OpenRaiser/Intern-Atlas huggingface dataset:
Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs
Venue: arXiv cs.RO 2016
Year: 2016

We study the nonlinear observability of a systems states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Common trajectory-planning algorithms tend to generate motions that lead to an unobservable subspace of the system state, causing suboptimal state estimation. We address this problem with a method that reasons about the quality of observability while respecting system dynamics and motion constraints to yield the optimal trajectory for rapid convergence of the self-calibration states (or other user-chosen states). Experiments performed on a simulated quadrotor system with a GPS-IMU sensor suite demonstrate the benefits of the optimized observability-aware trajectories when compared to a covariance-based approach and multiple heuristic approaches. Our method is approx. 80x faster than the covariance-based approach and achieves better results than any other approach in the self-calibration task. We applied our method to a waypoint navigation task and achieved a approx. 2x improvement in the integrated RMSE of the global position estimates and approx. 4x improvement in the integrated RMSE of the GPS-IMU transformation estimates compared to a minimal-energy trajectory planner.
Knowledge Ratings in MetaboLights
Venue: arXiv cs.DL 2016
Year: 2016

This technical report presents an evaluation of the ontology annotations in the metadata of a subset of entries of MetaboLights, a database for Metabolomics experiments and derived information. The work includes a manual analysis of the entries and a comprehensive qualitative evaluation of their annotations, together with the evaluation guide and its rationale, that was defined and followed. The approach was also implemented as a software script that given a MetaboLights entry returns a quantitative evaluation of the quality of its annotations (available on request).
Hybrid Analog-Digital Precoding Revisited under Realistic RF Modeling
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we revisit hybrid analog-digital precoding systems with emphasis on their modelling and radio-frequency (RF) losses, to realistically evaluate their benefits in 5G system implementations. For this, we decompose the analog beamforming networks (ABFN) as a bank of commonly used RF components and formulate realistic model constraints based on their S-parameters. Specifically, we concentrate on fully-connected ABFN (FC-ABFN) and Butler networks for implementing the discrete Fourier transform (DFT) in the RF domain. The results presented in this paper reveal that the performance and energy efficiency of hybrid precoding systems are severely affected, once practical factors are considered in the overall design. In this context, we also show that Butler RF networks are capable of providing better performances than FC-ABFN for systems with a large number of RF chains.
Diving deeper into mentee networks
Venue: arXiv cs.LG 2016
Year: 2016

Modern computer vision is all about the possession of powerful image representations. Deeper and deeper convolutional neural networks have been built using larger and larger datasets and are made publicly available. A large swath of computer vision scientists use these pre-trained networks with varying degrees of successes in various tasks. Even though there is tremendous success in copying these networks, the representational space is not learnt from the target dataset in a traditional manner. One of the reasons for opting to use a pre-trained network over a network learnt from scratch is that small datasets provide less supervision and require meticulous regularization, smaller and careful tweaking of learning rates to even achieve stable learning without weight explosion. It is often the case that large deep networks are not portable, which necessitates the ability to learn mid-sized networks from scratch. In this article, we dive deeper into training these mid-sized networks on small datasets from scratch by drawing additional supervision from a large pre-trained network. Such learning also provides better generalization accuracies than networks trained with common regularization techniques such as l2, l1 and dropouts. We show that features learnt thus, are more general than those learnt independently. We studied various characteristics of such networks and found some interesting behaviors.
Influence of Agents Heterogeneity in Cellular Model of Evacuation
Venue: arXiv cs.MA 2016
Year: 2016

The influence of agents heterogeneity on the microscopic characteristics of pedestrian flow is studied via an evacuation simulation tool based on the Floor-Field model. The heterogeneity is introduced in agents velocity, aggressiveness, and sensitivity to occupation. The simulation results are compared to data gathered during an original experiment. The comparison shows that the heterogeneity in aggressiveness and sensitivity occupation enables to reproduce some microscopic aspects. The heterogeneity in velocity seems to be redundant.
Your Neighbors Are My Spies: Location and other Privacy Concerns in GLBT-focused Location-based Dating Applications
Venue: arXiv cs.CY 2016
Year: 2016

Trilateration is one of the well-known threat models to the user's location privacy in location-based apps, especially those contain highly sensitive information such as dating apps. The threat model mainly bases on the publicly shown distance from a targeted victim to the adversary to pinpoint the victim's location. As a countermeasure, most of location-based apps have already implemented the 'hide distance' function, or added noise to the publicly shown distance in order to protect their user's location privacy. The effectiveness of such approaches however is still questionable.
The Algorithmic Autoregulation Software Development Methodology
Venue: arXiv cs.SE 2016
Year: 2016

We present a new self-regulating methodology for coordinating distributed team work called Algorithmic Autoregulation (AA), based on recent social networking concepts and individual merit. Team members take on an egalitarian role, and stay voluntarily logged into so-called AA sessions for part of their time (e.g. 2 hours per day), during which they create periodical logs - short text sentences - they wish to share about their activity with the team. These logs are publicly aggregated in a website and are peer-validated after the end of a session, as in code review. A short screencast is ideally recorded at the end of each session to make AA logs more understandable. This methodology has shown to be well-suited for increasing the efficiency of distributed teams working on Global Software Development (GSD), as observed in our reported experience in actual real-world situations. This efficiency boost is mainly achieved through 1) built-in asynchronous on-demand communication in conjunction with documentation of work, products, and processes, and 2) reduced need for central management, meetings or time-consuming reports. Hence, the AA methodology legitimizes and facilitates the activities of a distributed software team. It thus enables other entities to have a solid means to fund these activities, allowing for new and concrete business models to emerge for very distributed software development. AA has been proposed, at its core, as a way of sustaining self-replicating hacker initiatives. These claims are discussed in a real case-study of running a distributed free software hacker team called Lab Macambira.
Development of a wearable haptic game interface
Venue: arXiv cs.HC 2016
Year: 2016

This paper outlines the development and evaluation of a wearable haptic game interface. The device differs from many traditional haptic feedback implementation in that it combines vibrotactile feedback with gesture based input, thus becoming a two way conduit between the user and the virtual environment. The device is intended to challenge what is considered an "interface" and sets out to purposefully blur the boundary between man and machine. This allows for a more immersive experience, and a user evaluation shows that the intuitive interface allows the user to become the aircraft that is controlled by the movements of the user's hand.
Advances in Property-Based Testing for $α$Prolog
Venue: arXiv cs.LO 2016
Year: 2016

$α$Check is a light-weight property-based testing tool built on top of $α$Prolog, a logic programming language based on nominal logic. $α$Prolog is particularly suited to the validation of the meta-theory of formal systems, for example correctness of compiler translations involving name-binding, alpha-equivalence and capture-avoiding substitution. In this paper we describe an alternative to the negation elimination algorithm underlying $α$Check that substantially improves its effectiveness. To substantiate this claim we compare the checker performances w.r.t. two of its main competitors in the logical framework niche, namely the QuickCheck/Nitpick combination offered by Isabelle/HOL and the random testing facility in PLT-Redex.
Stable Throughput Region of the Two-User Broadcast Channel
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we consider the two-user broadcast channel and we characterize its stable throughout region. We start the analysis by providing the stability region for the general case without any specific considerations on transmission and reception mechanisms. We also provide conditions for the stable throughput region to be convex. Subsequently, we consider the case where the transmitter uses superposition coding and we consider two special cases for the receivers. The first one is when both receivers treat interference as noise. The second is when the user with a better channel uses successive decoding and the other receiver treats interference as noise.
A Novel Method to Study Bottom-up Visual Saliency and its Neural Mechanism
Venue: arXiv cs.CV 2016
Year: 2016

In this study, we propose a novel method to measure bottom-up saliency maps of natural images. In order to eliminate the influence of top-down signals, backward masking is used to make stimuli (natural images) subjectively invisible to subjects, however, the bottom-up saliency can still orient the subjects attention. To measure this orientation/attention effect, we adopt the cueing effect paradigm by deploying discrimination tasks at each location of an image, and measure the discrimination performance variation across the image as the attentional effect of the bottom-up saliency. Such attentional effects are combined to construct a final bottomup saliency map. Based on the proposed method, we introduce a new bottom-up saliency map dataset of natural images to benchmark computational models. We compare several state-of-the-art saliency models on the dataset. Moreover, the proposed paradigm is applied to investigate the neural basis of the bottom-up visual saliency map by analyzing psychophysical and fMRI experimental results. Our findings suggest that the bottom-up saliency maps of natural images are constructed in V1. It provides a strong scientific evidence to resolve the long standing dispute in neuroscience about where the bottom-up saliency map is constructed in human brain.
Detection of epileptic seizure in EEG signals using linear least squares preprocessing
Venue: arXiv cs.LG 2016
Year: 2016

An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the pre developed spline function.Different statistical measures namely classification accuracy, true positive and negative rates, false positive and negative rates and precision are utilized to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods. Logistic, LazyIB1, LazyIB5 and J48 are the best classifiers. Their true positive and negative rates are $1$ while false positive and negative rates are zero and the corresponding precision values are $1$. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure.
Robust Joint Alignment of Multiple Versions of a Piece of Music
Venue: arXiv cs.SD 2016
Year: 2016

Large music content libraries often comprise multiple versions of a piece of music. To establish a link between different versions, automatic music alignment methods map each position in one version to a corresponding position in another version. Due to the leeway in interpreting a piece, any two versions can differ significantly, for example, in terms of local tempo, articulation, or playing style. For a given pair of versions, these differences can be significant such that even state-of-the-art methods fail to identify a correct alignment. In this paper, we present a novel method that increases the robustness for difficult to align cases. Instead of aligning only pairs of versions as done in previous methods, our method aligns multiple versions in a joint manner. This way, the alignment can be computed by comparing each version not only with one but with several versions, which stabilizes the comparison and leads to an increase in alignment robustness. Using recordings from the Mazurka Project, the alignment error for our proposed method was 14% lower on average compared to a state-of-the-art method, with significantly less outliers (standard deviation 53% lower).
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details. Different from most of existing methods that aim at predicting high-resolution (HR) images directly, DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning. DEGREE further augments the SR process with edge-preserving capability, namely the LR image and its edge map can jointly infer the sharp edge details of the HR image during the recurrent recovery process. To speed up its training convergence rate, by-pass connections across multiple layers of DEGREE are constructed. In addition, we offer an understanding on DEGREE from the view-point of sub-band frequency decomposition on image signal and experimentally demonstrate how DEGREE can recover different frequency bands separately. Extensive experiments on three benchmark datasets clearly demonstrate the superiority of DEGREE over well-established baselines and DEGREE also provides new state-of-the-arts on these datasets.
Low-Complexity Hybrid Linear/Tomlinson-Harashima Precoding for Downlink Large-Scale MU-MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose a novel low-complexity hybrid linear/Tomlinson-Harashima precoder (H-L-THP) for downlink large-scale multiuser multiple-input multiple-output (MU-MIMO) systems. The proposed precoder comprises an inner linear precoder which utilizes only the second order statistics of the channel state information (CSI) and outer THPs which use the instantaneous overall CSI of the cascade of the actual channel and the inner precoder. The user terminals are divided into groups, where for each group a THP successively mitigates the intra-group interference, whereas the inter-group interference is canceled by the inner linear precoder. Our simulation results show that the bit error rate (BER) of the proposed H-L-THP precoder is close to that of the conventional THP precoder, and is substantially lower than the BER of the commonly used regularized zero-forcing (RZF) precoder. Moreover, we derive exact expressions for the computational complexity of the proposed H-L-THP precoder in terms of the required numbers of floating-point operations. These results reveal that the proposed H-L-THP precoder has a much lower computational complexity than the conventional THP and RZF precoders, and is thus an excellent candidate for practical implementations.
A Linear Time Parameterized Algorithm for Node Unique Label Cover
Venue: arXiv cs.DS 2016
Year: 2016

The optimization version of the Unique Label Cover problem is at the heart of the Unique Games Conjecture which has played an important role in the proof of several tight inapproximability results. In recent years, this problem has been also studied extensively from the point of view of parameterized complexity. Cygan et al. [FOCS 2012] proved that this problem is fixed-parameter tractable (FPT) and Wahlström [SODA 2014] gave an FPT algorithm with an improved parameter dependence. Subsequently, Iwata, Wahlström and Yoshida [2014] proved that the edge version of Unique Label Cover can be solved in linear FPT-time. That is, there is an FPT algorithm whose dependence on the input-size is linear. However, such an algorithm for the node version of the problem was left as an open problem. In this paper, we resolve this question by presenting the first linear-time FPT algorithm for Node Unique Label Cover.
Supervisory Control for Behavior Composition
Venue: arXiv cs.AI 2016
Year: 2016

We relate behavior composition, a synthesis task studied in AI, to supervisory control theory from the discrete event systems field. In particular, we show that realizing (i.e., implementing) a target behavior module (e.g., a house surveillance system) by suitably coordinating a collection of available behaviors (e.g., automatic blinds, doors, lights, cameras, etc.) amounts to imposing a supervisor onto a special discrete event system. Such a link allows us to leverage on the solid foundations and extensive work on discrete event systems, including borrowing tools and ideas from that field. As evidence of that we show how simple it is to introduce preferences in the mapped framework.
Effective Backscatter Approximation for Photometry in Murky Water
Venue: arXiv cs.CV 2016
Year: 2016

Shading-based approaches like Photometric Stereo assume that the image formation model can be effectively optimized for the scene normals. However, in murky water this is a very challenging problem. The light from artificial sources is not only reflected by the scene but it is also scattered by the medium particles, yielding the backscatter component. Backscatter corresponds to a complex term with several unknown variables, and makes the problem of normal estimation hard. In this work, we show that instead of trying to optimize the complex backscatter model or use previous unrealistic simplifications, we can approximate the per-pixel backscatter signal directly from the captured images. Our method is based on the observation that backscatter is saturated beyond a certain distance, i.e. it becomes scene-depth independent, and finally corresponds to a smoothly varying signal which depends strongly on the light position with respect to each pixel. Our backscatter approximation method facilitates imaging and scene reconstruction in murky water when the illumination is artificial as in Photometric Stereo. Specifically, we show that it allows accurate scene normal estimation and offers potentials like single image restoration. We evaluate our approach using numerical simulations and real experiments within both the controlled environment of a big water-tank and real murky port-waters.
Opening the Frey/Osborne Black Box: Which Tasks of a Job are Susceptible to Computerization?
Venue: arXiv cs.CY 2016
Year: 2016

In their seminal paper, Frey and Osborne quantified the automation of jobs, by assigning each job in the O*NET database a probability to be automated. In this paper, we refine their results in the following way: Every O*NET job consists of a set of tasks, and these tasks can be related. We use a linear program to assign probabilities to tasks, such that related tasks have a similar probability and the tasks can explain the computerization probability of a job. Analyzing jobs on the level of tasks helps comprehending the results, as experts as well as laymen can more easily criticize and refine what parts of a job are susceptible to computerization.
Augmented Reality Oculus Rift
Venue: arXiv cs.GR 2016
Year: 2016

This paper covers the whole process of developing an Augmented Reality Stereoscopig Render Engine for the Oculus Rift. To capture the real world in form of a camera stream, two cameras with fish-eye lenses had to be installed on the Oculus Rift DK1 hardware. The idea was inspired by Steptoe \cite{steptoe2014presence}. After the introduction, a theoretical part covers all the most neccessary elements to achieve an AR System for the Oculus Rift, following the implementation part where the code from the AR Stereo Engine is explained in more detail. A short conclusion section shows some results, reflects some experiences and in the final chapter some future works will be discussed. The project can be accessed via the git repository https://github.com/MaXvanHeLL/ARift.git.
Locally Repairable Regenerating Code Constructions
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we give locally repairable regenerating code (LRRC) [1]-[3], [5], [6] constructions that can protect the file size promised by the graph analysis of the modified family helper selection (MFHS) scheme [1] at the minimum-bandwidth-regenerating (MBR) point.
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Venue: arXiv cs.CV 2016
Year: 2016

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.