OpenRaiser/Intern-Atlas huggingface dataset:
Effects of reciprocity on random walks in weighted networks
Venue: arXiv cs.SI 2017
Year: 2017

It has been recently reported that the reciprocity of real-life weighted networks is very pronounced, however its impact on dynamical processes is poorly understood. In this paper, we study random walks in a scale-free directed weighted network with a trap at the central hub node, where the weight of each directed edge is dominated by a parameter controlling the extent of network reciprocity. We derive two expressions for the mean first passage time (MFPT) to the trap, by using two different techniques, the results of which agree well with each other. We also analytically determine all the eigenvalues as well as their multiplicities for the fundamental matrix of the dynamical process, and show that the largest eigenvalue has an identical dominant scaling as that of the MFPT.We find that the weight parameter has a substantial effect on the MFPT, which behaves as a power-law function of the system size with the power exponent dependent on the parameter, signaling the crucial role of reciprocity in random walks occurring in weighted networks.
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"
Venue: arXiv cs.CV 2017
Year: 2017

Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes faces at three different scales. It simultaneously utilizes three parallel outputs of the convolutional feature maps to predict multi-scale candidate face regions. The "atrous" convolution trick (convolution with up-sampled filters) and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN to further boost its performance. The second stage is a Boosted Forests classifier, which utilizes deep facial features pooled from inside the candidate face regions as well as deep contextual features pooled from a larger region surrounding the candidate face regions. This step is included to further remove hard negative samples. Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.
Private Learning on Networks: Part II
Venue: arXiv cs.DC 2017
Year: 2017

This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents. The agents solve the optimization problem using local computation and communication between adjacent agents in the network. We present two randomized iterative algorithms for distributed optimization. To improve privacy, our algorithms add "structured" randomization to the information exchanged between the agents. We prove deterministic correctness (in every execution) of the proposed algorithms despite the information being perturbed by noise with non-zero mean. We prove that a special case of a proposed algorithm (called function sharing) preserves privacy of individual polynomial objective functions under a suitable connectivity condition on the network topology.
Coordinated Multi-Point (CoMP) Transmission in Downlink Multi-cell NOMA Systems: Models and Spectral Efficiency Performance
Venue: arXiv cs.NI 2017
Year: 2017

We outline a general framework to use coordinated multi-point (CoMP) transmission technology in downlink multi-cell non-orthogonal multiple access (NOMA) systems considering distributed power allocation at each cell. In this framework, CoMP transmission is used for users experiencing strong receive-signals from multiple cells while each cell adopts NOMA for resource allocation to its active users. After a brief review of the working principles of different CoMP schemes, we identify their applicability and necessary conditions for their use in downlink multi-cell NOMA system. After that, we discuss different network scenarios with different spatial distributions of users and study the applicability of CoMP schemes in these network scenarios. To the end, a numerical performance evaluation is carried out for the proposed CoMP-NOMA systems and the results are compared with those for conventional orthogonal multiple access (OMA)-based CoMP systems. The numerical results quantify the spectral efficiency gain of the proposed CoMP-NOMA models over CoMP-OMA. Finally, we conclude this article by identifying the potential major challenges in implementing CoMP-NOMA in future cellular systems.
AdiosStMan: Parallelizing Casacore Table Data System Using Adaptive IO System
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we investigate the Casacore Table Data System (CTDS) used in the casacore and CASA libraries, and methods to parallelize it. CTDS provides a storage manager plugin mechanism for third-party devel- opers to design and implement their own CTDS storage managers. Hav- ing this in mind, we looked into various storage backend techniques that can possibly enable parallel I/O for CTDS by implementing new storage managers. After carrying on benchmarks showing the excellent parallel I/O throughput of the Adaptive IO System (ADIOS), we implemented an ADIOS based parallel CTDS storage manager. We then applied the CASA MSTransform frequency split task to verify the ADIOS Storage Manager. We also ran a series of performance tests to examine the I/O throughput in a massively parallel scenario.
New algorithms for the Minimum Coloring Cut Problem
Venue: arXiv cs.DS 2017
Year: 2017

The Minimum Coloring Cut Problem is defined as follows: given a connected graph G with colored edges, find an edge cut E' of G (a minimal set of edges whose removal renders the graph disconnected) such that the number of colors used by the edges in E' is minimum. In this work, we present two approaches based on Variable Neighborhood Search to solve this problem. Our algorithms are able to find all the optimum solutions described in the literature.
Goal-Driven Dynamics Learning via Bayesian Optimization
Venue: arXiv cs.SY 2017
Year: 2017

Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used in conjunction with optimal control schemes to efficiently design a controller for a given task. This model is updated directly based on the performance observed in experiments on the physical system in an iterative manner until a desired performance is achieved. We demonstrate the efficacy of the proposed approach through simulations and real experiments on a quadrotor testbed.
Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm
Venue: arXiv cs.DS 2017
Year: 2017

We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction. Fluid Communities is based on the propagation methodology, which represents the state-of-the-art in terms of computational cost and scalability. While being highly efficient, Fluid Communities is able to find communities in synthetic graphs with an accuracy close to the current best alternatives. Additionally, Fluid Communities is the first propagation-based algorithm capable of identifying a variable number of communities in network. To illustrate the relevance of the algorithm, we evaluate the diversity of the communities found by Fluid Communities, and find them to be significantly different from the ones found by alternative methods.
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
Venue: arXiv cs.LG 2017
Year: 2017

This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Venue: arXiv cs.LG 2017
Year: 2017

Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application. Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.
Factoring Exogenous State for Model-Free Monte Carlo
Venue: arXiv cs.LG 2017
Year: 2017

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This "off-policy Monte Carlo simulation" method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high-dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP.
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a crowd counting method. Crowd counting is difficult because of large appearance changes of a target which caused by density and scale changes. Conventional crowd counting methods generally utilize one predictor (e,g., regression and multi-class classifier). However, such only one predictor can not count targets with large appearance changes well. In this paper, we propose to predict the number of targets using multiple CNNs specialized to a specific appearance, and those CNNs are adaptively selected according to the appearance of a test image. By integrating the selected CNNs, the proposed method has the robustness to large appearance changes. In experiments, we confirm that the proposed method can count crowd with lower counting error than a CNN and integration of CNNs with fixed weights. Moreover, we confirm that each predictor automatically specialized to a specific appearance.
A practical approach to dialogue response generation in closed domains
Venue: arXiv cs.CL 2017
Year: 2017

We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover $>$70\% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.
Palgol: A High-Level DSL for Vertex-Centric Graph Processing with Remote Data Access
Venue: arXiv cs.DC 2017
Year: 2017

Pregel is a popular distributed computing model for dealing with large-scale graphs. However, it can be tricky to implement graph algorithms correctly and efficiently in Pregel's vertex-centric model, especially when the algorithm has multiple computation stages, complicated data dependencies, or even communication over dynamic internal data structures. Some domain-specific languages (DSLs) have been proposed to provide more intuitive ways to implement graph algorithms, but due to the lack of support for remote access --- reading or writing attributes of other vertices through references --- they cannot handle the above mentioned dynamic communication, causing a class of Pregel algorithms with fast convergence impossible to implement. To address this problem, we design and implement Palgol, a more declarative and powerful DSL which supports remote access. In particular, programmers can use a more declarative syntax called chain access to naturally specify dynamic communication as if directly reading data on arbitrary remote vertices. By analyzing the logic patterns of chain access, we provide a novel algorithm for compiling Palgol programs to efficient Pregel code. We demonstrate the power of Palgol by using it to implement several practical Pregel algorithms, and the evaluation result shows that the efficiency of Palgol is comparable with that of hand-written code.
Optimal Design of Energy-Efficient Millimeter Wave Hybrid Transceivers for Wireless Backhaul
Venue: arXiv cs.IT 2017
Year: 2017

This work analyzes a mmWave single-cell network, which comprises a macro base station (BS) and an overlaid tier of small-cell BSs using a wireless backhaul for data traffic. We look for the optimal number of antennas at both BS and small-cell BSs that maximize the energy efficiency (EE) of the system when a hybrid transceiver architecture is employed. Closed-form expressions for the EE-optimal values of the number of antennas are derived that provide valuable insights into the interplay between the optimization variables and hardware characteristics. Numerical and analytical results show that the maximal EE is achieved by a 'close-to' fully-digital system wherein the number of BS antennas is approximately equal to the number of served small cells.
Transmission Game in MIMO Interference Channels With Radio-Frequency Energy Harvesting
Venue: arXiv cs.IT 2017
Year: 2017

For multi-user transmissions over MIMO interference channels, each user designs the transmit covariance matrix to maximize its information rate. When passive radio-frequency (RF) energy harvesters are present in the network, the transmissions are constrained by both the transmit power limits and the energy harvesting requirements. A passive RF energy harvester collects the radiated energy from nearby wireless information transmitters instead of using a dedicated wireless power source. It needs multiple transmitters to concentrate their RF radiation on it because typical electric field strengths are weak. In this paper, strategic games are proposed for the multi-user transmissions. First, in a non-cooperative game, each transmitter has a best-response strategy for the transmit covariance matrix that follows a multi-level water-filling solution. A pure-strategy Nash equilibrium exists. Secondly, in a cooperative game, there is no need to estimate the proportion of the harvested energy from each transmitter. Rather, the transmitters bargain over the unit-reward of the energy contribution. An approximation of the information rate is used in constructing the individual utility such that the problem of network utility maximization can be decomposed and the bargaining process can be implemented distributively. The bargaining solution gives a point of rates that is superior to the Nash equilibria and close to the Pareto front. Simulation results verify the algorithms that provide good communication performance while satisfying the RF energy-harvesting requirements.
Automatic Temperature Setpoint Tuning of a Thermoforming Machine using Fuzzy Terminal Iterative Learning Control
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a new way to design a Fuzzy Terminal Iterative Learning Control (TILC) to control the heater temperature setpoints of a thermoforming machine. This fuzzy TILC is based on the inverse of a fuzzy model of this machine, and is built from experimental (or simulation) data with kriging interpolation. The Fuzzy Inference System usually used for a fuzzy model is the zero order Takagi Sugeno Kwan system (constant consequents). In this paper, the 1st order Takagi Sugeno Kwan system is used, with the fuzzy model rules expressed using matrices. This makes the inversion of the fuzzy model much easier than the inversion of the fuzzy model based on the TSK of order 0. Based on simulation results, the proposed fuzzy TILC seems able to give a very good initial guess as to the heater temperature setpoints, making it possible to have almost no wastage of plastic sheets. Simulation results show the effectiveness of the fuzzy TILC compared to a crisp TILC, even though the fuzzy controller is based on a fuzzy model built from noisy data.
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
Venue: arXiv cs.SI 2017
Year: 2017

The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.
Solar Power Forecasting Using Support Vector Regression
Venue: arXiv cs.LG 2017
Year: 2017

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they can become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be used to mitigate some of the challenges that arise from the uncertainty in the resource. While wind energy forecasting research is considered mature, solar energy forecasting is witnessing a steadily growing attention from the research community. This paper presents a support vector regression model to produce solar power forecasts on a rolling basis for 24 hours ahead over an entire year, to mimic the practical business of energy forecasting. Twelve weather variables are considered from a high-quality benchmark dataset and new variables are extracted. The added value of the heat index and wind speed as additional variables to the model is studied across different seasons. The support vector regression model performance is compared with artificial neural networks and multiple linear regression models for energy forecasting.
Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we study the robust consensus problem for a set of discrete-time linear agents to coordinate over an uncertain communication network, which is to achieve consensus against the transmission errors and noises resulted from the information exchange between the agents. We model the network by means of communication links subject to multiplicative stochastic uncertainties, which are susceptible to describing packet dropout, random delay, and fading phenomena. Different communication topologies, such as undirected graphs and leader-follower graphs, are considered. We derive sufficient conditions for robust consensus in the mean square sense. This results unveil intrinsic constraints on consensus attainment imposed by the network synchronizability, the unstable agent dynamics, and the channel uncertainty variances. Consensus protocols are designed based on the state information transmitted over the uncertain channels, by solving a modified algebraic Riccati equation.
Towards thinner convolutional neural networks through Gradually Global Pruning
Venue: arXiv cs.CV 2017
Year: 2017

Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices. Among many pruning granularities, neuron level pruning will remove redundant neurons and filters in the model and result in thinner networks. In this paper, we propose a gradually global pruning scheme for neuron level pruning. In each pruning step, a small percent of neurons were selected and dropped across all layers in the model. We also propose a simple method to eliminate the biases in evaluating the importance of neurons to make the scheme feasible. Compared with layer-wise pruning scheme, our scheme avoid the difficulty in determining the redundancy in each layer and is more effective for deep networks. Our scheme would automatically find a thinner sub-network in original network under a given performance.