OpenRaiser/Intern-Atlas huggingface dataset:
Higher-order Spectral Clustering for Heterogeneous Graphs
Venue: arXiv cs.SI 2018
Year: 2018

Higher-order connectivity patterns such as small induced sub-graphs called graphlets (network motifs) are vital to understand the important components (modules/functional units) governing the configuration and behavior of complex networks. Existing work in higher-order clustering has focused on simple homogeneous graphs with a single node/edge type. However, heterogeneous graphs consisting of nodes and edges of different types are seemingly ubiquitous in the real-world. In this work, we introduce the notion of typed-graphlet that explicitly captures the rich (typed) connectivity patterns in heterogeneous networks. Using typed-graphlets as a basis, we develop a general principled framework for higher-order clustering in heterogeneous networks. The framework provides mathematical guarantees on the optimality of the higher-order clustering obtained. The experiments demonstrate the effectiveness of the framework quantitatively for three important applications including (i) clustering, (ii) link prediction, and (iii) graph compression. In particular, the approach achieves a mean improvement of 43x over all methods and graphs for clustering while achieving a 18.7% and 20.8% improvement for link prediction and graph compression, respectively.
Performance Evaluation of VoLTE Based on Field Measurement Data
Venue: arXiv cs.NI 2018
Year: 2018

Voice over Long-Term Evolution (VoLTE) has been witnessing a rapid deployment by network carriers worldwide. During the phases of VoLTE deployments, carriers would typically face challenges in understanding the factors affecting the VoLTE performance and then optimizing it to meet or exceed the performance of the legacy circuit switched (CS) network (i.e., 2G/3G). The main challenge of VoLTE service quality is the LTE network optimization and the performance aspects of the service in different LTE deployment scenarios. In this paper, we present a detailed practical performance analysis of VoLTE based on commercially deployed 3GPP Release-10 LTE networks. The analysis evaluates VoLTE performance in terms of real-time transport protocol (RTP) error rate, RTP jitter and delays, block error rate (BLER) in different radio conditions and VoLTE voice quality in terms of mean opinion score (MOS). In addition, the paper evaluates key VoLTE features such as RObust Header Compression (ROHC) and transmission time interval (TTI) bundling. This paper provides guidelines for best practices of VoLTE deployment as well as practical performance evaluation based on field measurement data from commercial LTE networks.
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
Venue: arXiv cs.CV 2018
Year: 2018

As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problem is modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose Context-Aware Deep Spatio-Temporal Network (CADSTN), a novel method to jointly model the spatio-temporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with state-of-the-art methods and runs in 60fps.
Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues' formula
Venue: arXiv cs.CG 2018
Year: 2018

In computational 3D geometric problems involving rotations, it is often that people have to convert back and forth between a rotational matrix and a rotation described by an axis and a corresponding angle. For this purpose, Rodrigues' rotation formula is a very popular expression to use because of its simplicity and efficiency. Nevertheless, while converting a rotation matrix to an axis of rotation and the rotation angle, there exists ambiguity. Further judgement or even manual interference may be necessary in some situations. An extension of the Rodrigues' formula helps to find the sine and cosine values of the rotation angle with respect to a given rotation axis is found and this simple extension may help to accelerate many applications.
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
Venue: arXiv cs.NI 2018
Year: 2018

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.
Graphlet Count Estimation via Convolutional Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Graphlets are defined as k-node connected induced subgraph patterns. For an undirected graph, 3-node graphlets include close triangle and open triangle. When k = 4, there are six types of graphlets, e.g., tailed-triangle and clique are two possible 4-node graphlets. The number of each graphlet, called graphlet count, is a signature which characterizes the local network structure of a given graph. Graphlet count plays a prominent role in network analysis of many fields, most notably bioinformatics and social science. However, computing exact graphlet count is inherently difficult and computational expensive because the number of graphlets grows exponentially large as the graph size and/or graphlet size k grow. To deal with this difficulty, many sampling methods were proposed to estimate graphlet count with bounded error. Nevertheless, these methods require large number of samples to be statistically reliable, which is still computationally demanding. Moreover, they have to repeat laborious counting procedure even if a new graph is similar or exactly the same as previous studied graphs. Intuitively, learning from historic graphs can make estimation more accurate and avoid many repetitive counting to reduce computational cost. Based on this idea, we propose a convolutional neural network (CNN) framework and two preprocessing techniques to estimate graphlet count. Extensive experiments on two types of random graphs and real world biochemistry graphs show that our framework can offer substantial speedup on estimating graphlet count of new graphs with high accuracy.
Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms
Venue: arXiv cs.CR 2018
Year: 2018

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation.
Image Completion on CIFAR-10
Venue: arXiv cs.CV 2018
Year: 2018

This project performed image completion on CIFAR-10, a dataset of 60,000 32x32 RGB images, using three different neural network architectures: fully convolutional networks, convolutional networks with fully connected layers, and encoder-decoder convolutional networks. The highest performing model was a deep fully convolutional network, which was able to achieve a mean squared error of .015 when comparing the original image pixel values with the predicted pixel values. As well, this network was able to output in-painted images which appeared real to the human eye.
Entity-Relationship Search over the Web
Venue: arXiv cs.IR 2018
Year: 2018

Entity-Relationship (E-R) Search is a complex case of Entity Search where the goal is to search for multiple unknown entities and relationships connecting them. We assume that a E-R query can be decomposed as a sequence of sub-queries each containing keywords related to a specific entity or relationship. We adopt a probabilistic formulation of the E-R search problem. When creating specific representations for entities (e.g. context terms) and for pairs of entities (i.e. relationships) it is possible to create a graph of probabilistic dependencies between sub-queries and entity plus relationship representations. To the best of our knowledge this represents the first probabilistic model of E-R search. We propose and develop a novel supervised Early Fusion-based model for E-R search, the Entity-Relationship Dependence Model (ERDM). It uses Markov Random Field to model term dependencies of E-R sub-queries and entity/relationship documents. We performed experiments with more than 800M entities and relationships extractions from ClueWeb-09-B with FACC1 entity linking. We obtained promising results using 3 different query collections comprising 469 E-R queries, with results showing that it is possible to perform E-R search without using fix and pre-defined entity and relationship types, enabling a wide range of queries to be addressed.
Triple Attention Mixed Link Network for Single Image Super Resolution
Venue: arXiv cs.CV 2018
Year: 2018

Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive performance. However, existing architectures have limitations due to the less sophisticated structure along with less strong representational power. In this work, to significantly enhance the feature representation, we proposed Triple Attention mixed link Network (TAN) which consists of 1) three different aspects (i.e., kernel, spatial and channel) of attention mechanisms and 2) fu-sion of both powerful residual and dense connections (i.e., mixed link). Specifically, the network with multi kernel learns multi hierarchical representations under different receptive fields. The output features are recalibrated by the effective kernel and channel attentions and feed into next layer partly residual and partly dense, which filters the information and enable the network to learn more powerful representations. The features finally pass through the spatial attention in the reconstruction network which generates a fusion of local and global information, let the network restore more details and improves the quality of reconstructed images. Thanks to the diverse feature recalibrations and the advanced information flow topology, our proposed model is strong enough to per-form against the state-of-the-art methods on the bench-mark evaluations.
Optimizing Waiting Thresholds Within A State Machine
Venue: arXiv cs.LG 2018
Year: 2018

Azure (the cloud service provided by Microsoft) is composed of physical computing units which are called nodes. These nodes are controlled by a software component called Fabric Controller (FC), which can consider the nodes to be in one of many different states such as Ready, Unhealthy, Booting, etc. Some of these states correspond to a node being unresponsive to FCs requests. When a node goes unresponsive for more than a set threshold, FC intervenes and reboots the node. We minimized the downtime caused by the intervention threshold when a node switches to the Unhealthy state by fitting various heavy-tail probability distributions. We consider using features of the node to customize the organic recovery model to the individual nodes that go unhealthy. This regression approach allows us to use information about the node like hardware, software versions, historical performance indicators, etc. to inform the organic recovery model and hence the optimal threshold. In another direction, we consider generalizing this to an arbitrary number of thresholds within the node state machine (or Markov chain). When the states become intertwined in ways that different thresholds start affecting each other, we can't simply optimize each of them in isolation. For best results, we must consider this as an optimization problem in many variables (the number of thresholds). We no longer have a nice closed form solution for this more complex problem like we did with one threshold, but we can still use numerical techniques (gradient descent) to solve it.
On the discrepancy of random low degree set systems
Venue: arXiv cs.DS 2018
Year: 2018

Motivated by the celebrated Beck-Fiala conjecture, we consider the random setting where there are $n$ elements and $m$ sets and each element lies in $t$ randomly chosen sets. In this setting, Ezra and Lovett showed an $O((t \log t)^{1/2})$ discrepancy bound in the regime when $n \leq m$ and an $O(1)$ bound when $n \gg m^t$. In this paper, we give a tight $O(\sqrt{t})$ bound for the entire range of $n$ and $m$, under a mild assumption that $t = Ω(\log \log m)^2$. The result is based on two steps. First, applying the partial coloring method to the case when $n = m \log^{O(1)} m$ and using the properties of the random set system we show that the overall discrepancy incurred is at most $O(\sqrt{t})$. Second, we reduce the general case to that of $n \leq m \log^{O(1)}m$ using LP duality and a careful counting argument.
Identification of promoted eclipse unstable interfaces using clone detection technique
Venue: arXiv cs.SE 2018
Year: 2018

The Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, client developers often use non-APIs which may cause their systems to fail when ported to new framework releases. To overcome this problem, Eclipse interface producers may promote unstable interfaces to APIs. However, client developers have no assistance to aid them to identify the promoted unstable interfaces in the Eclipse framework. We aim to help API users identify promoted unstable interfaces. We used the clone detection technique to identify promoted unstable interfaces as the framework evolves. Our empirical investigation on 16 Eclipse major releases presents the following observations. First, we have discovered that there exists over 60% non-API methods of the total interfaces in each of the analyzed 16 Eclipse releases. Second, we have discovered that the percentage of promoted non-APIs identified through clone detection ranges from 0.20% to 10.38%.
Deep learning cardiac motion analysis for human survival prediction
Venue: arXiv cs.LG 2018
Year: 2018

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
Interface Selection for Power Management in UMTS/WLAN Overlaying Network
Venue: arXiv cs.NI 2018
Year: 2018

The multiple choices of access networks offer different opportunities and overcome the limitations of other technologies. Optimal selection of interface is a big challenge for multiple interfaces supported mobile terminals to make a seamless handover and to optimize the power consumption. Seamless handover, resource management, and CAC to support QoS and multiple interface management to reduce power consumption in mobile terminal are the most important issues for the UMTS/WLAN overlaying network. The access of both interfaces simultaneously can reduce the handover latency and data loss in heterogeneous handover. The MN may maintain one interface connection while other interface can be used for handover process. But the access of both interfaces increases the consumption of power in MN. In this paper we present an efficient interface selection scheme including interface selection algorithms, interface selection mechanism and CAC considering battery power consumption for overlaying networks. MN's battery power level and provision of QoS/QoE in the target interface are considered as important parameters for our interface selection algorithm.
Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
Venue: arXiv cs.RO 2018
Year: 2018

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
From Simulation to Real-World Robotic Mobile Fulfillment Systems
Venue: arXiv cs.RO 2018
Year: 2018

In a new type of automated parts-to-picker warehouse system - a Robotic Mobile Fulfillment System (RMFS) - robots are sent to transport pods (movable shelves) to human operators at stations to pick/put items from/to pods. There are many operational decision problems in such a system, and some of them are interdependent and influence each other. In order to analyze the decision problems and the relationships between them, there are two open-source simulation frameworks in the literature, Alphabet Soup and RAWSim-O. However, the steps between simulation and real-world RMFS are not clear in the literature. Therefore, this paper aims to bridge this gap. The simulator is firstly transferred as core software. The core software is connected with an open-source ERP system, called Odoo, while it is also connected with real robots and stations through an XOR-bench. The XOR-bench enables the RMFS to be integrated with several mini-robots and mobile industrial robots in (removed) experiments for the purpose of research and education.
Improving resource elasticity in cloud computing thanks to model-free control
Venue: arXiv cs.DC 2018
Year: 2018

In cloud computing management, the dynamic adaptation of computing resource allocations under time-varying workload is an active domain of investigation. Several control strategies were already proposed. Here the model-free control setting and the corresponding "intelligent" controllers, which are most successful in many concrete engineering situations, are employed for the "horizontal elasticity." When compared to the commercial "Auto-Scaling" algorithms, our easily implementable approach, behaves better even with sharp workload fluctuations. This is confirmed by experiments on Amazon Web Services (AWS).
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
Venue: arXiv cs.CV 2018
Year: 2018

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.