OpenRaiser/Intern-Atlas huggingface dataset:
Design Considerations for a 5G Network Architecture
Venue: arXiv cs.NI 2017
Year: 2017

The data rates of up to 10 GB/s will characterize 5G networks telecommunications standards that are envisioned to replace the current 4G/IMT standards. The number of network-connected devices is expected to be 7 trillion by the end of this year and the traffic is expected to rise by an order of magnitude in the next 8 years. It is expected that elements of 5G will be rolled out by early 2020s to meet business and consumer demands as well as requirements of the Internet of Things. China's Ministry of Industry and Information Technology announced in September 2016 that the government-led 5G Phase-1 tests of key wireless technologies for future 5G networks were completed with satisfactory results. This paper presents an overview of the challenges facing 5G before it can be implemented to meet expected requirements of capacity, data rates, reduced latency, connectivity to massive number of devices, reduced cost and energy, and appropriate quality of experience.
Link Prediction using Top-$k$ Shortest Distances
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we apply an efficient top-$k$ shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-$k$ distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.
Optimized Data Representation for Interactive Multiview Navigation
Venue: arXiv cs.MM 2017
Year: 2017

In contrary to traditional media streaming services where a unique media content is delivered to different users, interactive multiview navigation applications enable users to choose their own viewpoints and freely navigate in a 3-D scene. The interactivity brings new challenges in addition to the classical rate-distortion trade-off, which considers only the compression performance and viewing quality. On the one hand, interactivity necessitates sufficient viewpoints for richer navigation; on the other hand, it requires to provide low bandwidth and delay costs for smooth navigation during view transitions. In this paper, we formally describe the novel trade-offs posed by the navigation interactivity and classical rate-distortion criterion. Based on an original formulation, we look for the optimal design of the data representation by introducing novel rate and distortion models and practical solving algorithms. Experiments show that the proposed data representation method outperforms the baseline solution by providing lower resource consumptions and higher visual quality in all navigation configurations, which certainly confirms the potential of the proposed data representation in practical interactive navigation systems.
Object Detection by Spatio-Temporal Analysis and Tracking of the Detected Objects in a Video with Variable Background
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin gray-value histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.
A branch-and-price approach with MILP formulation to modularity density maximization on graphs
Venue: arXiv cs.SI 2017
Year: 2017

For clustering of an undirected graph, this paper presents an exact algorithm for the maximization of modularity density, a more complicated criterion to overcome drawbacks of the well-known modularity. The problem can be interpreted as the set-partitioning problem, which reminds us of its integer linear programming (ILP) formulation. We provide a branch-and-price framework for solving this ILP, or column generation combined with branch-and-bound. Above all, we formulate the column generation subproblem to be solved repeatedly as a simpler mixed integer linear programming (MILP) problem. Acceleration techniques called the set-packing relaxation and the multiple-cutting-planes-at-a-time combined with the MILP formulation enable us to optimize the modularity density for famous test instances including ones with over 100 vertices in around four minutes by a PC. Our solution method is deterministic and the computation time is not affected by any stochastic behavior. For one of them, column generation at the root node of the branch-and-bound tree provides a fractional upper bound solution and our algorithm finds an integral optimal solution after branching.
Konzept für Bildanalysen in Hochdurchsatz-Systemen am Beispiel des Zebrabärblings
Venue: arXiv cs.CV 2017
Year: 2017

With image-based high-throughput experiments, new challenges arise in both, the design of experiments and the automated analysis. To be able to handle the massive number of single experiments and the corresponding amount of data, a comprehensive concept for the design of experiments and a new evaluation method is needed. This work proposes a new method for an optimized experiment layout that enables the determination of parameters, adapted for the needs of automated image analysis. Furthermore, a catalogue of new image analysis modules, especially developed for zebrafish analysis, is presented. The combination of both parts offers the user, usually a biologist, an approach for high-throughput zebrafish image analysis, which enables the extraction of new signals and optimizes the design of experiments. The result is a reduction of data amount, redundant information and workload as well as classification errors.
Optimally-Tuned Nonparametric Linear Equalization for Massive MU-MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum mean-square error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show that NOPE achieves the same performance as that of the L-MMSE equalizer in the large-antenna limit, and we demonstrate its efficacy in realistic, finite-dimensional systems. From a practical perspective, NOPE is computationally efficient and avoids dedicated training that is typically required for parameter estimation
A Model for Information Networks: Efficiency, Stability and Dynamics
Venue: arXiv cs.GT 2017
Year: 2017

We introduce a simple network model that is inspired by social information networks such as twitter. Agents are nodes, connecting to another agent by building a directed edge has a cost, and reaching other agents via short directed paths has a benefit; in effect, an agent wants to reach others quickly, but without the cost of directly connecting each and every one. Even in its simplest form, edges in this framework are neither substitutes or complements in general; hence, standard techniques are required to study the model's properties and dynamics do not apply. We prove that an asynchronous edge dynamics always converge to a stable network; in fact, for this convergence is fast for a range of parameters. Moreover, the set of stable networks are nontrivial and can support the type of network structures that have been observed to appear in social information networks -- from community clusters to broadcast networks, depending on the parameters many natural formations can emerge. We further study the static game, and give classes of stable and efficient networks for nontrivial parameter ranges. We close several problems, and leave many interesting ones open.
Phone-aware Neural Language Identification
Venue: arXiv cs.CL 2017
Year: 2017

Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlooked by most of existing neural LID models, although this information has been used in the conventional phonetic LID systems with a great success. We present a phone-aware neural LID architecture, which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR system. By utilizing the phonetic knowledge, the LID performance can be significantly improved. Interestingly, even if the test language is not involved in the ASR training, the phonetic knowledge still presents a large contribution. Our experiments conducted on four languages within the Babel corpus demonstrated that the phone-aware approach is highly effective.
Manifold Relevance Determination: Learning the Latent Space of Robotics
Venue: arXiv cs.RO 2017
Year: 2017

In this article we present the basics of manifold relevance determination (MRD) as introduced in \cite{mrd}, and some applications where the technology might be of particular use. Section 1 acts as a short tutorial of the ideas developed in \cite{mrd}, while Section 2 presents possible applications in sensor fusion, multi-agent SLAM, and "human-appropriate" robot movement (e.g. legibility and predictability~\cite{dragan-hri-2013}). In particular, we show how MRD can be used to construct the underlying models in a data driven manner, rather than directly leveraging first principles theories (e.g., physics, psychology) as is commonly the case for sensor fusion, SLAM, and human robot interaction. We note that [Bekiroglu et al., 2016] leveraged MRD for correcting unstable robot grasps to stable robot grasps.
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
Venue: arXiv cs.CL 2017
Year: 2017

Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods.
Fast Approximate Construction of Best Complex Antipodal Spherical Codes
Venue: arXiv cs.IT 2017
Year: 2017

Compressive Sensing (CS) theory states that real-world signals can often be recovered from much fewer measurements than those suggested by the Shannon sampling theorem. Nevertheless, recoverability does not only depend on the signal, but also on the measurement scheme. The measurement matrix should behave as close as possible to an isometry for the signals of interest. Therefore the search for optimal CS measurement matrices of size $m\times n$ translates into the search for a set of $n$ $m$-dimensional vectors with minimal coherence. Best Complex Antipodal Spherical Codes (BCASCs) are known to be optimal in terms of coherence. An iterative algorithm for BCASC generation has been recently proposed that tightly approaches the theoretical lower bound on coherence. Unfortunately, the complexity of each iteration depends quadratically on $m$ and $n$. In this work we propose a modification of the algorithm that allows reducing the quadratic complexity to linear on both $m$ and $n$. Numerical evaluation showed that the proposed approach does not worsen the coherence of the resulting BCASCs. On the contrary, an improvement was observed for large $n$. The reduction of the computational complexity paves the way for using the BCASCs as CS measurement matrices in problems with large $n$. We evaluate the CS performance of the BCASCs for recovering sparse signals. The BCASCs are shown to outperform both complex random matrices and Fourier ensembles as CS measurement matrices, both in terms of coherence and sparse recovery performance, especially for low $m/n$, which is the case of interest in CS.
Uncoordinated access schemes for the IoT: approaches, regulations, and performance
Venue: arXiv cs.NI 2017
Year: 2017

Internet of Things (IoT) devices communicate using a variety of protocols, differing in many aspects, with the channel access method being one of the most important. Most of the transmission technologies explicitly designed for IoT and Machine-to-Machine (M2M) communication use either an ALOHA-based channel access or some type of Listen Before Talk (LBT) strategy, based on carrier sensing. In this paper, we provide a comparative overview of the uncoordinated channel access methods for IoT technologies, namely ALOHA-based and LBT schemes, in relation with the ETSI and FCC regulatory frameworks. Furthermore, we provide a performance comparison of these access schemes, both in terms of successful transmissions and energy efficiency, in a typical IoT deployment. Results show that LBT is effective in reducing inter-node interference even for long-range transmissions, though the energy efficiency can be lower than that provided by ALOHA methods. The adoption of rate-adaptation schemes, furthermore, lowers the energy consumption while improving the fairness among nodes at different distances from the receiver. Coexistence issues are also investigated, showing that in massive deployments LBT is severely affected by the presence of ALOHA devices in the same area.
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Venue: arXiv cs.AI 2017
Year: 2017

Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alternative to improve a prediction model, but collecting such knowledge is laborious to the expert if the number of candidate features is very large. We introduce a probabilistic model that can incorporate expert feedback about the impact of genomic measurements on the sensitivity of a cancer cell for a given drug. We also present two methods to intelligently collect this feedback from the expert, using experimental design and multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51), expert knowledge decreased the prediction error by 8%. Furthermore, the intelligent approaches can be used to reduce the workload of feedback collection to less than 30% on average compared to a naive approach.
Making up for the deficit in a marathon run
Venue: arXiv cs.NE 2017
Year: 2017

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athlete's malaise). Therefore, making up for the deficit between predicted and achieved results is the main ingredient of the analysis performed by trainers after the competition. In the analysis, they search for parts of a marathon course where the athlete lost time. This paper proposes an automatic making up for the deficit by using a Differential Evolution algorithm. In this case study, the results that were obtained by a wearable sports-watch by an athlete in a real marathon are analyzed. The first experiments with Differential Evolution show the possibility of using this method in the future.
3D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage
Venue: arXiv cs.IT 2017
Year: 2017

Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality. Furthermore, we model the UAV-BS deployment in the horizontal dimension as a circle placement problem and a smallest enclosing circle problem. Simulations are conducted to evaluate the performance of the proposed method for different spatial distributions of the users.
Learning Deep Networks from Noisy Labels with Dropout Regularization
Venue: arXiv cs.CV 2017
Year: 2017

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.
Analysis of Channel-Based User Authentication by Key-Less and Key-Based Approaches
Venue: arXiv cs.IT 2017
Year: 2017

User authentication (UA) supports the receiver in deciding whether a message comes from the claimed transmitter or from an impersonating attacker. In cryptographic approaches messages are signed with either an asymmetric or symmetric key, and a source of randomness is required to generate the key. In physical layer authentication (PLA) instead the receiver checks if received messages presumably coming from the same source undergo the same channel. We compare these solutions by considering the physical-layer channel features as randomness source for generating the key, thus allowing an immediate comparison with PLA (that already uses these features). For the symmetric-key approach we use secret key agreement, while for asymmetric-key the channel is used as entropy source at the transmitter. We focus on the asymptotic case of an infinite number of independent and identically distributed channel realizations, showing the correctness of all schemes and analyzing the secure authentication rate, that dictates the rate at which the probability that UA security is broken goes to zero as the number of used channel resources (to generate the key or for PLA) goes to infinity. Both passive and active attacks are considered and by numerical results we compare the various systems.
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning
Venue: arXiv cs.CY 2017
Year: 2017

P2P lending presents as an innovative and flexible alternative for conventional lending institutions like banks, where lenders and borrowers directly make transactions and benefit each other without complicated verifications. However, due to lack of specialized laws, delegated monitoring and effective managements, P2P platforms may spawn potential risks, such as withdraw failures, investigation involvements and even runaway bosses, which cause great losses to lenders and are especially serious and notorious in China. Although there are abundant public information and data available on the Internet related to P2P platforms, challenges of multi-sourcing and heterogeneity matter. In this paper, we promote a novel deep learning model, OMNIRank, which comprehends multi-dimensional features of P2P platforms for risk quantification and produces scores for ranking. We first construct a large-scale flexible crawling framework and obtain great amounts of multi-source heterogeneous data of domestic P2P platforms since 2007 from the Internet. Purifications like duplication and noise removal, null handing, format unification and fusion are applied to improve data qualities. Then we extract deep features of P2P platforms via text comprehension, topic modeling, knowledge graph and sentiment analysis, which are delivered as inputs to OMNIRank, a deep learning model for risk quantification of P2P platforms. Finally, according to rankings generated by OMNIRank, we conduct flourish data visualizations and interactions, providing lenders with comprehensive information supports, decision suggestions and safety guarantees.
Towards Understanding the Impact of Crime in a Choice of a Route by a Bus Passenger
Venue: arXiv cs.CY 2017
Year: 2017

In this paper we describe a simulation platform that supports studies on the impact of crime on urban mobility. We present an example of how this can be achieved by seeking to understand the effect, on the transport system, if users of this system decide to choose optimal routes of time between origins and destinations that normally follow. Based on real data from a large Brazilian metropolis, we found that the percentage of users who follow this policy is small. Most prefer to follow less efficient routes by making bus exchanges at terminals. This can be understood as an indication that the users of the transport system privilege the security factor.
A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models
Venue: arXiv cs.CY 2017
Year: 2017

This large-scale study, consisting of 24.5 million hand hygiene opportunities spanning 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand hygiene compliance. We examine the use of features such as temperature, relative humidity, influenza severity, day/night shift, federal holidays and the presence of new residents in predicting daily hand hygiene compliance. The results suggest that colder temperatures and federal holidays have an adverse effect on hand hygiene compliance rates, and that individual cultures and attitudes regarding hand hygiene seem to exist among facilities.