dictated by the factor of savings in computational load. We verify the performance of our method on a Verasonics imaging system and compare the resulting images to time-domain processing. We show that up to 77 fold reduction in computational complexity can be achieved in a typical imaging setups. The efficient implementation makes CE a feasible approach in array imaging paving the way to enhanced SNR as well as improved imaging depth and frame-rate.
Broadcast Strategies and Performance Evaluation of IEEE 802.15.4 in Wireless Body Area Networks WBAN
Venue: arXiv cs.PF 2016
Year: 2016

The rapid advances in sensors and ultra-low power wireless communication has enabled a new generation of wireless sensor networks: Wireless Body Area Networks (WBAN). To the best of our knowledge the current paper is the first to address broadcast in WBAN. We first analyze several broadcast strategies inspired from the area of Delay Tolerant Networks (DTN). The proposed strategies are evaluated via the OMNET++ simulator that we enriched with realistic human body mobility models and channel models issued from the recent research on biomedical and health informatics. Contrary to the common expectation, our results show that existing research in DTN cannot be transposed without significant modifications in WBANs area. That is, existing broadcast strategies for DTNs do not perform well with human body mobility. However, our extensive simulations give valuable insights and directions for designing efficient broadcast in WBAN. Furthermore, we propose a novel broadcast strategy that outperforms the existing ones in terms of end-to-end delay, network coverage and energy consumption. Additionally, we performed investigations of independent interest related to the ability of all the studied strategies to ensure the total order delivery property when stressed with various packet rates. These investigations open new and challenging research directions.
Correlation Alignment for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2016
Year: 2016

In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is also much simpler than other distribution matching methods. CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. We first describe a solution that applies a linear transformation to source features to align them with target features before classifier training. For linear classifiers, we propose to equivalently apply CORAL to the classifier weights, leading to added efficiency when the number of classifiers is small but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA) outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (DNNs). The resulting Deep CORAL approach works seamlessly with DNNs and achieves state-of-the-art performance on standard benchmark datasets. Our code is available at:~\url{https://github.com/VisionLearningGroup/CORAL}
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories. It exploits localization cues that emerge from training classification-tasked convolutional networks, to drive a "self-supervision" process that automatically labels a sparse, diverse training set of points likely to belong to classes of interest. Our approach has almost no hyperparameters, is modular, and allows for very fast training of segmentation in less than 3 minutes. It obtains competitive results on the VOC 2012 segmentation benchmark. More, significantly the modularity and fast training of our framework allows new classes to efficiently added for inference.
Revisiting Hammel et al. (1987): Does the shadowing property hold for modern computers?
Venue: arXiv cs.NA 2016
Year: 2016

Computational techniques are extensively applied in nonlinear science. However, while the use of computers for research has been expressive, the evaluation of numerical results does not grow in the same pace. Hammel et al. (Journal of Complexity, 1987, 3(2), 136--145) were pioneers in the numerical reliability field and have proved a theorem that a pseudo-orbit of a logistic map is shadowed by a true orbit within a distance of $10^{-8}$ for $10^{7}$ iterates. But the simulation of the logistic map with less than 100 iterates presents an error greater than $10^{-8}$ in a modern computer, performing a test based on the concept of multiple pseudo-orbits and symbolic computing.
Fusion of Range and Thermal Images for Person Detection
Venue: arXiv cs.CV 2016
Year: 2016

Detecting people in images is a challenging problem. Differences in pose, clothing and lighting, along with other factors, cause a lot of variation in their appearance. To overcome these issues, we propose a system based on fused range and thermal infrared images. These measurements show considerably less variation and provide more meaningful information. We provide a brief introduction to the sensor technology used and propose a calibration method. Several data fusion algorithms are compared and their performance is assessed on a simulated data set. The results of initial experiments on real data are analyzed and the measurement errors and the challenges they present are discussed. The resulting fused data are used to efficiently detect people in a fixed camera set-up. The system is extended to include person tracking.
Over-the-air Function Computation in Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

Many sensor applications are interested in computing a function over measurements (e.g., sum, average, max) as opposed to collecting all sensor data. Today, such data aggregation is done in a cluster-head. Sensor nodes transmit their values sequentially to a cluster-head node, which calculates the aggregation function and forwards it to the base station. In contrast, this paper explores the possibility of computing a desired function over the air. We devise a solution that enables sensors to transmit coherently over the wireless medium so that the cluster-head directly receives the value of the desired function. We present analysis and preliminary results that demonstrate that such a design yield a large improvement in network throughput.
Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages
Venue: arXiv cs.AI 2016
Year: 2016

Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. This article introduces a new supervised classification method: the extend natural neighbor (ENaN) method, and shows that it provides a better classification result without choosing the neighborhood parameter artificially. Unlike the original KNN based method which needs a prior k, the ENaNE method predicts different k in different stages. Therefore, the ENaNE method is able to learn more from flexible neighbor information both in training stage and testing stage, and provide a better classification result.
Pano2Vid: Automatic Cinematography for Watching 360$^{\circ}$ Videos
Venue: arXiv cs.CV 2016
Year: 2016

We introduce the novel task of Pano2Vid $-$ automatic cinematography in panoramic 360$^{\circ}$ videos. Given a 360$^{\circ}$ video, the goal is to direct an imaginary camera to virtually capture natural-looking normal field-of-view (NFOV) video. By selecting "where to look" within the panorama at each time step, Pano2Vid aims to free both the videographer and the end viewer from the task of determining what to watch. Towards this goal, we first compile a dataset of 360$^{\circ}$ videos downloaded from the web, together with human-edited NFOV camera trajectories to facilitate evaluation. Next, we propose AutoCam, a data-driven approach to solve the Pano2Vid task. AutoCam leverages NFOV web video to discriminatively identify space-time "glimpses" of interest at each time instant, and then uses dynamic programming to select optimal human-like camera trajectories. Through experimental evaluation on multiple newly defined Pano2Vid performance measures against several baselines, we show that our method successfully produces informative videos that could conceivably have been captured by human videographers.
Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads
Venue: arXiv cs.DB 2016
Year: 2016

Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these systems support real-world image analysis use cases, and how performant are the image analytics tasks implemented on top of such systems. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.
Complex Matrix Factorization for Face Recognition
Venue: arXiv cs.CV 2016
Year: 2016

This work developed novel complex matrix factorization methods for face recognition; the methods were complex matrix factorization (CMF), sparse complex matrix factorization (SpaCMF), and graph complex matrix factorization (GraCMF). After real-valued data are transformed into a complex field, the complex-valued matrix will be decomposed into two matrices of bases and coefficients, which are derived from solutions to an optimization problem in a complex domain. The generated objective function is the real-valued function of the reconstruction error, which produces a parametric description. Factorizing the matrix of complex entries directly transformed the constrained optimization problem into an unconstrained optimization problem. Additionally, a complex vector space with N dimensions can be regarded as a 2N-dimensional real vector space. Accordingly, all real analytic properties can be exploited in the complex field. The ability to exploit these important characteristics motivated the development herein of a simpler framework that can provide better recognition results. The effectiveness of this framework will be clearly elucidated in later sections in this paper.
Filter sharing: Efficient learning of parameters for volumetric convolutions
Venue: arXiv cs.CV 2016
Year: 2016

Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them. In medical applications where training data is hard to come by, these sophisticated machine learning models are difficult to train. In this paper, we propose a method to reduce the inherent complexity of CNNs during training by exploiting the significant redundancy that is noticed in the learnt CNN filters. Our method relies on finding a small set of filters and mixing coefficients to derive every filter in each convolutional layer at the time of training itself, thereby reducing the number of parameters to be trained. We consider the problem of 3D lung nodule segmentation in CT images and demonstrate the effectiveness of our method in achieving good results with only few training examples.
Inverses, Conditionals and Compositional Operators in Separative Valuation Algebra
Venue: arXiv cs.AI 2016
Year: 2016

Compositional models were introduce by Jirousek and Shenoy in the general framework of valuation-based systems. They based their theory on an axiomatic system of valuations involving not only the operations of combination and marginalisation, but also of removal. They claimed that this systems covers besides the classical case of discrete probability distributions, also the cases of Gaussian densities and belief functions, and many other systems. Whereas their results on the compositional operator are correct, the axiomatic basis is not sufficient to cover the examples claimed above. We propose here a different axiomatic system of valuation algebras, which permits a rigorous mathematical theory of compositional operators in valuation-based systems and covers all the examples mentioned above. It extends the classical theory of inverses in semigroup theory and places thereby the present theory into its proper mathematical frame. Also this theory sheds light on the different structures of valuation-based systems, like regular algebras (represented by probability potentials), canncellative algebras (Gaussian potentials) and general separative algebras (density functions).
Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm
Venue: arXiv cs.CV 2016
Year: 2016

We propose a novel tree-like curvilinear structure reconstruction algorithm based on supervised learning and graph theory. In this work we analyze image patches to obtain the local major orientations and the rankings that correspond to the curvilinear structure. To extract local curvilinear features, we compute oriented gradient information using steerable filters. We then employ Structured Support Vector Machine for ordinal regression of the input image patches, where the ordering is determined by shape similarity to latent curvilinear structure. Finally, we progressively reconstruct the curvilinear structure by looking for geodesic paths connecting remote vertices in the graph built on the structured output rankings. Experimental results show that the proposed algorithm faithfully provides topological features of the curvilinear structures using minimal pixels for various datasets.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Venue: arXiv cs.CV 2016
Year: 2016

Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset.
Domain knowledge assisted cyst segmentation in OCT retinal images
Venue: arXiv cs.CV 2016
Year: 2016

3D imaging modalities are becoming increasingly popular and relevant in retinal imaging owing to their effectiveness in highlighting structures in sub-retinal layers. OCT is one such modality which has great importance in the context of analysis of cystoid structures in subretinal layers. Signal to noise ratio(SNR) of the images obtained from OCT is less and hence automated and accurate determination of cystoid structures from OCT is a challenging task. We propose an automated method for detecting/segmenting cysts in 3D OCT volumes. The proposed method is biologically inspired and fast aided by the domain knowledge about the cystoid structures. An ensemble learning methodRandom forests is learnt for classification of detected region into cyst region. The method achieves detection and segmentation in a unified setting. We believe the proposed approach with further improvements can be a promising starting point for more robust approach. This method is validated against the training set achieves a mean dice coefficient of 0.3893 with a standard deviation of 0.2987