OpenRaiser/Intern-Atlas huggingface dataset:
Do ResearchGate Scores create ghost academic reputations?
Venue: arXiv cs.SI 2017
Year: 2017

The academic social network site ResearchGate (RG) has its own indicator, RG Score, for its members. The high profile nature of the site means that the RG score may be used for recruitment, promotion and other tasks for which researchers are evaluated. In response, this study investigates whether it is reasonable to employ the RG Score as evidence of scholarly reputation. For this, three different author samples were investigated. An outlier sample includes 104 authors with high values. A Nobel sample comprises 73 Nobel winners from Medicine & Physiology, Chemistry, Physics and Economics (from 1975 to 2015). A longitudinal sample includes weekly data on 4 authors with different RG Scores. The results suggest that high RG Scores are built primarily from activity related to asking and answering questions in the site. In particular, it seems impossible to get a high RG Score solely through publications. Within RG it is possible to distinguish between (passive) academics that interact little in the site and active platform users, who can get high RG Scores through engaging with others inside the site (questions, answers, social networks with influential researchers). Thus, RG Scores should not be mistaken for academic reputation indicators.
Adaptive Regularization of Some Inverse Problems in Image Analysis
Venue: arXiv cs.CV 2017
Year: 2017

We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems. The scheme automatically trades off data fidelity and regularization depending on the current data fit during the iterative optimization, so that regularization is strongest initially, and wanes as data fidelity improves, with the weight of the regularizer being minimized at convergence. We also introduce the use of a Huber loss function in both data fidelity and regularization terms, and present an efficient convex optimization algorithm based on the alternating direction method of multipliers (ADMM) using the equivalent relation between the Huber function and the proximal operator of the one-norm. We illustrate and validate our adaptive Huber-Huber model on synthetic and real images in segmentation, motion estimation, and denoising problems.
Increasing the Discovery Power and Confidence Levels of Disease Association Studies: A Survey
Venue: arXiv cs.OH 2017
Year: 2017

The majority of common diseases are influenced by multiple genetic and environmental factors such as Cancer. Even though uncovering the main causes of disease is deemed difficult due to the complexity of gene-gene and gene-environment interactions, major research efforts aim at identifying disease risk factors, especially genetic ones. Over the past decade, disease association studies have been used to uncover the susceptibility, aetiology and mechanisms of action pertaining to common diseases. In disease association studies, genetic data is analyzed in order to reveal the relationship between different types of variants, and a disease of interest. The ultimate goal of association studies is to facilitate susceptibility testing for disease prediction, early diagnosis and enhanced prognosis . Susceptibility testing and disease prediction are particularly important for diseases that can be prevented by diet, drugs or change in lifestyle. The discovered associations assist in understanding the molecular mechanisms influenced by the reported variants, and in identifying important risk factors. Current association studies suffer from several shortcomings. This report surveys the literature that addresses the shortcomings of current methods the identify genetic disease associations. In addition, it reviews the suggested solutions that either enhance some aspect of the methodologies, or complement them.
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelation function or equivalently the power spectral density (PSD) of the process is available, the Minimum Mean Squared Error (MMSE) predictor is readily obtained. In particular, it is given by a linear operator that depends on autocorrelation of the process as well as the noise power in the observed samples. The prediction becomes, however, quite challenging when the PSD of the process is unknown. In this paper, we propose a blind predictor that does not require the a priori knowledge of the PSD of the process and compare its performance with that of an MMSE predictor that has a full knowledge of the PSD. To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process. We apply the well-known atomic-norm minimization technique to the observed samples to obtain a discrete quantization of the underlying random spectrum, which we use to predict the process. Our simulation results show that this estimator has a good performance comparable with that of the MMSE estimator.
Sequential Dialogue Context Modeling for Spoken Language Understanding
Venue: arXiv cs.CL 2017
Year: 2017

Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.
Signal reconstruction via operator guiding
Venue: arXiv cs.CV 2017
Year: 2017

Signal reconstruction from a sample using an orthogonal projector onto a guiding subspace is theoretically well justified, but may be difficult to practically implement. We propose more general guiding operators, which increase signal components in the guiding subspace relative to those in a complementary subspace, e.g., iterative low-pass edge-preserving filters for super-resolution of images. Two examples of super-resolution illustrate our technology: a no-flash RGB photo guided using a high resolution flash RGB photo, and a depth image guided using a high resolution RGB photo.
Increasing the Likelihood of Finding Public Transport Riders that Face Problems Through a Data-Driven approach
Venue: arXiv cs.CY 2017
Year: 2017

The maintenance of big cities public transport service quality requires constant monitoring, which may become an expensive and time-consuming practice. The perception of quality, from the users point of view is an important aspect of quality monitoring. In this sense, we proposed a methodology for data analysis and visualization, supported by software, which allows for the structuring of estimates and assumptions of where and who seems to be having unsatisfactory experiences while making use of the public transportation in populous metropolitan areas. Moreover, it provides support in setting up a plan for on-site quality surveys. The proposed methodology increases the likelihood that, with the on-site visits, the interviewer finds users who suffer inconveniences, which influence their behavior. Simulation comparison and a small-scale pilot survey stand for the validity of the proposed method.
Wireless Sensor Network based System for the Prevention of Hospital Acquired Infections
Venue: arXiv cs.CY 2017
Year: 2017

Hospital acquired infections are a serious threat to the health and well-being of patients and medical staff within clinical units. Many of these infections arise as a consequence of medical personnel that come into contact with contaminated persons, surfaces or equipment and then with patients, without following proper hygiene procedures. In this paper we present our ongoing efforts in the development of a wireless sensor network based cyber-physical system which aims to prevent hospital infections by increasing compliance to established hygiene guidelines. The solution, currently developed under European Union funding integrates a network of sensors for monitoring clinical workflows and ambient conditions, a workflow engine that executes encoded workflow instances and monitoring software that provides real-time information in case of infection risk detection. As a motivating example, we employ the workflow in the general practitioner's office in order to comprehensively present types of sensors and their positioning in the monitored location. Using the information collected by deployed sensors, the system is capable of immediately detecting infection risks and taking action to prevent the spread of infections.
Analysis of Information Technologies Used to Insure Working Efficiency of Personnel
Venue: arXiv cs.HC 2017
Year: 2017

The work is devoted to a modern state, methods and tools of monitoring, assessment and prediction of the indicators showing physical condition of a person and his/her capabilities to perform work duties. The work contains an analysis of existing gadgets and software that allow tracking physical condition of personnel at the working place. The analysis showing significant interconnections and factors that determine a necessary level of working capacity and productivity of personnel allows organizing Work & Rest Schedule of employees in an effective manner.
Multi-Scale Spatially Weighted Local Histograms in O(1)
Venue: arXiv cs.CV 2017
Year: 2017

Weighting pixel contribution considering its location is a key feature in many fundamental image processing tasks including filtering, object modeling and distance matching. Several techniques have been proposed that incorporate Spatial information to increase the accuracy and boost the performance of detection, tracking and recognition systems at the cost of speed. But, it is still not clear how to efficiently ex- tract weighted local histograms in constant time using integral histogram. This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search. We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram.
Collaborative Descriptors: Convolutional Maps for Preprocessing
Venue: arXiv cs.CV 2017
Year: 2017

The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features. To achieve this concept, we apply convolutional maps for pre-processing, namely the convovlutional maps are used as input of hand-crafted features. We recorded an increase in the performance rate of +17.06 % (multi-class object recognition) and +24.71 % (car detection) from grayscale input to convolutional maps. Although the framework is straight-forward, the concept should be inherited for an improved representation.
4d isip: 4d implicit surface interest point detection
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a new method to detect 4D spatiotemporal interest points though an implicit surface, we refer to as the 4D-ISIP. We use a 3D volume which has a truncated signed distance function(TSDF) for every voxel to represent our 3D object model. The TSDF represents the distance between the spatial points and object surface points which is an implicit surface representation. Our novelty is to detect the points where the local neighborhood has significant variations along both spatial and temporal directions. We established a system to acquire 3D human motion dataset using only one Kinect. Experimental results show that our method can detect 4D-ISIP for different human actions.
Near Optimal Parallel Algorithms for Dynamic DFS in Undirected Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Depth first search (DFS) tree is a fundamental data structure for solving graph problems. The classical algorithm [SiComp74] for building a DFS tree requires $O(m+n)$ time for a given graph $G$ having $n$ vertices and $m$ edges. Recently, Baswana et al. [SODA16] presented a simple algorithm for updating DFS tree of an undirected graph after an edge/vertex update in $\tilde{O}(n)$ time. However, their algorithm is strictly sequential. We present an algorithm achieving similar bounds, that can be adopted easily to the parallel environment. In the parallel model, a DFS tree can be computed from scratch using $m$ processors in expected $\tilde{O}(1)$ time [SiComp90] on an EREW PRAM, whereas the best deterministic algorithm takes $\tilde{O}(\sqrt{n})$ time [SiComp90,JAlg93] on a CRCW PRAM. Our algorithm can be used to develop optimal (upto polylog n factors deterministic algorithms for maintaining fully dynamic DFS and fault tolerant DFS, of an undirected graph. 1- Parallel Fully Dynamic DFS: Given an arbitrary online sequence of vertex/edge updates, we can maintain a DFS tree of an undirected graph in $\tilde{O}(1)$ time per update using $m$ processors on an EREW PRAM. 2- Parallel Fault tolerant DFS: An undirected graph can be preprocessed to build a data structure of size O(m) such that for a set of $k$ updates (where $k$ is constant) in the graph, the updated DFS tree can be computed in $\tilde{O}(1)$ time using $n$ processors on an EREW PRAM. Moreover, our fully dynamic DFS algorithm provides, in a seamless manner, nearly optimal (upto polylog n factors) algorithms for maintaining a DFS tree in semi-streaming model and a restricted distributed model. These are the first parallel, semi-streaming and distributed algorithms for maintaining a DFS tree in the dynamic setting.
Benchmark Graphs for Practical Graph Isomorphism
Venue: arXiv cs.DS 2017
Year: 2017

The state-of-the-art solvers for the graph isomorphism problem can readily solve generic instances with tens of thousands of vertices. Indeed, experiments show that on inputs without particular combinatorial structure the algorithms scale almost linearly. In fact, it is non-trivial to create challenging instances for such solvers and the number of difficult benchmark graphs available is quite limited. We describe a construction to efficiently generate small instances for the graph isomorphism problem that are difficult or even infeasible for said solvers. Up to this point the only other available instances posing challenges for isomorphism solvers were certain incidence structures of combinatorial objects (such as projective planes, Hadamard matrices, Latin squares, etc.). Experiments show that starting from 1500 vertices our new instances are several orders of magnitude more difficult on comparable input sizes. More importantly, our method is generic and efficient in the sense that one can quickly create many isomorphism instances on a desired number of vertices. In contrast to this, said combinatorial objects are rare and difficult to generate and with the new construction it is possible to generate an abundance of instances of arbitrary size. Our construction hinges on the multipedes of Gurevich and Shelah and the Cai-Fürer-Immerman gadgets that realize a certain abelian automorphism group and have repeatedly played a role in the context of graph isomorphism. Exploring limits of such constructions, we also explain that there are group theoretic obstructions to generalizing the construction with non-abelian gadgets.
A Survey of Distant Supervision Methods using PGMs
Venue: arXiv cs.AI 2017
Year: 2017

Relation Extraction refers to the task of populating a database with tuples of the form $r(e_1, e_2)$, where $r$ is a relation and $e_1$, $e_2$ are entities. Distant supervision is one such technique which tries to automatically generate training examples based on an existing KB such as Freebase. This paper is a survey of some of the techniques in distant supervision which primarily rely on Probabilistic Graphical Models (PGMs).
Uplink Analysis of Large MU-MIMO Systems With Space-Constrained Arrays in Ricean Fading
Venue: arXiv cs.IT 2017
Year: 2017

Closed-form approximations to the expected per-terminal signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency of a large multiuser multiple-input multiple-output system are presented. Our analysis assumes correlated Ricean fading with maximum ratio combining on the uplink, where the base station (BS) is equipped with a uniform linear array (ULA) with physical size restrictions. Unlike previous studies, our model caters for the presence of unequal correlation matrices and unequal Rice factors for each terminal. As the number of BS antennas grows without bound, with a finite number of terminals, we derive the limiting expected per-terminal SINR and ergodic sum spectral efficiency of the system. Our findings suggest that with restrictions on the size of the ULA, the expected SINR saturates with increasing operating signal-to-noise-ratio (SNR) and BS antennas. Whilst unequal correlation matrices result in higher performance, the presence of strong line-of-sight (LoS) has an opposite effect. Our analysis accommodates changes in system dimensions, SNR, LoS levels, spatial correlation levels and variations in fixed physical spacings of the BS array.
A Local Prime Factor Decomposition Algorithm for Strong Product Graphs
Venue: arXiv cs.DM 2017
Year: 2017

This work is concerned with the prime factor decomposition (PFD) of strong product graphs. A new quasi-linear time algorithm for the PFD with respect to the strong product for arbitrary, finite, connected, undirected graphs is derived. Moreover, since most graphs are prime although they can have a product-like structure, also known as approximate graph products, the practical application of the well-known "classical" prime factorization algorithm is strictly limited. This new PFD algorithm is based on a local approach that covers a graph by small factorizable subgraphs and then utilizes this information to derive the global factors. Therefore, we can take advantage of this approach and derive in addition a method for the recognition of approximate graph products.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.
Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding
Venue: arXiv cs.SD 2017
Year: 2017

Advances in virtual reality have generated substantial interest in accurately reproducing and storing spatial audio in the higher order ambisonics (HOA) representation, given its rendering flexibility. Recent standardization for HOA compression adopted a framework wherein HOA data are decomposed into principal components that are then encoded by standard audio coding, i.e., frequency domain quantization and entropy coding to exploit psychoacoustic redundancy. A noted shortcoming of this approach is the occasional mismatch in principal components across blocks, and the resulting suboptimal transitions in the data fed to the audio coder. Instead, we propose a framework where singular value decomposition (SVD) is performed after transformation to the frequency domain via the modified discrete cosine transform (MDCT). This framework not only ensures smooth transition across blocks, but also enables frequency dependent SVD for better energy compaction. Moreover, we introduce a novel noise substitution technique to compensate for suppressed ambient energy in discarded higher order ambisonics channels, which significantly enhances the perceptual quality of the reconstructed HOA signal. Objective and subjective evaluation results provide evidence for the effectiveness of the proposed framework in terms of both higher compression gains and better perceptual quality, compared to existing methods.
Demographics of News Sharing in the U.S. Twittersphere
Venue: arXiv cs.SI 2017
Year: 2017

The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to be more active in terms of sharing news, biasing the news audience to the interests of these demographic groups. Our results also quantify differences in interests of news sharing across demographics, which has implications for personalized news digests.
Transreality puzzle as new genres of entertainment technology
Venue: arXiv cs.HC 2017
Year: 2017

The author considers a class of mechatronic puzzles falling in the mixed-reality category, present examples of such devices, and propose a way to categorize them. Close relationships of such devices with the Tangible User Interface are described. The device designed by the author as an illustration of a mixed reality puzzle is presented.
Performance of SWIPT-based Differential Amplify-and-Forward Relaying with Direct Link
Venue: arXiv cs.IT 2017
Year: 2017

A novel asymptotic closed-form probability density function (pdf) of the two-hop (TH) link is derived for a simultaneous wireless information and power transfer based differential amplify-and-forward system. Based on the pdf, asymptotic closed-form average bit-error rate expressions of the single TH link and the TH link with direct link combined with a linear combining scheme are both derived. Monte Carlo simulations verify the analytical expressions.