OpenRaiser/Intern-Atlas huggingface dataset:
Providing Wireless Coverage to High-rise Buildings Using UAVs
Venue: arXiv cs.IT 2017
Year: 2017

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. First, we present a realistic Outdoor-Indoor path loss model and describe the tradeoff introduced by this model. Then, we study the problem of efficient UAV placement, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is non-convex and is generally difficult to solve. To that end, we consider two cases of practical interest and provide the efficient solutions to the formulated problem under these cases. In the first case, we aim to find the minimum transmit power such that an indoor user with the maximum path loss can be covered. In the second case, we assume that the locations of indoor users are symmetric across the dimensions of each floor.
Power System Supplementary Damping Controllers in the Presence of Saturation
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents the analysis and a method to design supplementary damping controllers (SDCs) for synchronous generators considering the effects of saturation limits. Usually such saturations of control signals are imposed in order to enforce practical limitations such as component ratings. However, to guarantee the stability in the presence of saturation limits, the state trajectories must remain inside the domain of attraction (DA). In this paper, the domain of attraction of a single-machine infinite-bus (SMIB) power system with saturation nonlinearity is estimated and compared with the exact description of the null controllable region. Then, state-feedback controllers are designed to enlarge the DA. Our analysis shows that nonlinear effects of saturation should be considered to guarantee stability and satisfactory performance. Simulation results on a detailed nonlinear model of a synchronous generator indicate that the DA enlarges with the proposed controller. The results also indicate that Critical Clearing Time (CCT) and damping of the system with saturation can be improved by the proposed method.
RFID-based Solutions for Smarter Healthcare
Venue: arXiv cs.CY 2017
Year: 2017

This paper proposes the application of RFID technology in healthcare industry based on its increased functionality, high reliability, easy-to-use capabilities and low cost. After a brief presentation of RFID technologies and their applications, the paper describes an RFID-based system that can provide efficient facilities to allow essential information management for emergency care across hospital boundaries. This system performs RFID-based identification of the patients, querying and retrieving medical data from various existing healthcare information systems, as well as storing and giving the most clinically significant information to the clinicians. Also, the system allows identifying and tracking RFID- tagged objects in order to provide new quality services for the mobility of objects.
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
Venue: arXiv cs.LG 2017
Year: 2017

Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could significantly improve the efficiency and lower the energy consumption at runtime, which enables the application of state-of-the-art deep learning models on low power devices. BMXNet is an open-source BNN library based on MXNet, which supports both XNOR-Networks and Quantized Neural Networks. The developed BNN layers can be seamlessly applied with other standard library components and work in both GPU and CPU mode. BMXNet is maintained and developed by the multimedia research group at Hasso Plattner Institute and released under Apache license. Extensive experiments validate the efficiency and effectiveness of our implementation. The BMXNet library, several sample projects, and a collection of pre-trained binary deep models are available for download at https://github.com/hpi-xnor
Minimally Actuated Serial Robot
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we propose a novel type of serial robot with minimal actuation. The robot is a serial rigid structure consisting of multiple links connected by passive joints and of movable actuators. The novelty of this robot is that the actuators travel over the links to a given joint and adjust the relative angle between the two adjacent links. The joints passively preserve their angles until one of the actuators moves them again. This actuation can be applied to any serial robot with two or more links. This unique configuration enables the robot to undergo the same wide range of motions typically associated with hyper-redundant robots but with much fewer actuators. The robot is modular and its size and geometry can be easily changed. We describe the robot's mechanical design and kinematics in detail and demonstrate its capabilities for obstacle avoidance with some simulated examples. In addition, we show how an experimental robot fitted with a single mobile actuator can maneuver through a confined space to reach its target.
A Unified Optimization Approach for Sparse Tensor Operations on GPUs
Venue: arXiv cs.MS 2017
Year: 2017

Sparse tensors appear in many large-scale applications with multidimensional and sparse data. While multidimensional sparse data often need to be processed on manycore processors, attempts to develop highly-optimized GPU-based implementations of sparse tensor operations are rare. The irregular computation patterns and sparsity structures as well as the large memory footprints of sparse tensor operations make such implementations challenging. We leverage the fact that sparse tensor operations share similar computation patterns to propose a unified tensor representation called F-COO. Combined with GPU-specific optimizations, F-COO provides highly-optimized implementations of sparse tensor computations on GPUs. The performance of the proposed unified approach is demonstrated for tensor-based kernels such as the Sparse Matricized Tensor- Times-Khatri-Rao Product (SpMTTKRP) and the Sparse Tensor- Times-Matrix Multiply (SpTTM) and is used in tensor decomposition algorithms. Compared to state-of-the-art work we improve the performance of SpTTM and SpMTTKRP up to 3.7 and 30.6 times respectively on NVIDIA Titan-X GPUs. We implement a CANDECOMP/PARAFAC (CP) decomposition and achieve up to 14.9 times speedup using the unified method over state-of-the-art libraries on NVIDIA Titan-X GPUs.
Dilated Residual Networks
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model's depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (`degridding'), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation.
Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream
Venue: arXiv cs.CL 2017
Year: 2017

Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item; it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams upon each of which the data mining algorithm can be run separately yielding more relevant results with a high efficiency.
Design of a Hybrid Modular Switch
Venue: arXiv cs.NI 2017
Year: 2017

Network Function Virtualization (NFV) shed new light for the design, deployment, and management of cloud networks. Many network functions such as firewalls, load balancers, and intrusion detection systems can be virtualized by servers. However, network operators often have to sacrifice programmability in order to achieve high throughput, especially at networks' edge where complex network functions are required. Here, we design, implement, and evaluate Hybrid Modular Switch (HyMoS). The hybrid hardware/software switch is designed to meet requirements for modern-day NFV applications in providing high-throughput, with a high degree of programmability. HyMoS utilizes P4-compatible Network Interface Cards (NICs), PCI Express interface and CPU to act as line cards, switch fabric, and fabric controller respectively. In our implementation of HyMos, PCI Express interface is turned into a non-blocking switch fabric with a throughput of hundreds of Gigabits per second. Compared to existing NFV infrastructure, HyMoS offers modularity in hardware and software as well as a higher degree of programmability by supporting a superset of P4 language.
Depth Distribution in High Dimensions
Venue: arXiv cs.CG 2017
Year: 2017

Motivated by the analysis of range queries in databases, we introduce the computation of the Depth Distribution of a set $\mathcal{B}$ of axis aligned boxes, whose computation generalizes that of the Klee's Measure and of the Maximum Depth. In the worst case over instances of fixed input size $n$, we describe an algorithm of complexity within $O({n^\frac{d+1}{2}\log n})$, using space within $O({n\log n})$, mixing two techniques previously used to compute the Klee's Measure. We refine this result and previous results on the Klee's Measure and the Maximum Depth for various measures of difficulty of the input, such as the profile of the input and the degeneracy of the intersection graph formed by the boxes.
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

Products reviews are one of the major resources to determine the public sentiment. The existing literature on reviews sentiment analysis mainly utilizes supervised paradigm, which needs labeled data to be trained on and suffers from domain-dependency. This article addresses these issues by describes a completely automatic approach for sentiment analysis based on unsupervised ensemble learning. The method consists of two phases. The first phase is contextual analysis, which has five processes, namely (1) data preparation; (2) spelling correction; (3) intensifier handling; (4) negation handling and (5) contrast handling. The second phase comprises the unsupervised learning approach, which is an ensemble of clustering classifiers using a majority voting mechanism with different weight schemes. The base classifier of the ensemble method is a modified k-means algorithm. The base classifier is modified by extracting initial centroids from the feature set via using SentWordNet (SWN). We also introduce new sentiment analysis problems of Australian airlines and home builders which offer potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalization ability.
On Residual CNN in text-dependent speaker verification task
Venue: arXiv cs.SD 2017
Year: 2017

Deep learning approaches are still not very common in the speaker verification field. We investigate the possibility of using deep residual convolutional neural network with spectrograms as an input features in the text-dependent speaker verification task. Despite the fact that we were not able to surpass the baseline system in quality, we achieved a quite good results for such a new approach getting an 5.23% ERR on the RSR2015 evaluation part. Fusion of the baseline and proposed systems outperformed the best individual system by 18% relatively.
Control and Energy Management System in Microgrids
Venue: arXiv cs.SY 2017
Year: 2017

As a cutting-edge technology, microgrids feature intelligent EMSs and sophisticated control, which will dramatically change our energy infrastructure. The modern microgrids are a relatively recent development with high potential to bring distributed generation, DES devices, controllable loads, communication infrastructure, and many new technologies into the mainstream. As a more controllable and intelligent entity, a microgrid has more growth potential than ever before. However, there are still many open questions, such as the future business models and economics. What is the cost-benefit to the end-user? How should we systematically evaluate the potential benefits and costs of control and energy management in a microgrid?
Directed random walks on polytopes with few facets
Venue: arXiv cs.DM 2017
Year: 2017

Let $P$ be a simple polytope with $n-d = 2$, where $d$ is the dimension and $n$ is the number of facets. The graph of such a polytope is also called a grid. It is known that the directed random walk along the edges of $P$ terminates after $O(\log^2 n)$ steps, if the edges are oriented in a (pseudo-)linear fashion. We prove that the same bound holds for the more general unique sink orientations.
Inverse Lyndon words and Inverse Lyndon factorizations of words
Venue: arXiv cs.FL 2017
Year: 2017

Motivated by applications to string processing, we introduce variants of the Lyndon factorization called inverse Lyndon factorizations. Their factors, named inverse Lyndon words, are in a class that strictly contains anti-Lyndon words, that is Lyndon words with respect to the inverse lexicographic order. The Lyndon factorization of a nonempty word w is unique but w may have several inverse Lyndon factorizations. We prove that any nonempty word w admits a canonical inverse Lyndon factorization, named ICFL(w), that maintains the main properties of the Lyndon factorization of w: it can be computed in linear time, it is uniquely determined, it preserves a compatibility property for sorting suffixes. In particular, the compatibility property of ICFL(w) is a consequence of another result: any factor in ICFL(w) is a concatenation of consecutive factors of the Lyndon factorization of w with respect to the inverse lexicographic order.
Robustness to unknown error in sparse regularization
Venue: arXiv cs.IT 2017
Year: 2017

Quadratically-constrained basis pursuit has become a popular device in sparse regularization; in particular, in the context of compressed sensing. However, the majority of theoretical error estimates for this regularizer assume an a priori bound on the noise level, which is usually lacking in practice. In this paper, we develop stability and robustness estimates which remove this assumption. First, we introduce an abstract framework and show that robust instance optimality of any decoder in the noise-aware setting implies stability and robustness in the noise-blind setting. This is based on certain sup-inf constants referred to as quotients, strictly related to the quotient property of compressed sensing. We then apply this theory to prove the robustness of quadratically-constrained basis pursuit under unknown error in the cases of random Gaussian matrices and of random matrices with heavy-tailed rows, such as random sampling matrices from bounded orthonormal systems. We illustrate our results in several cases of practical importance, including subsampled Fourier measurements and recovery of sparse polynomial expansions.
Discriminatively Learned Hierarchical Rank Pooling Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we present novel temporal encoding methods for action and activity classification by extending the unsupervised rank pooling temporal encoding method in two ways. First, we present "discriminative rank pooling" in which the shared weights of our video representation and the parameters of the action classifiers are estimated jointly for a given training dataset of labelled vector sequences using a bilevel optimization formulation of the learning problem. When the frame level features vectors are obtained from a convolutional neural network (CNN), we rank pool the network activations and jointly estimate all parameters of the model, including CNN filters and fully-connected weights, in an end-to-end manner which we coined as "end-to-end trainable rank pooled CNN". Importantly, this model can make use of any existing convolutional neural network architecture (e.g., AlexNet or VGG) without modification or introduction of additional parameters. Then, we extend rank pooling to a high capacity video representation, called "hierarchical rank pooling". Hierarchical rank pooling consists of a network of rank pooling functions, which encode temporal semantics over arbitrary long video clips based on rich frame level features. By stacking non-linear feature functions and temporal sub-sequence encoders one on top of the other, we build a high capacity encoding network of the dynamic behaviour of the video. The resulting video representation is a fixed-length feature vector describing the entire video clip that can be used as input to standard machine learning classifiers. We demonstrate our approach on the task of action and activity recognition. Obtained results are comparable to state-of-the-art methods on three important activity recognition benchmarks with classification performance of 76.7% mAP on Hollywood2, 69.4% on HMDB51, and 93.6% on UCF101.
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Venue: arXiv cs.RO 2017
Year: 2017

Multisensory polices are known to enhance both state estimation and target tracking. However, in the space of end-to-end sensorimotor control, this multi-sensor outlook has received limited attention. Moreover, systematic ways to make policies robust to partial sensor failure are not well explored. In this work, we propose a specific customization of Dropout, called \textit{Sensor Dropout}, to improve multisensory policy robustness and handle partial failure in the sensor-set. We also introduce an additional auxiliary loss on the policy network in order to reduce variance in the band of potential multi- and uni-sensory policies to reduce jerks during policy switching triggered by an abrupt sensor failure or deactivation/activation. Finally, through the visualization of gradients, we show that the learned policies are conditioned on the same latent states representation despite having diverse observations spaces - a hallmark of true sensor-fusion. Simulation results of the multisensory policy, as visualized in TORCS racing game, can be seen here: https://youtu.be/QAK2lcXjNZc.
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Venue: arXiv cs.SI 2017
Year: 2017

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.
Unsupervised Person Re-identification: Clustering and Fine-tuning
Venue: arXiv cs.CV 2017
Year: 2017

The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this paper, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains. Our method is easy to implement and can be viewed as an effective baseline for unsupervised re-ID feature learning. Specifically, PUL iterates between 1) pedestrian clustering and 2) fine-tuning of the convolutional neural network (CNN) to improve the original model trained on the irrelevant labeled dataset. Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning when the model is weak, CNN is fine-tuned on a small amount of reliable examples which locate near to cluster centroids in the feature space. As the model becomes stronger in subsequent iterations, more images are being adaptively selected as CNN training samples. Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence. This process is naturally formulated as self-paced learning. We then point out promising directions that may lead to further improvement. Extensive experiments on three large-scale re-ID datasets demonstrate that PUL outputs discriminative features that improve the re-ID accuracy.
Deep-LMS for gigabit transmission over unshielded twisted pair cables
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we propose a rapidly converging LMS algorithm for crosstalk cancellation. The architecture is similar to deep neural networks, where multiple layers are adapted sequentially. The application motivating this approach is gigabit rate transmission over unshielded twisted pairs using a vectored system. The crosstalk cancellation algorithm uses an adaptive non-diagonal preprocessing matrix prior to a conventional LMS crosstalk canceler. The update of the preprocessing matrix is inspired by deep neural networks. However, since most the operations in the Deep-LMS algorithm are linear, we are capable of providing an exact convergence speed analysis. The role of the preprocessing matrix is to speed up the convergence of the conventional LMS crosstalk canceler and hence the convergence of the overall system. The Deep-LMS is important for crosstalk cancellation in the novel G.fast standard, where traditional LMS converges very slowly due to the ill-conditioned covariance matrix of the received signal at the extended bandwidth. Simulation results support our analysis and show significant reduction in convergence time compared to existing LMS variants.
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
Venue: arXiv cs.LG 2017
Year: 2017

This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class. The determined probability distribution is then used in a maximum a posteriori (MAP) approach for the reconstruction. The parameters of the prior distribution are learned from training data. The motivation behind this approach is to model the higher-order statistical dependencies between the coefficients of the sparse representation, with the final goal of improving the reconstruction. The performance of the proposed method is validated on the Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.