OpenRaiser/Intern-Atlas huggingface dataset:
Performance of Adaptive Link Selection with Buffer-Aided Relays in Underlay Cognitive Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate the performance of a three-node dual-hop cognitive radio network (CRN) with a half-duplex (HD) decode-and-forward (DF) buffer-aided relay. We derive expressions for the average rate and symbol error rate (SER) performance of an adaptive link selection based channel-aware buffer-aided relay (CABR) scheme that imposes peak-power and peak-interference constraints on the secondary nodes, and compare them with those of conventional non-buffer-aided relay (CNBR) and conventional buffer-aided relay (CBR) schemes for a delay-tolerant system. For finite-delay systems, we analyze the performance of a modified threshold-based scheme for fixed-rate transmission, and demonstrate that use of a last-in-first-out buffer is advantageous in some situations. We bring out the trade-offs between delay, throughput and SER. Computer simulation results are presented to demonstrate accuracy of the derived expressions.
An Upper Bound on the Sum Capacity of the Downlink Multicell Processing with Finite Backhaul Capacity
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study upper bounds on the sum capacity of the downlink multicell processing model with finite backhaul capacity for the simple case of 2 base stations and 2 mobile users. It is modelled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity, and the second hop from the base stations to the mobile users via a Gaussian interference channel. The converse is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our upper bound improves upon the existing upper bound greatly in the medium backhaul capacity range, and as a result, the gap between the upper bounds and the sum rate of the time-sharing of the known achievable schemes is significantly reduced.
Automatic Generation of Vectorized Montgomery Algorithm
Venue: arXiv cs.MS 2016
Year: 2016

Modular arithmetic is widely used in crytography and symbolic computation. This paper presents a vectorized Montgomery algorithm for modular multiplication, the key to fast modular arithmetic, that fully utilizes the SIMD instructions. We further show how the vectorized algorithm can be automatically generated by the {\SPIRAL} system, as part of the effort for automatic generation of a modular polynomial multiplication library.
Automatic Library Generation for Modular Polynomial Multiplication
Venue: arXiv cs.SC 2016
Year: 2016

Polynomial multiplication is a key algorithm underlying computer algebra systems (CAS) and its efficient implementation is crucial for the performance of CAS. In this paper we design and implement algorithms for polynomial multiplication using approaches based the fast Fourier transform (FFT) and the truncated Fourier transform (TFT). We improve on the state-of-the-art in both theoretical and practical performance. The {\SPIRAL} library generation system is extended and used to automatically generate and tune the performance of a polynomial multiplication library that is optimized for memory hierarchy, vectorization and multi-threading, using new and existing algorithms. The performance tuning has been aided by the use of automation where many code choices are generated and intelligent search is utilized to find the "best" implementation on a given architecture. The performance of autotuned implementations is comparable to, and in some cases better than, the best hand-tuned code.
Crowdsourcing Information Extraction for Biomedical Systematic Reviews
Venue: arXiv cs.HC 2016
Year: 2016

Information extraction is a critical step in the practice of conducting biomedical systematic literature reviews. Extracted structured data can be aggregated via methods such as statistical meta-analysis. Typically highly trained domain experts extract data for systematic reviews. The high expense of conducting biomedical systematic reviews has motivated researchers to explore lower cost methods that achieve similar rigor without compromising quality. Crowdsourcing represents one such promising approach. In this work-in-progress study, we designed a crowdsourcing task for biomedical information extraction. We briefly report the iterative design process and the results of two pilot testings. We found that giving more concrete examples in the task instruction can help workers better understand the task, especially for concepts that are abstract and confusing. We found a few workers completed most of the work, and our payment level appeared more attractive to workers from low-income countries. In the future, we will further evaluate our results with reference to gold standard extractions, thus assessing the feasibility of tasking crowd workers with extracting biomedical intervention information for systematic reviews.
GTApprox: surrogate modeling for industrial design
Venue: arXiv cs.MS 2016
Year: 2016

We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of test problems. In addition, we describe several applications of GTApprox to real engineering problems.
Constructions of Optimal Cyclic $(r,δ)$ Locally Repairable Codes
Venue: arXiv cs.IT 2016
Year: 2016

A code is said to be a $r$-local locally repairable code (LRC) if each of its coordinates can be repaired by accessing at most $r$ other coordinates. When some of the $r$ coordinates are also erased, the $r$-local LRC can not accomplish the local repair, which leads to the concept of $(r,δ)$-locality. A $q$-ary $[n, k]$ linear code $\cC$ is said to have $(r, δ)$-locality ($δ\ge 2$) if for each coordinate $i$, there exists a punctured subcode of $\cC$ with support containing $i$, whose length is at most $r + δ- 1$, and whose minimum distance is at least $δ$. The $(r, δ)$-LRC can tolerate $δ-1$ erasures in total, which degenerates to a $r$-local LRC when $δ=2$. A $q$-ary $(r,δ)$ LRC is called optimal if it meets the Singleton-like bound for $(r,δ)$-LRCs. A class of optimal $q$-ary cyclic $r$-local LRCs with lengths $n\mid q-1$ were constructed by Tamo, Barg, Goparaju and Calderbank based on the $q$-ary Reed-Solomon codes. In this paper, we construct a class of optimal $q$-ary cyclic $(r,δ)$-LRCs ($δ\ge 2$) with length $n\mid q-1$, which generalizes the results of Tamo \emph{et al.} Moreover, we construct a new class of optimal $q$-ary cyclic $r$-local LRCs with lengths $n\mid q+1$ and a new class of optimal $q$-ary cyclic $(r,δ)$-LRCs ($δ\ge 2$) with lengths $n\mid q+1$. The constructed optimal LRCs with length $n=q+1$ have the best-known length $q+1$ for the given finite field with size $q$ when the minimum distance is larger than $4$.
Mobile Relays for Smart Cities: Mathematical Proofs
Venue: arXiv cs.IT 2016
Year: 2016

The increasing number of connected vehicles in densely populated urban areas provides an interesting opportunity to counteract the high wireless data demands in high density and highly mobile scenarios. The idea is to support the macro base station (BS) with a secondary communication tier composed of a set of smart and connected vehicles that are in movement in the urban area. As a first step towards a comprehensive cost-benefit analysis of this architecture, this paper considers the case where these vehicles are equipped with femto-mobile Access Points (fmAPs) and constitute a mobile out-of-band relay infrastructure. In particular, three techniques to select an fmAP (if more than one is available) are proposed and the maximal feasible gain in the packet delivery rate and data rate as a function of the vehicle density, average vehicle speeds, handoff overhead cost, as well as physical layer parameters is characterized. The analytical and simulation results provide a first benchmark characterizing this architecture and the definition of guidelines for its future realistic study and implementation.
UnrealCV: Connecting Computer Vision to Unreal Engine
Venue: arXiv cs.CV 2016
Year: 2016

Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution
Venue: arXiv cs.CV 2016
Year: 2016

Image deconvolution is still to be a challenging ill-posed problem for recovering a clear image from a given blurry image, when the point spread function is known. Although competitive deconvolution methods are numerically impressive and approach theoretical limits, they are becoming more complex, making analysis, and implementation difficult. Furthermore, accurate estimation of the regularization parameter is not easy for successfully solving image deconvolution problems. In this paper, we develop an effective approach for image restoration based on one explicit image filter - guided filter. By applying the decouple of denoising and deblurring techniques to the deconvolution model, we reduce the optimization complexity and achieve a simple but effective algorithm to automatically compute the parameter in each iteration, which is based on Morozov's discrepancy principle. Experimental results demonstrate that the proposed algorithm outperforms many state-of-the-art deconvolution methods in terms of both ISNR and visual quality.
Rate-Distortion Analysis of Quantizers with Error Feedback
Venue: arXiv cs.SY 2016
Year: 2016

A Delta-Sigma modulator that is often utilized to convert analog signals into digital signals can be modeled as a static uniform quantizer with an error feedback filter. In this paper, we present a rate-distortion analysis of quantizers with error feedback including the Delta-Sigma modulators, assuming that the error owing to overloading in the static quantizer is negligible. We demonstrate that the amplitude response of the optimal error feedback filter that minimizes the mean squared quantization error can be parameterized by one parameter. This parameterization enables us to determine the optimal error feedback filter numerically. The relationship between the number of bits used for the quantization and the achievable mean squared error can be obtained using the optimal error feedback filter. This clarifies the rate-distortion property of quantizers with error feedback. Then, ideal optimal error feedback filters are approximated by practical filters using the Yule-Walker method and the linear matrix inequality-based method. Numerical examples are provided for demonstrating our analysis and synthesis.
Extreme Scale-out SuperMUC Phase 2 - lessons learned
Venue: arXiv cs.DC 2016
Year: 2016

In spring 2015, the Leibniz Supercomputing Centre (Leibniz-Rechenzentrum, LRZ), installed their new Peta-Scale System SuperMUC Phase2. Selected users were invited for a 28 day extreme scale-out block operation during which they were allowed to use the full system for their applications. The following projects participated in the extreme scale-out workshop: BQCD (Quantum Physics), SeisSol (Geophysics, Seismics), GPI-2/GASPI (Toolkit for HPC), Seven-League Hydro (Astrophysics), ILBDC (Lattice Boltzmann CFD), Iphigenie (Molecular Dynamic), FLASH (Astrophysics), GADGET (Cosmological Dynamics), PSC (Plasma Physics), waLBerla (Lattice Boltzmann CFD), Musubi (Lattice Boltzmann CFD), Vertex3D (Stellar Astrophysics), CIAO (Combustion CFD), and LS1-Mardyn (Material Science). The projects were allowed to use the machine exclusively during the 28 day period, which corresponds to a total of 63.4 million core-hours, of which 43.8 million core-hours were used by the applications, resulting in a utilization of 69%. The top 3 users were using 15.2, 6.4, and 4.7 million core-hours, respectively.
Efficiency enhancement based on allocating bizarre peaks
Venue: arXiv cs.IT 2016
Year: 2016

A new work has been proposed in this paper in order to overcome one of the main drawbacks that found in the Orthogonal Frequency Division Multiplex (OFDM) systems, namely Peak to Average Power Ratio (PAPR). Furthermore, this work will be compared with a previously published work that uses the neural network (NN) as a solution to remedy this deficiency. The proposed work could be considered as a special averaging technique (SAT), which consists of wavelet transformation in its first stage, a globally statistical adaptive detecting algorithm as a second stage; and in the third stage it replaces the affected peaks by making use of moving average filter process. In the NN work, the learning process makes use of a previously published work that is based on three linear coding techniques. In order to check the proposed work validity, a MATLAB simulation has been run and has two main variables to compare with; namely BER and CCDF curves. This is true under the same bandwidth occupancy and channel characteristics. Two types of tested data have been used; randomly generated data and a practical data that have been extracted from a funded project entitled by ECEM. From the achieved simulation results, the work that is based on SAT shows promising results in reducing the PAPR effect reached up to 80% over the work in the literature and our previously published work. This means that this work gives an extra reduction up to 15% of our previously published work. However, this achievement will be under the cost of complexity. This penalty could be optimized by imposing the NN to the SAT work in order to enhance the wireless systems performance.
A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records
Venue: arXiv cs.LG 2016
Year: 2016

Rare diseases are very difficult to identify among large number of other possible diagnoses. Better availability of patient data and improvement in machine learning algorithms empower us to tackle this problem computationally. In this paper, we target one such rare disease - cardiac amyloidosis. We aim to automate the process of identifying potential cardiac amyloidosis patients with the help of machine learning algorithms and also learn most predictive factors. With the help of experienced cardiologists, we prepared a gold standard with 73 positive (cardiac amyloidosis) and 197 negative instances. We achieved high average cross-validation F1 score of 0.98 using an ensemble machine learning classifier. Some of the predictive variables were: Age and Diagnosis of cardiac arrest, chest pain, congestive heart failure, hypertension, prim open angle glaucoma, and shoulder arthritis. Further studies are needed to validate the accuracy of the system across an entire health system and its generalizability for other diseases.
A Survey of Downlink Non-orthogonal Multiple Access for 5G Wireless Communication Networks
Venue: arXiv cs.IT 2016
Year: 2016

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for the next generation cellular communication networks. In this paper, we first discuss a simple NOMA model with two users served by a single-carrier simultaneously to illustrate its basic principles. Then, a more general model with multicarrier serving an arbitrary number of users on each subcarrier is also discussed. An overview of existing works on performance analysis, resource allocation, and multiple-input multiple-output NOMA are summarized and discussed. Furthermore, we discuss the key features of NOMA and its potential research challenges.
Sentiment Classification of Food Reviews
Venue: arXiv cs.CL 2016
Year: 2016

Sentiment analysis of reviews is a popular task in natural language processing. In this work, the goal is to predict the score of food reviews on a scale of 1 to 5 with two recurrent neural networks that are carefully tuned. As for baseline, we train a simple RNN for classification. Then we extend the baseline to GRU. In addition, we present two different methods to deal with highly skewed data, which is a common problem for reviews. Models are evaluated using accuracies.
Challenges and Opportunities in Edge Computing
Venue: arXiv cs.DC 2016
Year: 2016

Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.
Dense Motion Estimation for Smoke
Venue: arXiv cs.CV 2016
Year: 2016

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.
An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we advance a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network - hidden Markov model) hybrid systems. This numerical sampling concept averages DNN outputs produced by a finite set of feature samples (drawn from a probabilistic distortion model) to approximate the posterior likelihoods of the context-dependent HMM states. As main innovation, we propose a weighted DNN-output averaging based on a minimum classification error criterion and apply it to a probabilistic distortion model for spatial diffuseness features. The experimental evaluation is performed on the 8-channel REVERB Challenge task using a DNN-HMM hybrid system with multichannel front-end signal enhancement. We show that the recognition accuracy of the DNN-HMM hybrid system improves by incorporating uncertainty decoding based on random sampling and that the proposed weighted DNN-output averaging further reduces the word error rate scores.
DoF Analysis in a Two-Layered Heterogeneous Wireless Interference Network
Venue: arXiv cs.IT 2016
Year: 2016

Degrees of freedom (DoF) is studied in the downlink of a heterogenous wireless network modeled as a two-layered interference network. The first layer of the interference network is the backhaul layer between macro base stations (MB) and small cell base stations (SB), which is modeled as a Wyner type linear network. The second layer is the transmission layer between SBs and mobile terminals (MTs), which is modeled as a linear Wyner $L_T$ network, i.e., each MT is connected to $L_T+1$ SBs. The SBs are assumed to be half-duplex, thus restricting the per user degrees of freedom (puDoF) in the system to $1/2$. The puDoF can be further restricted by the number of antennas at the MB. For $L_T \in \{1,2\}$, the optimal puDoF can be achieved by using simple interference avoidance schemes. The increase in the connectivity of transmission layer beyond $L_T=2$ limits the achievable puDoF using only zero-forcing schemes to less than 1/2, even in the presence of large number of antennas at each MB but the optimal puDoF can be achieved by making each message available at multiple SBs. This is done by sending an appropriate linear combination to the SB to zero-force interference at the intended user. The maximum per user DoF of 1/2 can be achieved in the linear network with sufficient number of antennas using only interference avoidance schemes. These results are also extended to a more realistic hexagonal cellular model as well.
Tracking Algorithm for Microscopic Flow Data Collection
Venue: arXiv cs.CV 2016
Year: 2016

Various methods to automate traffic data collection have recently been developed by many researchers. A macroscopic data collection through image processing has been proposed. For microscopic traffic flow data, such as individual speed and time or distance headway, tracking of individual movement is needed. The tracking algorithms for pedestrian or vehicle have been developed to trace the movement of one or two pedestrians based on sign pattern, and feature detection. No research has been done to track many pedestrians or vehicles at once. This paper describes a new and fast algorithm to track the movement of many individual vehicles or pedestrians
Feedback and Timing in a Crowdsourcing Game
Venue: arXiv cs.HC 2016
Year: 2016

The present research examines two problems inherent to the creation of crowdsourcing games: how to give feedback when the right answer is not always known by the game and how much time to give players without sacrificing data quality. Taken together, the present research provides an important first step in considering how to create fun, challenging crowdsourcing games that generate quality data.
Determination of Pedestrian Flow Performance Based on Video Tracking and Microscopic Simulations
Venue: arXiv cs.CV 2016
Year: 2016

One of the objectives of understanding pedestrian behavior is to predict the effect of proposed changes in the design or evaluation of pedestrian facilities. We want to know the impact to the user of the facilities, as the design of the facilities change. That impact was traditionally evaluated by level of service standards. Another design criterion to measure the impact of design change is measured by the pedestrian flow performance index. This paper describes the determination of pedestrian flow performance based video tracking or any microscopic pedestrian simulation models. Most of pedestrian researches have been done on a macroscopic level, which is an aggregation of all pedestrian movement in pedestrian areas into flow, average speed and area module. Macroscopic level, however, does not consider the interaction between pedestrians. It is also not well suited for prediction of pedestrian flow performance in pedestrian areas or in buildings with some obstruction, that reduces the effective width of the walkways. On the other hand, the microscopic level has a more general usage and considers detail in the design. More efficient pedestrian flow can even be reached with less space. Those results have rejected the linearity assumption of space and flow in the macroscopic level.