OpenRaiser/Intern-Atlas huggingface dataset:
On the Capacity of the AWGN Channel with Additive Radar Interference
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the capacity of a communications channel that, in addition to additive white Gaussian noise, also suffers from interference caused by a co-existing radar transmission. The radar interference (of short duty-cycle and of much wider bandwidth than the intended communication signal) is modeled as an additive term whose amplitude is known and constant, but whose phase is independent and identically uniformly distributed at each channel use. The capacity achieving input distribution, under the standard average power constraint, is shown to have independent modulo and phase. The phase is uniformly distributed in $[0,2π]$. The modulo is discrete with countably infinitly many mass points, but only finitely many in any bounded interval. From numerical evaluations, a proper-complex Gaussian input is seen to perform quite well for weak radar interference. We also show that for very large radar interference, capacity is equal to $1/2\log (1 + S)$ and a proper-complex Gaussian input achieves it. It is concluded that the presence of the radar interference results in a loss of half of the degrees of freedom compared to an AWGN channel without radar interference.
Interaction Information for Causal Inference: The Case of Directed Triangle
Venue: arXiv cs.AI 2017
Year: 2017

Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the information, which is shared in any proper subset of those variables. Unlike (conditional) mutual information, which is always non-negative, interaction information can be negative. We utilize this property to find the direction of causal influences among variables in a triangle topology under some mild assumptions.
Covert Communication with Finite Blocklength in AWGN Channels
Venue: arXiv cs.IT 2017
Year: 2017

Covert communication is to achieve a reliable transmission from a transmitter to a receiver while guaranteeing an arbitrarily small probability of this transmission being detected by a warden. In this work, we study the covert communication in AWGN channels with finite blocklength, in which the number of channel uses is finite. Specifically, we analytically prove that the entire block (all available channel uses) should be utilized to maximize the effective throughput of the transmission subject to a predetermined covert requirement. This is a nontrivial result because more channel uses results in more observations at the warden for detecting the transmission. We also determine the maximum allowable transmit power per channel use, which is shown to decrease as the blocklength increases. Despite the decrease in the maximum allowable transmit power per channel use, the maximum allowable total power over the entire block is proved to increase with the blocklength, which leads to the fact that the effective throughput increases with the blocklength.
Validating and describing linked data portals using shapes
Venue: arXiv cs.DB 2017
Year: 2017

Linked data portals need to be able to advertise and describe the structure of their content. A sufficiently expressive and intuitive schema language will allow portals to communicate these structures. Validation tools will aid in the publication and maintenance of linked data and increase their quality. Two schema language proposals have recently emerged for describing the structures of RDF graphs: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In this paper we describe how these formalisms can be used in the development of a linked data portal to describe and validate its contents. As a use case, we specify a data model inspired by the WebIndex data model, a medium size linked data portal, using both ShEx and SHACL, and we propose a benchmark that can generate compliant test data structures of any size. We then perform some preliminary experiments showing performance of one validation engine based on ShEx.
Deep Submodular Functions
Venue: arXiv cs.LG 2017
Year: 2017

We start with an overview of a class of submodular functions called SCMMs (sums of concave composed with non-negative modular functions plus a final arbitrary modular). We then define a new class of submodular functions we call {\em deep submodular functions} or DSFs. We show that DSFs are a flexible parametric family of submodular functions that share many of the properties and advantages of deep neural networks (DNNs). DSFs can be motivated by considering a hierarchy of descriptive concepts over ground elements and where one wishes to allow submodular interaction throughout this hierarchy. Results in this paper show that DSFs constitute a strictly larger class of submodular functions than SCMMs. We show that, for any integer $k>0$, there are $k$-layer DSFs that cannot be represented by a $k'$-layer DSF for any $k'<k$. This implies that, like DNNs, there is a utility to depth, but unlike DNNs, the family of DSFs strictly increase with depth. Despite this, we show (using a "backpropagation" like method) that DSFs, even with arbitrarily large $k$, do not comprise all submodular functions. In offering the above results, we also define the notion of an antitone superdifferential of a concave function and show how this relates to submodular functions (in general), DSFs (in particular), negative second-order partial derivatives, continuous submodularity, and concave extensions. To further motivate our analysis, we provide various special case results from matroid theory, comparing DSFs with forms of matroid rank, in particular the laminar matroid. Lastly, we discuss strategies to learn DSFs, and define the classes of deep supermodular functions, deep difference of submodular functions, and deep multivariate submodular functions, and discuss where these can be useful in applications.
Improved Phantom Cell deployment for Capacity Enhancement
Venue: arXiv cs.NI 2017
Year: 2017

Vastly increasing capacity and coverage demand in communication networks accompanied by energy efficiency challenge is getting attraction in research topics of this area. In this paper, an improved structure of Phantom cell heterogeneous networks (HetNets) is proposed to fulfil these requirements in the next generation cellular networks.It will be shown that the proposed orthogonal frequency-division multiple access (OFDMA) based structure can be employed in both indoor and outdoor environments by applying two individual frequency bands. Furthermore, the resource allocation problem of the proposed structure is investigated in downlink path. To this aim, a proper algorithm is presented in order to maximize the total throughput of all phantom cell users' equipments with regard to the protected minimum network capacity of the existing macrocell. To fulfil the objective goal, an iterative approach is employed in which OFDM subchannels and power transmitted by base stations are sequentially assigned and optimized at each step for every single frequency band. It is showed that the overall joint subchannel and power allocation algorithm converges to local maximum of the original designed problem. Performance improvement of the proposed algorithm is confirmed in both indoor and outdoor environments by numerical results.
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
Venue: arXiv cs.LG 2017
Year: 2017

We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition, we also constrain the activations to 8-bits thus enabling sub 8-bit full integer inference pipeline. Our method uses smaller clusters of N filters with a common scaling factor to minimize the quantization loss, while also maximizing the number of ternary operations. We show that with a cluster size of N=4 on Resnet-101, can achieve 71.8% TOP-1 accuracy, within 6% of the best full precision results while replacing ~85% of all multiplications with 8-bit accumulations. Using the same method with 4-bit weights achieves 76.3% TOP-1 accuracy which within 2% of the full precision result. We also study the impact of the size of the cluster on both performance and accuracy, larger cluster sizes N=64 can replace ~98% of the multiplications with ternary operations but introduces significant drop in accuracy which necessitates fine tuning the parameters with retraining the network at lower precision. To address this we have also trained low-precision Resnet-50 with 8-bit activations and ternary weights by pre-initializing the network with full precision weights and achieve 68.9% TOP-1 accuracy within 4 additional epochs. Our final quantized model can run on a full 8-bit compute pipeline, with a potential 16x improvement in performance compared to baseline full-precision models.
A novel method for automatic localization of joint area on knee plain radiographs
Venue: arXiv cs.CV 2017
Year: 2017

Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computer-aided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tackle the issue. Here, three different datasets of knee radiographs were used (n = 473/93/77) to validate the overall performance of the method. Our pipeline consists of two parts: anatomically-based joint area proposal and their evaluation using Histogram of Oriented Gradients and the pre-trained Support Vector Machine classifier scores. The obtained results for the used datasets show the mean intersection over the union equal to: 0.84, 0.79 and 0.78. Using a high-end computer, the method allows to automatically annotate conventional knee radiographs within 14-16ms and high resolution ones within 170ms. Our results demonstrate that the developed method is suitable for large-scale analyses.
Overlay Routing for Fast Video Transfers in CDN
Venue: arXiv cs.NI 2017
Year: 2017

Content Delivery Networks (CDN) are witnessing the outburst of video streaming (e.g., personal live streaming or Video-on-Demand) where the video content, produced or accessed by mobile phones, must be quickly transferred from a point to another of the network. Whenever a user requests a video not directly available at the edge server, the CDN network must 1) identify the best location in the network where the content is stored, 2) set up a connection and 3) deliver the video as quickly as possible. For this reason, existing CDNs are adopting an overlay structure to reduce latency, leveraging the flexibility introduced by the Software Defined Networking (SDN) paradigm. In order to guarantee a satisfactory Quality of Experience (QoE) to users, the connection must respect several Quality of Service (QoS) constraints. In this paper, we focus on the sub-problem 2), by presenting an approach to efficiently compute and maintain paths in the overlay network. Our approach allows to speed up the transfer of video segments by finding minimum delay overlay paths under constraints on hop count, jitter, packet loss and relay processing capacity. The proposed algorithm provides a near-optimal solution, while drastically reducing the execution time. We show on traces collected in a real CDN that our solution allows to maximize the number of fast video transfers.
A New Method for Removing the Moire' Pattern from Images
Venue: arXiv cs.CV 2017
Year: 2017

During the last decades, denoising methods have attracted much attention of researchers. The conventional method for removing the Moire' pattern from images is using notch filters in the Frequency-domain. In this paper a new method is proposed that can achieve a better performance in comparison with the traditional method. Median filter is used in some part of spectrum of noisy images to reduce the noise. At the second part of this paper, to demonstrate the robustness of the proposed method, it is implemented for some noisy images that have moire' pattern. Experiments on noisy images with different characteristics show that the proposed method increases the PSNR values compared with previous methods.
Analysis of challenges faced by WebRTC videoconferencing and a remedial architecture
Venue: arXiv cs.MM 2017
Year: 2017

Lately, World Wide Web came up with an evolution in the niche of videoconference applications. Latest technologies give browsers a capacity to initiate real-time communications. WebRTC is one of the free and open source projects that aim at providing the users freedom to enjoy real-time communications, and it does so by following and redefining the standards. However, WebRTC is still a new project and it lacks some high-end videoconferencing features such as media mixing, recording of a session and different network conditions adaptation. This paper is an attempt at analyzing the shortcomings and challenges faced by WebRTC and proposing a Multipoint Control Unit or traditional communications entity based architecture as a solution.
An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems
Venue: arXiv cs.SD 2017
Year: 2017

Several recent polyphonic music transcription systems have utilized deep neural networks to achieve state of the art results on various benchmark datasets, pushing the envelope on framewise and note-level performance measures. Unfortunately we can observe a sort of glass ceiling effect. To investigate this effect, we provide a detailed analysis of the particular kinds of errors that state of the art deep neural transcription systems make, when trained and tested on a piano transcription task. We are ultimately forced to draw a rather disheartening conclusion: the networks seem to learn combinations of notes, and have a hard time generalizing to unseen combinations of notes. Furthermore, we speculate on various means to alleviate this situation.
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically, the pancreas demonstrates very high inter-patient anatomical variability in both its shape and volume. In this paper, we present an automated system using 3D computed tomography (CT) volumes via a two-stage cascaded approach: pancreas localization and segmentation. For the first step, we localize the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step. We introduce a fully deep-learning approach, based on an efficient application of holistically-nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. We show that our introduced localizer compares favorably to both a conventional non-deep-learning method and a recent hybrid approach based on spatial aggregation of superpixels using random forest classification. The second, segmentation, phase operates within the computed bounding box and integrates semantic mid-level cues of deeply-learned organ interior and boundary maps, obtained by two additional and separate realizations of HNNs. By integrating these two mid-level cues, our method is capable of generating boundary-preserving pixel-wise class label maps that result in the final pancreas segmentation. Quantitative evaluation is performed on a publicly available dataset of 82 patient CT scans using 4-fold cross-validation (CV). We achieve a Dice similarity coefficient (DSC) of 81.27+/-6.27% in validation, which significantly outperforms previous state-of-the art methods that report DSCs of 71.80+/-10.70% and 78.01+/-8.20%, respectively, using the same dataset.
A Characterization of Integral ISS for Switched and Time-varying Systems
Venue: arXiv cs.SY 2017
Year: 2017

Most of the existing characterizations of the integral input-to-state stability (iISS) property are not valid for time-varying or switched systems in cases where converse Lyapunov theorems for stability are not available. This note provides a characterization that is valid for switched and time-varying systems, and shows that natural extensions of some of the existing characterizations result in only sufficient but not necessary conditions. The results provided also pinpoint suitable iISS gains and relate these to supply functions and bounds on the function defining the system dynamics.
Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow
Venue: arXiv cs.CV 2017
Year: 2017

Small flying robots can perform landing maneuvers using bio-inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images on-board, limiting the update rate of divergence measurements, and thus the speed of the control loop and the robot. Event-based cameras overcome these limitations by only measuring pixel-level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This paper presents, to the best of our knowledge, the first work integrating event-based optical flow estimation into the control loop of a flying robot. We extend an existing 'local plane fitting' algorithm to obtain an improved and more computationally efficient optical flow estimation method, valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller on-board of a quadrotor. Experiments show that, using event-based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.
Sequential Convex Programming for the Efficient Verification of Parametric MDPs
Venue: arXiv cs.LO 2017
Year: 2017

Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of these computationally demanding problems belong to the subclass of signomial programs. This insight allows for a sequential optimization algorithm to efficiently compute sound but possibly suboptimal solutions. Each stage of this algorithm solves a geometric programming problem. These geometric programs are obtained by convexifying the nonconvex constraints of the original problem. Direct applications of the encodings as nonlinear pro- grams are model repair and parameter synthesis. We demonstrate the scalability and quality of our approach by well-known benchmarks
Combining Agile with Traditional V Model for Enhancement of Maturity in Software Development
Venue: arXiv cs.SE 2017
Year: 2017

In the field of software engineering there are many new archetypes are introducing day to day Improve the efficiency and effectiveness of software development. Due to dynamic environment organizations are frequently exchanging their software constraint to meet their objectives. The propose research is a new approach by integrating the traditional V model and agile methodology to combining the strength of these models while minimizing their individual weakness.The fluctuating requirements of emerging a carried software system and accumulative cost of operational software are imposing researchers and experts to determine innovative and superior means for emerging software application at slight business or at enterprise level are viewing for. Agile methodology has its own benefits but there are deficiency several of the features of traditional software development methodologies that are essential for success. Thats why an embedded approach will be the right answer for software industry rather than a pure agile approach. This research shows how agile embedded traditional can play a vital role in development of software. A survey conducted to find the impact of this approach in industry. Both qualitative and quantitative analysis performed.
Design, Analysis and Application of A Volumetric Convolutional Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their design is empirical. In the design of the VCNN, we propose a feed-forward K-means clustering algorithm to determine the filter number and size at each convolutional layer systematically. For the analysis of the VCNN, the cause of confusing classes in the output of the VCNN is explained by analyzing the relationship between the filter weights (also known as anchor vectors) from the last fully-connected layer to the output. Furthermore, a hierarchical clustering method followed by a random forest classification method is proposed to boost the classification performance among confusing classes. For the application of the VCNN, we examine the 3D shape classification problem and conduct experiments on a popular ModelNet40 dataset. The proposed VCNN offers the state-of-the-art performance among all volume-based CNN methods.
PCA-Initialized Deep Neural Networks Applied To Document Image Analysis
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we present a novel approach for initializing deep neural networks, i.e., by turning PCA into neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.