OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
Venue: arXiv cs.CV 2016
Year: 2016

In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonlinear spectral unmixing method following the recent multilinear mixing model of [1], which includes an infinite number of terms related to interactions between different endmembers. The proposed unmixing method is unsupervised in the sense that the endmembers are estimated jointly with the abundances and other parameters of interest, i.e., the transition probability of undergoing further interactions. Non-negativity and sum-to one constraints are imposed on abundances while only nonnegativity is considered for endmembers. The resulting unmixing problem is formulated as a constrained nonlinear optimization problem, which is solved by a block coordinate descent strategy, consisting of updating the endmembers, abundances and transition probability iteratively. The proposed method is evaluated and compared with linear unmixing methods for synthetic and real hyperspectral datasets acquired by the AVIRIS sensor. The advantage of using non-linear unmixing as opposed to linear unmixing is clearly shown in these examples.
A Sparse and Low-Rank Optimization Framework for Index Coding via Riemannian Optimization
Venue: arXiv cs.IT 2016
Year: 2016

Side information provides a pivotal role for message delivery in many communication scenarios to accommodate increasingly large data sets, e.g., caching networks. Although index coding provides a fundamental modeling framework to exploit the benefits of side information, the index coding problem itself still remains open and only a few instances have been solved. In this paper, we propose a novel sparse and low- rank optimization modeling framework for the index coding problem to characterize the tradeoff between the amount of side information and the achievable data rate. Specifically, sparsity of the model measures the amount of side information, while low- rankness represents the achievable data rate. The resulting sparse and low-rank optimization problem has non-convex sparsity inducing objective and non-convex rank constraint. To address the coupled challenges in objective and constraint, we propose a novel Riemannian optimization framework by exploiting the quotient manifold geometry of fixed-rank matrices, accompanied by a smooth sparsity inducing surrogate. Simulation results demonstrate the appealing sparsity and low-rankness tradeoff in the proposed model, thereby revealing the tradeoff between the amount of side information and the achievable data rate in the index coding problem.
Some Closed Classes of Three-Valued Logic Generated by Periodic Symmetric Functions
Venue: arXiv cs.LO 2016
Year: 2016

Closed classes of three-valued logic generated by periodic symmetric funtions that equal $1$ in tuples from $\{1,2\}^n$ and equal $0$ on the rest tuples are considered. Criteria for bases existence and finite bases existence for these classes is obtained.
Composing applications with OntoCompo
Venue: arXiv cs.HC 2016
Year: 2016

In this paper, we present an ontology-based approach to compose applications while preserving their former look. Our composition process relies on the manipulation of User Interfaces (UI) and on several ontologies describing relationships between tasks, UI and Functionalities. Our tool, called OntoCompo, supports compositions realized by the developer thanks to the selection, extraction and positioning of UI elements to constitute the newapplication.
A Generalized Erasure Channel in the Sense of Polarization for Binary Erasure Channels
Venue: arXiv cs.IT 2016
Year: 2016

The polar transformation of a binary erasure channel (BEC) can be exactly approximated by other BECs. Arıkan proposed that polar codes for a BEC can be efficiently constructed by using its useful property. This study proposes a new class of arbitrary input generalized erasure channels, which can be exactly approximated the polar transformation by other same channel models, as with the BEC. One of the main results is the recursive formulas of the polar transformation of the proposed channel. In the study, we evaluate the polar transformation by using the $α$-mutual information. Particularly, when the input alphabet size is a prime power, we examines the following: (i) inequalities for the average of the $α$-mutual information of the proposed channel after the one-step polar transformation, and (ii) the exact proportion of polarizations of the $α$-mutual information of proposed channels in infinite number of polar transformations.
A Singularly Perturbed Boundary Value Problems with Fractional Powers of Elliptic Operators
Venue: arXiv cs.NA 2016
Year: 2016

A boundary value problem for a fractional power $0 < \varepsilon < 1$ of the second-order elliptic operator is considered. The boundary value problem is singularly perturbed when $\varepsilon \rightarrow 0$. It is solved numerically using a time-dependent problem for a pseudo-parabolic equation. For the auxiliary Cauchy problem, the standard two-level schemes with weights are applied. The numerical results are presented for a model two-dimen\-sional boundary value problem with a fractional power of an elliptic operator. Our work focuses on the solution of the boundary value problem with $0 < \varepsilon \ll 1$.
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
Venue: arXiv cs.LG 2016
Year: 2016

We introduce a novel artificial neural network architecture that integrates robustness to adversarial input in the network structure. The main idea of our approach is to force the network to make predictions on what the given instance of the class under consideration would look like and subsequently test those predictions. By forcing the network to redraw the relevant parts of the image and subsequently comparing this new image to the original, we are having the network give a "proof" of the presence of the object.
Community structure and interaction dynamics through the lens of quotes
Venue: arXiv cs.SI 2016
Year: 2016

This is the first work investigating community structure and interaction dynamics through the lens of quotes in online discussion forums. We examine four forums of different size, language, and topic. Quote usage, which is surprisingly consistent over time and users, appears to have an important role in aiding intra-thread navigation, and uncovers a hidden "social" structure in communities otherwise lacking all trappings (from friends and followers to reputations) of today's social networks.
Liposomes versus metallic nanostructures: differences in the process of knowledge translation in cancer
Venue: arXiv cs.DL 2016
Year: 2016

This research maps the knowledge translation process for two different types of nanotechnologies applied to cancer: liposomes and metallic nanostructures (MNs). We performed a structural analysis of citation networks and text mining supported in controlled vocabularies. In the case of liposomes, our results identify subnetworks (invisible colleges) associated with different therapeutic strategies: nanopharmacology, hyperthermia, and gene therapy. Only in the pharmacological strategy was an organized knowledge translation process identified, which, however, is monopolized by the liposomal doxorubicins. In the case of MNs, subnetworks are not differentiated by the type of therapeutic strategy, and the content of the documents is still basic research. Research on MNs is highly focused on developing a combination of molecular imaging and photothermal therapy.
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
Venue: arXiv cs.CV 2016
Year: 2016

Content-based image retrieval (CBIR) in large medical image archives is a challenging and necessary task. Generally, different feature extraction methods are used to assign expressive and invariant features to each image such that the search for similar images comes down to feature classification and/or matching. The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes. We combine neural codes for global classification with Radon barcodes for the final retrieval. We also examine image search based on regions of interest (ROI) matching after image retrieval. The IRMA dataset with more than 14,000 x-rays images is used to evaluate the performance of our method. Experimental results show that our approach is superior to many published works.
A Parallel Solution to Finding Nodal Neighbors in Generic Meshes
Venue: arXiv cs.CG 2016
Year: 2016

In this paper we specifically present a parallel solution to finding the one-ring neighboring nodes and elements for each vertex in generic meshes. The finding of nodal neighbors is computationally straightforward but expensive for large meshes. To improve the efficiency, the parallelism is adopted by utilizing the modern Graphics Processing Unit (GPU). The presented parallel solution is heavily dependent on the parallel sorting, scan, and reduction, and can be applied to determine both the neighboring nodes and elements. To evaluate the performance, the parallel solution is compared to the corresponding serial solution. Experimental results show that: our parallel solution can achieve the speedups of approximately 55 and 90 over the corresponding serial solution for finding neighboring nodes and elements, respectively. Our parallel solution is efficient and easy to implement, but requires the allocation of large device memory.
Referenced Publication Year Spectroscopy (RPYS) and Algorithmic Historiography: The Bibliometric Reconstruction of András Schubert's Œuvre
Venue: arXiv cs.DL 2016
Year: 2016

Referenced Publication Year Spectroscopy (RPYS) was recently introduced as a method to analyze the historical roots of research fields and groups or institutions. RPYS maps the distribution of the publication years of the cited references in a document set. In this study, we apply this methodology to the œuvre of an individual researcher on the occasion of a Festschrift for András Schubert's 70th birthday. We discuss the different options of RPYS in relation to one another (e.g. Multi-RPYS), and in relation to the longer-term research program of algorithmic historiography (e.g., HistCite) based on Schubert's publications (n=172) and cited references therein as a bibliographic domain in scientometrics. Main path analysis and Multi-RPYS of the citation network are used to show the changes and continuities in Schubert's intellectual career. Diachronic and static decomposition of a document set can lead to different results, while the analytically distinguishable lines of research may overlap and interact over time, and intermittent.
Automatic Segmentation of Dynamic Objects from an Image Pair
Venue: arXiv cs.CV 2016
Year: 2016

Automatic segmentation of objects from a single image is a challenging problem which generally requires training on large number of images. We consider the problem of automatically segmenting only the dynamic objects from a given pair of images of a scene captured from different positions. We exploit dense correspondences along with saliency measures in order to first localize the interest points on the dynamic objects from the two images. We propose a novel approach based on techniques from computational geometry in order to automatically segment the dynamic objects from both the images using a top-down segmentation strategy. We discuss how the proposed approach is unique in novelty compared to other state-of-the-art segmentation algorithms. We show that the proposed approach for segmentation is efficient in handling large motions and is able to achieve very good segmentation of the objects for different scenes. We analyse the results with respect to the manually marked ground truth segmentation masks created using our own dataset and provide key observations in order to improve the work in future.
Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions
Venue: arXiv cs.MA 2016
Year: 2016

Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.
Analysis of Human-Body Blockage in Urban Millimeter-Wave Cellular Communications
Venue: arXiv cs.NI 2016
Year: 2016

The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to "blocking" caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.
Energy Efficient Downlink Transmission for Multi-cell Massive DAS with Pilot Contamination
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the energy efficiency (EE) of a downlink multi-cell massive distributed antenna system (DAS) in the presence of pilot contamination (PC), where the antennas are clustered on the remote radio heads (RRHs). We employ a practical power consumption model by considering the transmit power, the circuit power, and the backhaul power, in contrast to most of the existing works which focus on co-located antenna systems (CAS) where the backhaul power is negligible. For a given average user rate, we consider the problem of maximizing the EE with respect to the number of each RRH antennas $n$, the number of RRHs $M$, the number of users $K$, and study the impact of system parameters on the optimal $n$, $M$ and $K$. Specifically, by applying random matrix theory, we derive the closed-form expressions of the optimal $n$, and find the solution of the optimal $M$ and $K$, under a simplified channel model with maximum ratio transmission. From the results, we find that to achieve the optimal EE, a large number of antennas is needed for a given user rate and PC. As the number of users increases, EE can be improved further by having more RRHs and antennas. Moreover, if the backhauling power is not large, massive DAS can be more energy efficient than massive CAS. These insights provide a useful guide to practical deployment of massive DAS.
Efficient Dictionary Learning with Sparseness-Enforcing Projections
Venue: arXiv cs.LG 2016
Year: 2016

Learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks. This paper studies the optimization of dictionaries on image data where the representation is enforced to be explicitly sparse with respect to a smooth, normalized sparseness measure. This involves the computation of Euclidean projections onto level sets of the sparseness measure. While previous algorithms for this optimization problem had at least quasi-linear time complexity, here the first algorithm with linear time complexity and constant space complexity is proposed. The key for this is the mathematically rigorous derivation of a characterization of the projection's result based on a soft-shrinkage function. This theory is applied in an original algorithm called Easy Dictionary Learning (EZDL), which learns dictionaries with a simple and fast-to-compute Hebbian-like learning rule. The new algorithm is efficient, expressive and particularly simple to implement. It is demonstrated that despite its simplicity, the proposed learning algorithm is able to generate a rich variety of dictionaries, in particular a topographic organization of atoms or separable atoms. Further, the dictionaries are as expressive as those of benchmark learning algorithms in terms of the reproduction quality on entire images, and result in an equivalent denoising performance. EZDL learns approximately 30 % faster than the already very efficient Online Dictionary Learning algorithm, and is therefore eligible for rapid data set analysis and problems with vast quantities of learning samples.
LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors
Venue: arXiv cs.DC 2016
Year: 2016

Scan (or prefix sum) is a fundamental and widely used primitive in parallel computing. In this paper, we present LightScan, a faster parallel scan primitive for CUDA-enabled GPUs, which investigates a hybrid model combining intra-block computation and inter-block communication to perform a scan. Our algorithm employs warp shuffle functions to implement fast intra-block computation and takes advantage of globally coherent L2 cache and the associated parallel thread execution (PTX) assembly instructions to realize lightweight inter-block communication. Performance evaluation using a single Tesla K40c GPU shows that LightScan outperforms existing GPU algorithms and implementations, and yields a speedup of up to 2.1, 2.4, 1.5 and 1.2 over the leading CUDPP, Thrust, ModernGPU and CUB implementations running on the same GPU, respectively. Furthermore, LightScan runs up to 8.9 and 257.3 times faster than Intel TBB running on 16 CPU cores and an Intel Xeon Phi 5110P coprocessor, respectively. Source code of LightScan is available at http://cupbb.sourceforge.net.
Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance
Venue: arXiv cs.CV 2016
Year: 2016

Understanding images with people often entails understanding their \emph{interactions} with other objects or people. As such, given a novel image, a vision system ought to infer which other objects/people play an important role in a given person's activity. However, existing methods are limited to learning action-specific interactions (e.g., how the pose of a tennis player relates to the position of his racquet when serving the ball) for improved recognition, making them unequipped to reason about novel interactions with actions or objects unobserved in the training data. We propose to predict the "interactee" in novel images---that is, to localize the \emph{object} of a person's action. Given an arbitrary image with a detected person, the goal is to produce a saliency map indicating the most likely positions and scales where that person's interactee would be found. To that end, we explore ways to learn the generic, action-independent connections between (a) representations of a person's pose, gaze, and scene cues and (b) the interactee object's position and scale. We provide results on a newly collected UT Interactee dataset spanning more than 10,000 images from SUN, PASCAL, and COCO. We show that the proposed interaction-informed saliency metric has practical utility for four tasks: contextual object detection, image retargeting, predicting object importance, and data-driven natural language scene description. All four scenarios reveal the value in linking the subject to its object in order to understand the story of an image.
Biogeography-Based Combinatorial Strategy for Efficient AUV Motion Planning and Task-Time Management
Venue: arXiv cs.RO 2016
Year: 2016

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle's mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the biogeography-based optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.
Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract signature features from the input pen-tip trajectory, a fully convolutional network to learn informative representation, a sequence modeling layer to make per-frame predictions on the input sequence and a transcription layer to translate the predictions into a label sequence. The FCRN is end-to-end trainable in contrast to conventional methods whose components are separately trained and tuned. We also present a refined beam search method that efficiently integrates the language model to decode the FCRN and significantly improve the recognition results. We evaluate the performance of the proposed method on the test sets from the databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition competition, and both achieve state-of-the-art performance with correct rates of 96.40% and 95.00%, respectively.