OpenRaiser/Intern-Atlas huggingface dataset:
Sub-cortical brain structure segmentation using F-CNN's
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.
A replay-attack resistant message authentication scheme using time-based keying hash functions and unique message identifiers
Venue: arXiv cs.CR 2016
Year: 2016

Hash-based message authentication codes are an extremely simple yet hugely effective construction for producing keyed message digests using shared secrets. HMACs have seen widespread use as ad-hoc digital signatures in many Internet applications. While messages signed with an HMAC are secure against sender impersonation and tampering in transit, if used alone they are susceptible to replay attacks. We propose a construction that extends HMACs to produce a keyed message digest that has a finite validity period. We then propose a message signature scheme that uses this time-dependent MAC along with an unique message identifier to calculate a set of authentication factors using which a recipient can readily detect and ignore replayed messages, thus providing perfect resistance against replay attacks. We further analyse time-based message authentication codes and show that they provide stronger security guarantees than plain HMACs, even when used independently of the aforementioned replay attack resistant message signature scheme.
On Column Selection in Approximate Kernel Canonical Correlation Analysis
Venue: arXiv cs.LG 2016
Year: 2016

We study the problem of column selection in large-scale kernel canonical correlation analysis (KCCA) using the Nyström approximation, where one approximates two positive semi-definite kernel matrices using "landmark" points from the training set. When building low-rank kernel approximations in KCCA, previous work mostly samples the landmarks uniformly at random from the training set. We propose novel strategies for sampling the landmarks non-uniformly based on a version of statistical leverage scores recently developed for kernel ridge regression. We study the approximation accuracy of the proposed non-uniform sampling strategy, develop an incremental algorithm that explores the path of approximation ranks and facilitates efficient model selection, and derive the kernel stability of out-of-sample mapping for our method. Experimental results on both synthetic and real-world datasets demonstrate the promise of our method.
Who Benefits from the "Sharing" Economy of Airbnb?
Venue: arXiv cs.SI 2016
Year: 2016

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of "algorithmic regulation".
Deep Cross-Modal Hashing
Venue: arXiv cs.IR 2016
Year: 2016

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, almost all existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with handcrafted features may not achieve satisfactory performance. In this paper, we propose a novel cross-modal hashing method, called deep crossmodal hashing (DCMH), by integrating feature learning and hash-code learning into the same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on two real datasets with text-image modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.
A Tractable Fully Bayesian Method for the Stochastic Block Model
Venue: arXiv cs.LG 2016
Year: 2016

The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs. Bayesian methods are used for (i) cluster assignment inference and (ii) model selection for the number of clusters. In this paper, we study the behavior of Bayesian inference in the SBM in the large sample limit. Combining variational approximation and Laplace's method, a consistent criterion of the fully marginalized log-likelihood is established. Based on that, we derive a tractable algorithm that solves tasks (i) and (ii) concurrently, obviating the need for an outer loop to check all model candidates. Our empirical and theoretical results demonstrate that our method is scalable in computation, accurate in approximation, and concise in model selection.
Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage
Venue: arXiv cs.SY 2016
Year: 2016

We propose and experimentally validate a control strategy to dispatch the operation of a distribution feeder interfacing heterogeneous prosumers by using a grid-connected battery energy storage system (BESS) as a controllable element coupled with a minimally invasive monitoring infrastructure. It consists in a two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute average power consumption trajectory for the next day of operation (called \emph{dispatch plan}) is determined, and intra-day/real-time operation, where the mismatch with respect to the \emph{dispatch plan} is corrected by applying receding horizon model predictive control (MPC) to decide the BESS charging/discharging profile while accounting for operational constraints. The consumption forecast necessary to compute the \emph{dispatch plan} and the battery model for the MPC algorithm are built by applying adaptive data driven methodologies. The discussed control framework currently operates on a daily basis to dispatch the operation of a 20~kV feeder of the EPFL university campus using a 750~kW/500~kWh lithium titanate BESS.
Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)
Venue: arXiv cs.SE 2016
Year: 2016

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of sustainable scientific software. It also summarizes a set of lightning talks in which speakers highlighted to-the-point lessons and challenges pertaining to sustaining scientific software. The final and main contribution of the report is a summary of the discussions, future steps, and future organization for a set of self-organized working groups on topics including developing pathways to funding scientific software; constructing useful common metrics for crediting software stakeholders; identifying principles for sustainable software engineering design; reaching out to research software organizations around the world; and building communities for software sustainability. For each group, we include a point of contact and a landing page that can be used by those who want to join that group's future activities. The main challenge left by the workshop is to see if the groups will execute these activities that they have scheduled, and how the WSSSPE community can encourage this to happen.
Stratified Bayesian Optimization
Venue: arXiv cs.LG 2016
Year: 2016

We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong dependence to improve performance. Our algorithm is similar in spirit to stratification, a technique from simulation, which uses strong dependence on a categorical representation of the random input to reduce variance. We demonstrate in numerical experiments that SBO outperforms state-of-the-art Bayesian optimization benchmarks that do not leverage this dependence.
Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data
Venue: arXiv cs.CV 2016
Year: 2016

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.
A mathematical formalization of data parallel operations
Venue: arXiv cs.DC 2016
Year: 2016

We give a mathematical formalization of `generalized data parallel' operations, a concept that covers such common scientific kernels as matrix-vector multiplication, multi-grid coarsening, load distribution, and many more. We show that from a compact specification such computational aspects as MPI messages or task dependencies can be automatically derived.
The counting house: measuring those who count. Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in the Google Scholar Citations, ResearcherID, ResearchGate, Mendeley & Twitter
Venue: arXiv cs.DL 2016
Year: 2016

Following in the footsteps of the model of scientific communication, which has recently gone through a metamorphosis (from the Gutenberg galaxy to the Web galaxy), a change in the model and methods of scientific evaluation is also taking place. A set of new scientific tools are now providing a variety of indicators which measure all actions and interactions among scientists in the digital space, making new aspects of scientific communication emerge. In this work we present a method for capturing the structure of an entire scientific community (the Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics community) and the main agents that are part of it (scientists, documents, and sources) through the lens of Google Scholar Citations. Additionally, we compare these author portraits to the ones offered by other profile or social platforms currently used by academics (ResearcherID, ResearchGate, Mendeley, and Twitter), in order to test their degree of use, completeness, reliability, and the validity of the information they provide. A sample of 814 authors (researchers in Bibliometrics with a public profile created in Google Scholar Citations was subsequently searched in the other platforms, collecting the main indicators computed by each of them. The data collection was carried out on September, 2015. The Spearman correlation was applied to these indicators (a total of 31) , and a Principal Component Analysis was carried out in order to reveal the relationships among metrics and platforms as well as the possible existence of metric clusters
On Cartesian line sampling with anisotropic total variation regularization
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the use of the anisotropic total variation seminorm to recover a two dimensional vector $x\in \mathbb{C}^{N\times N}$ from its partial Fourier coefficients, sampled along Cartesian lines. We prove that if $(x_{k,j} - x_{k-1,j})_{k,j}$ has at most $s_1$ nonzero coefficients in each column and $(x_{k,j} - x_{k,j-1})_{k,j}$ has at most $s_2$ nonzero coefficients in each row, then, up to multiplication by $\log$ factors, one can exactly recover $x$ by sampling along $s_1$ horizontal lines of its Fourier coefficients and along $s_2$ vertical lines of its Fourier coefficients. Finally, unlike standard compressed sensing estimates, the $\log$ factors involved are dependent on the separation distance between the nonzero entries in each row/column of the gradient of $x$ and not on $N^2$, the ambient dimension of $x$.
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization
Venue: arXiv cs.CV 2016
Year: 2016

Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics can be modeled with a sparse component overlaid on the smooth background. The background and foreground are separated using a sparse decomposition framework regularized with a few suitable regularization terms which promotes the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to have superior performance over some prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm.
Simulation of bifurcated stent grafts to treat abdominal aortic aneurysms (AAA)
Venue: arXiv cs.GR 2016
Year: 2016

In this paper a method is introduced, to visualize bifurcated stent grafts in CT-Data. The aim is to improve therapy planning for minimal invasive treatment of abdominal aortic aneurysms (AAA). Due to precise measurement of the abdominal aortic aneurysm and exact simulation of the bifurcated stent graft, physicians are supported in choosing a suitable stent prior to an intervention. The presented method can be used to measure the dimensions of the abdominal aortic aneurysm as well as simulate a bifurcated stent graft. Both of these procedures are based on a preceding segmentation and skeletonization of the aortic, right and left iliac. Using these centerlines (aortic, right and left iliac) a bifurcated initial stent is constructed. Through the implementation of an ACM method the initial stent is fit iteratively to the vessel walls - due to the influence of external forces (distance- as well as balloonforce). Following the fitting process, the crucial values for choosing a bifurcated stent graft are measured, e.g. aortic diameter, right and left common iliac diameter, minimum diameter of distal neck. The selected stent is then simulated to the CT-Data - starting with the initial stent. It hereby becomes apparent if the dimensions of the bifurcated stent graft are exact, i.e. the fitting to the arteries was done properly and no ostium was covered.
A Review of Control Algorithms for Autonomous Quadrotors
Venue: arXiv cs.SY 2016
Year: 2016

The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H-infinity, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.
An efficient null space inexact Newton method for hydraulic simulation of water distribution networks
Venue: arXiv cs.SY 2016
Year: 2016

Null space Newton algorithms are efficient in solving the nonlinear equations arising in hydraulic analysis of water distribution networks. In this article, we propose and evaluate an inexact Newton method that relies on partial updates of the network pipes' frictional headloss computations to solve the linear systems more efficiently and with numerical reliability. The update set parameters are studied to propose appropriate values. Different null space basis generation schemes are analysed to choose methods for sparse and well-conditioned null space bases resulting in a smaller update set. The Newton steps are computed in the null space by solving sparse, symmetric positive definite systems with sparse Cholesky factorizations. By using the constant structure of the null space system matrices, a single symbolic factorization in the Cholesky decomposition is used multiple times, reducing the computational cost of linear solves. The algorithms and analyses are validated using medium to large-scale water network models.
Coding in the fork network in the framework of Kolmogorov complexity
Venue: arXiv cs.IT 2016
Year: 2016

Many statements from the classic information theory (the theory of Shannon's entropy) have natural counterparts in the algorithmic information theory (in the framework of Kolmogorov complexity). In this paper we discuss one simple instance of the parallelism between Shannon's and Kolmogorov's theories: we prove in the setting of Kolmogorov complexity a version of Wolf's characterization of admissible rates for the fork network.
Decoy Bandits Dueling on a Poset
Venue: arXiv cs.LG 2016
Year: 2016

We adress the problem of dueling bandits defined on partially ordered sets, or posets. In this setting, arms may not be comparable, and there may be several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms of any poset even when pairs of comparable arms cannot be distinguished from pairs of incomparable arms, with a set of minimal assumptions. This algorithm relies on the concept of decoys, which stems from social psychology. For the easier case where the incomparability information may be accessible, we propose a second algorithm, SlicingBandits, which takes advantage of this information and achieves a very significant gain of performance compared to UnchainedBandits. We provide theoretical guarantees and experimental evaluation for both algorithms.
Revisiting the Sanders-Freiman-Ruzsa Theorem in $\mathbb{F}_p^n$ and its Application to Non-malleable Codes
Venue: arXiv cs.DM 2016
Year: 2016

Non-malleable codes (NMCs) protect sensitive data against degrees of corruption that prohibit error detection, ensuring instead that a corrupted codeword decodes correctly or to something that bears little relation to the original message. The split-state model, in which codewords consist of two blocks, considers adversaries who tamper with either block arbitrarily but independently of the other. The simplest construction in this model, due to Aggarwal, Dodis, and Lovett (STOC'14), was shown to give NMCs sending k-bit messages to $O(k^7)$-bit codewords. It is conjectured, however, that the construction allows linear-length codewords. Towards resolving this conjecture, we show that the construction allows for code-length $O(k^5)$. This is achieved by analysing a special case of Sanders's Bogolyubov-Ruzsa theorem for general Abelian groups. Closely following the excellent exposition of this result for the group $\mathbb{F}_2^n$ by Lovett, we expose its dependence on $p$ for the group $\mathbb{F}_p^n$, where $p$ is a prime.
Comments on "On Clock Synchronization Algorithms for Wireless Sensor Networks Under Unknown Delay"
Venue: arXiv cs.DC 2016
Year: 2016

The generalization of the maximum-likelihood-like estimator for clock skew by Leng and Wu in the above paper is erroneous because the correlation of the noise components in the model is not taken into account in the derivation of the maximum likelihood estimator, its performance bound, and the optimal selection of the gap between two subtracting time stamps. This comment investigates the issue of noise correlation in the model and provides the range of the gap for which the maximum likelihood estimator and its performance bound are valid and corrects the optimal selection of the gap based on the provided range.
A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
Venue: arXiv cs.NE 2016
Year: 2016

With this paper, we contribute to the growing research area of feature-based analysis of bio-inspired computing. In this research area, problem instances are classified according to different features of the underlying problem in terms of their difficulty of being solved by a particular algorithm. We investigate the impact of different sets of evolved instances for building prediction models in the area of algorithm selection. Building on the work of Poursoltan and Neumann [11,10], we consider how evolved instances can be used to predict the best performing algorithm for constrained continuous optimisation from a set of bio-inspired computing methods, namely high performing variants of differential evolution, particle swarm optimization, and evolution strategies. Our experimental results show that instances evolved with a multi-objective approach in combination with random instances of the underlying problem allow to build a model that accurately predicts the best performing algorithm for a wide range of problem instances.