OpenRaiser/Intern-Atlas huggingface dataset:
Acoustic intensity, energy-density and diffuseness estimation in a directionally-constrained region
Venue: arXiv cs.SD 2016
Year: 2016

This work presents a method for estimation of the acoustic intensity, the energy density and the associated sound field diffuseness around the origin, when the sound field is weighted with a spatial filter. The method permits energetic DOA estimation and sound field characterization focused in a specific angular region determined by the beam pattern of the spatial filter. The formulation of the estimators is presented and their behavior is analyzed for the fundamental cases useful in parametric sound field models of a single plane wave, a uniform diffuse field and a mixture of the two.
Active Canny: Edge Detection and Recovery with Open Active Contour Models
Venue: arXiv cs.CV 2016
Year: 2016

We introduce an edge detection and recovery framework based on open active contour models (snakelets). This is motivated by the noisy or broken edges output by standard edge detection algorithms, like Canny. The idea is to utilize the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking them if gradient magnitudes are above some threshold. We initialize short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges, and they provide a smooth edge representation of the image; they can also be used for higher level analysis, like contour segmentation.
First-Order Bayesian Network Specifications Capture the Complexity Class PP
Venue: arXiv cs.AI 2016
Year: 2016

The point of this note is to prove that a language is in the complexity class PP if and only if the strings of the language encode valid inferences in a Bayesian network defined using function-free first-order logic with equality.
Fully-Trainable Deep Matching
Venue: arXiv cs.CV 2016
Year: 2016

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this paper, we remove this limitation by rewriting the complete DM algorithm as a convolutional neural network. This results in a novel deep architecture for image matching that involves a number of new layer types and that, similar to recent networks for image segmentation, has a U-topology. We demonstrate the utility of the approach by improving the performance of DM by learning it end-to-end on an image matching task.
Dilemma First Search for Effortless Optimization of NP-Hard Problems
Venue: arXiv cs.DS 2016
Year: 2016

To tackle the exponentiality associated with NP-hard problems, two paradigms have been proposed. First, Branch & Bound, like Dynamic Programming, achieve efficient exact inference but requires extensive information and analysis about the problem at hand. Second, meta-heuristics are easier to implement but comparatively inefficient. As a result, a number of problems have been left unoptimized and plain greedy solutions are used. We introduce a theoretical framework and propose a powerful yet simple search method called Dilemma First Search (DFS). DFS exploits the decision heuristic needed for the greedy solution for further optimization. DFS is useful when it is hard to design efficient exact inference. We evaluate DFS on two problems: First, the Knapsack problem, for which efficient algorithms exist, serves as a toy example. Second, Decision Tree inference, where state-of-the-art algorithms rely on the greedy or randomness-based solutions. We further show that decision trees benefit from optimizations that are performed in a fraction of the iterations required by a random-based search.
Integrated, reliable and cloud-based personal health record: A scoping review
Venue: arXiv cs.CY 2016
Year: 2016

Personal Health Records (PHR) emerge as an alternative to integrate patient's health information to give a global view of patients' status. However, integration is not a trivial feature when dealing with a variety electronic health systems from healthcare centers. Access to PHR sensitive information must comply with privacy policies defined by the patient. Architecture PHR design should be in accordance to these, and take advantage of nowadays technology. Cloud computing is a current technology that provides scalability, ubiquity, and elasticity features. This paper presents a scoping review related to PHR systems that achieve three characteristics: integrated, reliable and cloud-based. We found 101 articles that addressed those characteristics. We identified four main research topics: proposal/developed systems, PHR recommendations for development, system integration and standards, and security and privacy. Integration is tackled with HL7 CDA standard. Information reliability is based in ABE security-privacy mechanism. Cloud-based technology access is achieved via SOA.
Token-passing Optimal Reduction with Embedded Read-back
Venue: arXiv cs.LO 2016
Year: 2016

We introduce a new interaction net implementation of optimal reduction for the pure untyped lambda calculus. Unlike others, our implementation allows to reach normal form regardless of the interaction net reduction strategy using the approach of so-called token-passing nets and a non-deterministic extension for interaction nets. Another new feature is the read-back mechanism implemented without leaving the formalism of interaction nets.
Efficient Completion of Weighted Automata
Venue: arXiv cs.DS 2016
Year: 2016

We consider directed graphs with edge labels from a semiring. We present an algorithm that allows efficient execution of queries for existence and weights of paths, and allows updates of the graph: adding nodes and edges, and changing weights of existing edges. We apply this method in the construction of matchbound certificates for automatically proving termination of string rewriting. We re-implement the decomposition/completion algorithm of Endrullis et al. (2006) in our framework, and achieve comparable performance.
Blending Entropy: A Term for Addressing Information Density in Mediated Reality
Venue: arXiv cs.HC 2016
Year: 2016

The virtuality continuum describes the degrees of positive virtuality under the umbrella term mixed reality. Besides adding virtual information within a mixed environment, diminished reality aims at reducing real world information. Mann defined the term mediated reality (MR), which also considered diminished reality, but without the possibility to describe different degrees of fusion between a mixed and a diminished reality. That is why this work defines the new term blending entropy that captures the relations between a mixed and a diminished reality. The blending entropy is based on the information density of the mediated reality and the actual area the user has to comprehend, which is named perceptual frustum. We describe the blending entropy's twodimensional dependencies and detail important points in the blending entropy's space.
Estimating the Distance Between Macro Base Station and Users in Heterogeneous Networks
Venue: arXiv cs.IT 2016
Year: 2016

In underlay heterogeneous networks (HetNets), the distance between a macro base station (MBS) and a macro user (MU) is crucial for a small-cell based station (SBS) to control the interference to the MU and achieve the coexistence. To obtain the distance between the MBS and the MU, the SBS needs a backhaul link from the macro system, such that the macro system is able to transmit the information of the distance to the SBS through the backhaul link. However, there may not exist any backhaul link from the macro system to the SBS in practical situations. Thus, it is challenging for the SBS to obtain the distance. To deal with this issue, we propose a median based (MB) estimator for the SBS to obtain the distance between the MBS and the MU without any backhaul link. Numerical results show that the estimation error of the MB estimator can be as small as $4\%$.
Towards Deep Compositional Networks
Venue: arXiv cs.CV 2016
Year: 2016

Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of explicit structure in features, which often leads to overfitting, absence of reconstruction from partial observations and limited generative abilities. Explicit structure is inherent in hierarchical compositional models, however, these lack the ability to optimize a well-defined cost function. We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function. Our experiments on two datasets show that the proposed compositional model performs on a par with standard CNNs on discriminative tasks, while, due to explicit modeling of the structure in the feature units, affording a straight-forward visualization of parts and faster inference due to separability of the units. Actions
Multimodal Attention for Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline.
SEAL's operating manual: a Spatially-bounded Economic Agent-based Lab
Venue: arXiv cs.MA 2016
Year: 2016

This text reports in detail how SEAL, a modeling framework for the economy based on individual agents and firms, works. Thus, it aims to be an usage manual for those wishing to use SEAL or SEAL's results. As a reference work, theoretical and research studies are only cited. SEAL is thought as a Lab that enables the simulation of the economy with spatially bounded microeconomic-based computational agents. Part of the novelty of SEAL comes from the possibility of simulating the economy in space and the instantiation of different public offices, i.e. government institutions, with embedded markets and actual data. SEAL is designed for Public Policy analysis, specifically those related to Public Finance, Taxes and Real Estate.
Opting Into Optimal Matchings
Venue: arXiv cs.DS 2016
Year: 2016

We revisit the problem of designing optimal, individually rational matching mechanisms (in a general sense, allowing for cycles in directed graphs), where each player --- who is associated with a subset of vertices --- matches as many of his own vertices when he opts into the matching mechanism as when he opts out. We offer a new perspective on this problem by considering an arbitrary graph, but assuming that vertices are associated with players at random. Our main result asserts that, under certain conditions, any fixed optimal matching is likely to be individually rational up to lower-order terms. We also show that a simple and practical mechanism is (fully) individually rational, and likely to be optimal up to lower-order terms. We discuss the implications of our results for market design in general, and kidney exchange in particular.
A Semi-Potential for Finite and Infinite Sequential Games (Extended Abstract)
Venue: arXiv cs.GT 2016
Year: 2016

We consider a dynamical approach to sequential games. By restricting the convertibility relation over strategy profiles, we obtain a semi-potential (in the sense of Kukushkin), and we show that in finite games the corresponding restriction of better-response dynamics will converge to a Nash equilibrium in quadratic time. Convergence happens on a per-player basis, and even in the presence of players with cyclic preferences, the players with acyclic preferences will stabilize. Thus, we obtain a candidate notion for rationality in the presence of irrational agents. Moreover, the restriction of convertibility can be justified by a conservative updating of beliefs about the other players strategies. For infinite sequential games we can retain convergence to a Nash equilibrium (in some sense), if the preferences are given by continuous payoff functions; or obtain a transfinite convergence if the outcome sets of the game are Delta^0_2 sets.
Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging
Venue: arXiv cs.LG 2016
Year: 2016

Magnetic resonance imaging (MRI) nowadays serves as an important modality for diagnostic and therapeutic guidance in clinics. However, the {\it slow acquisition} process, the dynamic deformation of organs, as well as the need for {\it real-time} reconstruction, pose major challenges toward obtaining artifact-free images. To cope with these challenges, the present paper advocates a novel subspace learning framework that permeates benefits from parallel factor (PARAFAC) decomposition of tensors (multiway data) to low-rank modeling of temporal sequence of images. Treating images as multiway data arrays, the novel method preserves spatial structures and unravels the latent correlations across various dimensions by means of the tensor subspace. Leveraging the spatio-temporal correlation of images, Tykhonov regularization is adopted as a rank surrogate for a least-squares optimization program. Alteranating majorization minimization is adopted to develop online algorithms that recursively procure the reconstruction upon arrival of a new undersampled $k$-space frame. The developed algorithms are {\it provably convergent} and highly {\it parallelizable} with lightweight FFT tasks per iteration. To further accelerate the acquisition process, randomized subsampling policies are devised that leverage intermediate estimates of the tensor subspace, offered by the online scheme, to {\it randomly} acquire {\it informative} $k$-space samples. In a nutshell, the novel approach enables tracking motion dynamics under low acquisition rates `on the fly.' GPU-based tests with real {\it in vivo} MRI datasets of cardiac cine images corroborate the merits of the novel approach relative to state-of-the-art alternatives.
Design of a High-Performance High-Pass Generalized Integrator Based Single-Phase PLL
Venue: arXiv cs.SY 2016
Year: 2016

Grid-interactive power converters are normally synchronized to the grid using phase-locked loops (PLLs). The performance of the PLLs is affected by the non-ideal conditions in the sensed grid voltage such as harmonics, frequency deviations and dc offsets in single-phase systems. In this paper, a single-phase PLL is presented to mitigate the effects of these non-idealities. This PLL is based on the popular second order generalized integrator (SOGI) structure. The SOGI structure is modified to eliminate of the effects of input dc offsets. The resulting SOGI structure has a high-pass filtering property. Hence, this PLL is termed as high-pass generalized integrator based PLL (HGI-PLL). It has fixed parameters which reduces the implementation complexity and aids in the implementation in low-end digital controllers. The HGI-PLL is shown to have least resource utilization among the SOGI based PLLs with dc cancelling capability. Systematic design methods are evolved leading to the design that limits the unit vector THD to within 1% for given non-ideal input conditions in terms of frequency deviation and harmonic distortion. The proposed designs achieve the fastest transient response. The performance of this PLL has been verified experimentally. The results are found to agree with the theoretical prediction.
HMD Vision-based Teleoperating UGV and UAV for Hostile Environment using Deep Learning
Venue: arXiv cs.RO 2016
Year: 2016

The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-time video data to a remote cloud server. With the advancement in machine learning algorithms in the field of computer vision, we incorporate state of the art deep convolutional neural networks to identify and predict individuals with malevolent intent. The classification is performed on every frame of the video stream by the trained network in the cloud server. The predicted output of the network is overlaid on the video stream with specific colour marks and prediction percentage. Finally the data is resized into half-side by side format and streamed to the head mount display worn by the human controller which facilitates first person view of the scenario. The ground vehicle is also coupled with an unmanned aerial vehicle for aerial surveillance. The proposed scheme is an assistive system and the final decision evidently lies with the human handler.
Color Spanning Annulus: Square, Rectangle and Equilateral Triangle
Venue: arXiv cs.CG 2016
Year: 2016

In this paper, we study different variations of minimum width color-spanning annulus problem among a set of points $P=\{p_1,p_2,\ldots,p_n\}$ in $I\!\!R^2$, where each point is assigned with a color in $\{1, 2, \ldots, k\}$. We present algorithms for finding a minimum width color-spanning axis parallel square annulus $(CSSA)$, minimum width color spanning axis parallel rectangular annulus $(CSRA)$, and minimum width color-spanning equilateral triangular annulus of fixed orientation $(CSETA)$. The time complexities of computing (i) a $CSSA$ is $O(n^3+n^2k\log k)$ which is an improvement by a factor $n$ over the existing result on this problem, (ii) that for a $CSRA$ is $O(n^4\log n)$, and for (iii) a $CSETA$ is $O(n^3k)$. The space complexity of all the algorithms is $O(k)$.
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)
Venue: arXiv cs.NA 2016
Year: 2016

The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country, from the 24th to 26th of August 2016. The workshop venue was at the Aalborg University campus. One implicit objective of this biennial workshop is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For this third edition, iTWIST'16 gathered about 50 international participants and features 8 invited talks, 12 oral presentations, and 12 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing (e.g., optics, computer vision, genomics, biomedical, digital communication, channel estimation, astronomy); Application of sparse models in non-convex/non-linear inverse problems (e.g., phase retrieval, blind deconvolution, self calibration); Approximate probabilistic inference for sparse problems; Sparse machine learning and inference; "Blind" inverse problems and dictionary learning; Optimization for sparse modelling; Information theory, geometry and randomness; Sparsity? What's next? (Discrete-valued signals; Union of low-dimensional spaces, Cosparsity, mixed/group norm, model-based, low-complexity models, ...); Matrix/manifold sensing/processing (graph, low-rank approximation, ...); Complexity/accuracy tradeoffs in numerical methods/optimization; Electronic/optical compressive sensors (hardware).
Structured Perfect Bayesian Equilibrium in Infinite Horizon Dynamic Games with Asymmetric Information
Venue: arXiv cs.GT 2016
Year: 2016

In dynamic games with asymmetric information structure, the widely used concept of equilibrium is perfect Bayesian equilibrium (PBE). This is expressed as a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. Unlike symmetric information dynamic games, where subgame perfect equilibrium (SPE) is the natural equilibrium concept, to date there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper we find a subset of PBE for an infinite horizon discounted reward asymmetric information dynamic game. We refer to it as Structured PBE or SPBE; in SPBE, any agents' strategy depends on the public history only through a common public belief and on private history only through the respective agents' latest private information (his private type). The public belief acts as a summary of all the relevant past information and it's dimension does not increase with time. The motivation for this comes the common information approach proposed in Nayyar et al. (2013) for solving decentralized team (non-strategic) resource allocation problems with asymmetric information. We calculate SPBE by solving a single-shot fixed-point equation and a corresponding forward recursive algorithm. We demonstrate our methodology by means of a public goods example.
Parallel Dynamics Computation using Prefix Sum Operations
Venue: arXiv cs.RO 2016
Year: 2016

We propose a new parallel framework for fast computation of inverse and forward dynamics of articulated robots based on prefix sums (scans). We re-investigate the well-known recursive Newton-Euler formulation of robot dynamics and show that the forward-backward propagation process for robot inverse dynamics is equivalent to two scan operations on certain semigroups. We show that the state-of-the-art forward dynamics algorithms may almost completely be cast into a sequence of scan operations, with unscannable parts clearly identified. This suggests a serial-parallel hybrid approach for systems with a moderate number of links. We implement our scan based algorithms on Nvidia CUDA platform with performance compared with multithreading CPU-based recursive algorithms; a significant level of acceleration is demonstrated.