OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery
Venue: arXiv cs.CV 2016
Year: 2016

When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.
Probabilistic Articulated Real-Time Tracking for Robot Manipulation
Venue: arXiv cs.RO 2016
Year: 2016

We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in the robot model, such as poor extrinsic camera calibration. We make our method computationally efficient through a principled combination of Kalman filtering of the joint measurements and asynchronous depth-image updates based on the Coordinate Particle Filter. We quantitatively evaluate our approach on a dataset recorded from a real robotic platform, annotated with ground truth from a motion capture system. We show that our approach is robust and accurate even under challenging conditions such as fast motion, significant and long-term occlusions, and time-varying biases. We release the dataset along with open-source code of our approach to allow for quantitative comparison with alternative approaches.
An interleaver design for polar codes over slow fading channels
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of using polar codes over slow fading wireless channels. For design, we focus on a parallel slow fading channel with 2 blocks, and polar codes with rate <= 1/2. Motivated by Arikan's systematic polar code construction, we propose an interleaver design for a general polar code. The interleaver comprises of using the bit reversal of the order of polarized bit channels. This interleaver is called a diversity interleaver. In addition to the diversity interleaver, a diversity polar code is proposed to further increase the diversity gain. The proposed designs are evaluated via link simulations for AWGN and fading channels. The simulation results show a performance close to the outage probability (within 2 dB) and significant gains over using a random interleaver.
Partial Procedural Geometric Model Fitting for Point Clouds
Venue: arXiv cs.GR 2016
Year: 2016

Geometric model fitting is a fundamental task in computer graphics and computer vision. However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e.g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models. This paper hence proposes a novel rigid geometric similarity metric, which is able to measure both the full similarity and the partial similarity between arbitrary geometric models. The proposed metric enables us to perform partial procedural geometric model fitting (PPGMF). The task of PPGMF is to search a procedural geometric model space for the model rigidly similar to a query of non-complete point set. Models in the procedural model space are generated according to a set of parametric modeling rules. A typical query is a point cloud. PPGMF is very useful as it can be used to fit arbitrary geometric models to non-complete (incomplete, over-complete or hybrid-complete) point cloud data. For example, most laser scanning data is non-complete due to occlusion. Our PPGMF method uses Markov chain Monte Carlo technique to optimize the proposed similarity metric over the model space. To accelerate the optimization process, the method also employs a novel coarse-to-fine model dividing strategy to reject dissimilar models in advance. Our method has been demonstrated on a variety of geometric models and non-complete data. Experimental results show that the PPGMF method based on the proposed metric is able to fit non-complete data, while the method based on other metrics is unable. It is also shown that our method can be accelerated by several times via early rejection.
New Algorithms for Maximizing Cellular Wireless Network Energy Efficiency
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we aim to maximize the energy efficiency of cellular wireless networks. Specifically, we address the power allocation problem in multi-cell multi-carrier systems. Considering realistic base station power consumption models, we formulate a network-wide energy efficiency maximization problem. Using tools from fractional programming, we cast this problem in the framework of bi-criterion optimization where rate maximization and power minimization are weighted accordingly. Interference pricing mechanism is applied to reduce the intercell interference and to achieve a higher network performance. We decompose the main problem into subproblems via dual decomposition. These subproblems are independently solved per sector using limited information exchange between base stations. We first derive our expressions and present algorithms for the single-tier networks. Then, we extend our analysis to two-tier networks where picocell base stations are deployed to improve the network performance and reduce the link distances. Lastly, we extend our framework and include the quality-of-service constraints.We obtain closed-form expressions for the power level updates which are determined by the multi-level water-filling algorithm, or, as it is sometimes called as, the modified waterfilling algorithm. Based on our simulation results, we demonstrate that the proposed algorithms can outperform the benchmark approaches in terms of energy efficiency by a factor of 2.7.
Estimation of Received Signal Strength Distribution for Smart Meters with Biased Measurement Data Set
Venue: arXiv cs.IT 2016
Year: 2016

This letter presents an experimental study and a novel modelling approach of the wireless channel of smart utility meters placed in basements or sculleries. The experimental data consist of signal strength measurements of consumption report packets. Since such packets are only registered if they can be decoded by the receiver, the part of the signal strength distribution that falls below the receiver sensitivity threshold is not observable. We combine a Rician fading model with a bias function that captures the cut-off in the observed signal strength measurements. Two sets of experimental data are analysed. It is shown that the proposed method offers an approximation of the distribution of the signal strength measurements that is better than a naïve Rician fitting.
Harmonic Sum-based Method for Heart Rate Estimation using PPG Signals Affected with Motion Artifacts
Venue: arXiv cs.CY 2016
Year: 2016

Wearable photoplethysmography (WPPG) has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the harmonic sum (HSUM) model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint harmonic sum (HSUM) model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing (SP) cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset.
Fault Tolerant Frequent Pattern Mining
Venue: arXiv cs.DC 2016
Year: 2016

FP-Growth algorithm is a Frequent Pattern Min- ing (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing, though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.
Parallel Stream Processing Against Workload Skewness and Variance
Venue: arXiv cs.DC 2016
Year: 2016

Key-based workload partitioning is a common strategy used in parallel stream processing engines, enabling effective key-value tuple distribution over worker threads in a logical operator. While randomized hashing on the keys is capable of balancing the workload for key-based partitioning when the keys generally follow a static distribution, it is likely to generate poor balancing performance when workload variance occurs on the incoming data stream. This paper presents a new key-based workload partitioning framework, with practical algorithms to support dynamic workload assignment for stateful operators. The framework combines hash-based and explicit key-based routing strategies for workload distribution, which specifies the destination worker threads for a handful of keys and assigns the other keys with the hashing function. When short-term distribution fluctuations occur to the incoming data stream, the system adaptively updates the routing table containing the chosen keys, in order to rebalance the workload with minimal migration overhead within the stateful operator. We formulate the rebalance operation as an optimization problem, with multiple objectives on minimizing state migration costs, controlling the size of the routing table and breaking workload imbalance among worker threads. Despite of the NP-hardness nature behind the optimization formulation, we carefully investigate and justify the heuristics behind key (re)routing and state migration, to facilitate fast response to workload variance with ignorable cost to the normal processing in the distributed system. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposals and prove the huge advantage of our approaches over state-of-the-art solutions in the literature.
Learning and Transfer of Modulated Locomotor Controllers
Venue: arXiv cs.RO 2016
Year: 2016

We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor primitives by training on simple tasks. This pre-trained module is fixed and connected to a low-frequency, high-level "cortical" network, with access to all sensors, which drives behavior by modulating the inputs to the spinal network. Where a monolithic end-to-end architecture fails completely, learning with a pre-trained spinal module succeeds at multiple high-level tasks, and enables the effective exploration required to learn from sparse rewards. We test our proposed architecture on three simulated bodies: a 16-dimensional swimming snake, a 20-dimensional quadruped, and a 54-dimensional humanoid. Our results are illustrated in the accompanying video at https://youtu.be/sboPYvhpraQ
MIMO: State of the Art and the Future in Focus
Venue: arXiv cs.NI 2016
Year: 2016

Antennas of transmitters and receivers have been manipulated to increase the capacity of transmission and reception of signals. Using many elements in antennas to shape beams and direct nulls in a particular point for optimum signal transmission and reception has over decades, had tremendous positive influence in received power and signal to noise ratio (SNR). However, since the antenna elements manipulation can be done both at base station and device terminal, it gives rise to an important method of using several antennas to put and obtain signals to and from space with increased capacity. This principle is termed Multiple-input and Multiple-output (MIMO). This paper discusses application of MIMO in the state of the art and next generation of wireless systems (5G). It also discusses four models of MIMO, SISO, SIMO, MISO and MIMO, considering three method of combing the signals from multipath propagations, Selection combining (SC), Equal gain combing (EGC) and maximum ratio combining (MRC). Spatial diversity and spatial multiplexing are also discussed as form of MIMO. Finally, Massive or Hyper MIMO which is a new method of increasing transmission capacity by very large scale for fifth generation of wireless system is discussed with its challenges and opportunities. Key terms-Diversity combining techniques, spatial multiplexing, channel state information (CSI). Massive MIMO
Cubical sets and the topological topos
Venue: arXiv cs.LO 2016
Year: 2016

Coquand's cubical set model for homotopy type theory provides the basis for a computational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. This paper contributes to the understanding of this model. We make three contributions: 1. Johnstone's topological topos was created to present the geometric realization of simplicial sets as a geometric morphism between toposes. Johnstone shows that simplicial sets classify strict linear orders with disjoint endpoints and that (classically) the unit interval is such an order. Here we show that it can also be a target for cubical realization by showing that Coquand's cubical sets classify the geometric theory of flat distributive lattices. As a side result, we obtain a simplicial realization of a cubical set. 2. Using the internal `interval' in the topos of cubical sets, we construct a Moore path model of identity types. 3. We construct a premodel structure internally in the cubical type theory and hence on the fibrant objects in cubical sets.
Link Prediction in evolving networks based on the popularity of nodes
Venue: arXiv cs.SI 2016
Year: 2016

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict the missing edges or identify the spurious edges, and attracts much attention from various fields. The key issue of link prediction is to estimate the likelihood of two nodes in networks. Most current approaches of link prediction base on static structural analysis and ignore the temporal aspects of evolving networks. Unlike previous work, in this paper, we propose a popularity based structural perturbation method (PBSPM) that characterizes the similarity of an edge not only from existing connections of networks, but also from the popularity of its two endpoints, since popular nodes have much more probability to form links between themselves. By taking popularity of nodes into account, PBSPM could suppress nodes that have high importance, but gradually become inactive. Therefore the proposed method is inclined to predict potential edges between active nodes, rather than edges between inactive nodes. Experimental results on four real networks show that the proposed method outperforms the state-of-the-art methods both in accuracy and robustness in evolving networks.
End-to-end attention-based distant speech recognition with Highway LSTM
Venue: arXiv cs.CL 2016
Year: 2016

End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems. In this paper, we extend existing end-to-end attention-based models that can be applied for Distant Speech Recognition (DSR) task. Specifically, we propose an end-to-end attention-based speech recognizer with multichannel input that performs sequence prediction directly at the character level. To gain a better performance, we also incorporate Highway long short-term memory (HLSTM) which outperforms previous models on AMI distant speech recognition task.
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges
Venue: arXiv cs.NI 2016
Year: 2016

Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.
Wireless Fractal Cellular Networks
Venue: arXiv cs.NI 2016
Year: 2016

With the seamless coverage of wireless cellular networks in modern society, it is interesting to consider the shape of wireless cellular coverage. Is the shape a regular hexagon, an irregular polygon, or another complex geometrical shape? Based on fractal theory, the statistical characteristic of the wireless cellular coverage boundary is determined by the measured wireless cellular data collected from Shanghai, China. The measured results indicate that the wireless cellular coverage boundary presents an extremely irregular geometrical shape, which is also called a statistical fractal shape. Moreover, the statistical fractal characteristics of the wireless cellular coverage boundary have been validated by values of the Hurst parameter estimated in angular scales. The statistical fractal characteristics of the wireless cellular coverage boundary can be used to evaluate and design the handoff scheme of mobile user terminals in wireless cellular networks.
Makespan Optimal Solving of Cooperative Path-Finding via Reductions to Propositional Satisfiability
Venue: arXiv cs.AI 2016
Year: 2016

The problem of makespan optimal solving of cooperative path finding (CPF) is addressed in this paper. The task in CPF is to relocate a group of agents in a non-colliding way so that each agent eventually reaches its goal location from the given initial location. The abstraction adopted in this work assumes that agents are discrete items moving in an undirected graph by traversing edges. Makespan optimal solving of CPF means to generate solutions that are as short as possi-ble in terms of the total number of time steps required for the execution of the solution. We show that reducing CPF to propositional satisfiability (SAT) represents a viable option for obtaining makespan optimal solutions. Several encodings of CPF into propositional formulae are suggested and experimentally evaluated. The evaluation indicates that SAT based CPF solving outperforms other makespan optimal methods significantly in highly constrained situations (environments that are densely occupied by agents).
Real-time analysis of cataract surgery videos using statistical models
Venue: arXiv cs.CV 2016
Year: 2016

The automatic analysis of the surgical process, from videos recorded during surgeries, could be very useful to surgeons, both for training and for acquiring new techniques. The training process could be optimized by automatically providing some targeted recommendations or warnings, similar to the expert surgeon's guidance. In this paper, we propose to reuse videos recorded and stored during cataract surgeries to perform the analysis. The proposed system allows to automatically recognize, in real time, what the surgeon is doing: what surgical phase or, more precisely, what surgical step he or she is performing. This recognition relies on the inference of a multilevel statistical model which uses 1) the conditional relations between levels of description (steps and phases) and 2) the temporal relations among steps and among phases. The model accepts two types of inputs: 1) the presence of surgical tools, manually provided by the surgeons, or 2) motion in videos, automatically analyzed through the Content Based Video retrieval (CBVR) paradigm. Different data-driven statistical models are evaluated in this paper. For this project, a dataset of 30 cataract surgery videos was collected at Brest University hospital. The system was evaluated in terms of area under the ROC curve. Promising results were obtained using either the presence of surgical tools ($A_z$ = 0.983) or motion analysis ($A_z$ = 0.759). The generality of the method allows to adapt it to any kinds of surgeries. The proposed solution could be used in a computer assisted surgery tool to support surgeons during the surgery.