OpenRaiser/Intern-Atlas huggingface dataset:
Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning
Venue: arXiv cs.NI 2018
Year: 2018

This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For a fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.
A hybrid architecture for astronomical computing
Venue: arXiv cs.DC 2018
Year: 2018

With many large science equipment constructing and putting into use, astronomy has stepped into the big data era. The new method and infrastructure of big data processing has become a new requirement of many astronomers. Cloud computing, Map/Reduce, Hadoop, Spark, etc. many new technology has sprung up in recent years. Comparing to the high performance computing(HPC), Data is the center of these new technology. So, a new computing architecture infrastructure is necessary, which can be shared by both HPC and big data processing. Based on Astronomy Cloud project of Chinese Virtual Observatory (China-VO), we have made much efforts to optimize the designation of the hybrid computing platform. which include the hardware architecture, cluster management, Job and Resource scheduling.
Optimal Metastability-Containing Sorting Networks
Venue: arXiv cs.DC 2018
Year: 2018

When setup/hold times of bistable elements are violated, they may become metastable, i.e., enter a transient state that is neither digital 0 nor 1. In general, metastability cannot be avoided, a problem that manifests whenever taking discrete measurements of analog values. Metastability of the output then reflects uncertainty as to whether a measurement should be rounded up or down to the next possible measurement outcome. Surprisingly, Lenzen and Medina (ASYNC 2016) showed that metastability can be contained, i.e., measurement values can be correctly sorted without resolving metastability first. However, both their work and the state of the art by Bund et al. (DATE 2017) leave open whether such a solution can be as small and fast as standard sorting networks. We show that this is indeed possible, by providing a circuit that sorts Gray code inputs (possibly containing a metastable bit) and has asymptotically optimal depth and size. Concretely, for 10-channel sorting networks and 16-bit wide inputs, we improve by 48.46% in delay and by 71.58% in area over Bund et al. Our simulations indicate that straightforward transistor-level optimization is likely to result in performance on par with standard (non-containing) solutions.
Spectral Efficiency Optimization For Millimeter Wave Multi-User MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

As a key enabling technology for 5G wireless, millimeter wave (mmWave) communication motivates the utilization of large-scale antenna arrays for achieving highly directional beamforming. However, the high cost and power consumption of RF chains stands in the way of adoption of the optimal fullydigital precoding in large-array systems. To reduce the number of RF chains while still maintaining the spatial multiplexing gain of large-array, hybrid precoding architecture has been proposed for mmWave systems and received considerable interest in both industry and academia. However, the optimal hybrid precoding design has not been fully understood, especially for the multi-user MIMO case. This paper is the first work that directly addresses the nonconvex hybrid precoding problem of mmWave multi-user MIMO systems (without any approximation) by using penalty dual decomposition (PDD) method. The proposed PDD method have a guaranteed convergence to KKT solutions of the hybrid precoding problem under a mild assumption. Simulation results show that, even when both the transmitter and the receivers are equipped with the fewest RF chains that are required to support multi-stream transmission, hybrid precoding can still approach the performance of fully-digital precoding in both the infinite resolution phase shifter case and the finite resolution phase shifter case with several bits quantization.
Decentralized Caching for Content Delivery Based on Blockchain: A Game Theoretic Perspective
Venue: arXiv cs.NI 2018
Year: 2018

Blockchains enables tamper-proof, ordered logging for transactional data in a decentralized manner over open-access, overlay peer-to-peer networks. In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains. We employ the blockchain-based smart contracts to construct an autonomous content caching market. In the market, the cache helpers are able to autonomously adapt their caching strategies according to the market statistics obtained from the blockchain, and the truthfulness of trustless nodes are financially enforced by smart contract terms. Further, we propose an incentive-compatible consensus mechanism based on proof-of-stake to financially encourage the cache helpers to stay active in service. We model the interaction between the cache helpers and the content providers as a Chinese restaurant game. Based on the theoretical analysis regarding the Nash equilibrium of the game, we propose a decentralized strategy-searching algorithm using sequential best response. The simulation results demonstrate both the efficiency and reliability of the proposed equilibrium searching algorithm.
Edge Computing Meets Millimeter-wave Enabled VR: Paving the Way to Cutting the Cord
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a novel proactive computing and mmWave communication for ultra-reliable and low latency wireless virtual reality (VR is proposed. By leveraging information about users' poses, proactive computing and caching are used to pre-compute and store users' HD video frames to minimize the computing latency. Furthermore, multi-connectivity is exploited to ensure reliable mmWave links to deliver users' requested HD frames. The performance of the proposed approach is validated on a VR network serving an interactive gaming arcade, where dynamic and real-time rendering of HD video frames is needed and impulse actions of different players impact the content to be shown. Simulation results show significant gains of up to $30\%$ reduction in end-to-end delay and $50\%$ in the $90^{\textrm{th}}$ percentile communication delay.
Task-parallel Analysis of Molecular Dynamics Trajectories
Venue: arXiv cs.DC 2018
Year: 2018

Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel frameworks: Spark, Dask and RADICAL-Pilot with respect to their ability to support data analytics on HPC resources and compare them with MPI. We investigate the data analysis requirements of Molecular Dynamics (MD) simulations which are significant consumers of supercomputing cycles, producing immense amounts of data. A typical large-scale MD simulation of a physical system of O(100k) atoms over μsecs can produce from O(10) GB to O(1000) GBs of data. We propose and evaluate different approaches for parallelization of a representative set of MD trajectory analysis algorithms, in particular the computation of path similarity and leaflet identification. We evaluate Spark, Dask and RADICAL-Pilot with respect to their abstractions and runtime engine capabilities to support these algorithms. We provide a conceptual basis for comparing and understanding different frameworks that enable users to select the optimal system for each application. We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

We present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in "holes" whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.
Clustering with Deep Learning: Taxonomy and New Methods
Venue: arXiv cs.LG 2018
Year: 2018

Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.
Byzantine Gathering in Polynomial Time
Venue: arXiv cs.DS 2018
Year: 2018

We study the task of Byzantine gathering in a network modeled as a graph. Despite the presence of Byzantine agents, all the other (good) agents, starting from possibly different nodes and applying the same deterministic algorithm, have to meet at the same node in finite time and stop moving. An adversary chooses the initial nodes of the agents and assigns a different label to each of them. The agents move in synchronous rounds and communicate with each other only when located at the same node. Within the team, f of the agents are Byzantine. A Byzantine agent acts in an unpredictable way: in particular it may forge the label of another agent or create a completely new one. Besides its label, which corresponds to a local knowledge, an agent is assigned some global knowledge GK that is common to all agents. In literature, the Byzantine gathering problem has been analyzed in arbitrary n-node graphs by considering the scenario when GK=(n,f) and the scenario when GK=f. In the first (resp. second) scenario, it has been shown that the minimum number of good agents guaranteeing deterministic gathering of all of them is f+1 (resp. f+2). For both these scenarios, all the existing deterministic algorithms, whether or not they are optimal in terms of required number of good agents, have a time complexity that is exponential in n and L, where L is the largest label belonging to a good agent. In this paper, we seek to design a deterministic solution for Byzantine gathering that makes a concession on the proportion of Byzantine agents within the team, but that offers a significantly lower complexity. We also seek to use a global knowledge whose the length of the binary representation is small. Assuming that the agents are in a strong team i.e., a team in which the number of good agents is at least some prescribed value that is quadratic in f, we give positive and negative results.
From Centralized to Decentralized Coded Caching
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of designing decentralized schemes for coded caching. In this problem there are $K$ users each caching $M$ files out of a library of $N$ total files. The question is to minimize $R$, the number of broadcast transmissions to satisfy all the user demands. Decentralized schemes allow the creation of each cache independently, allowing users to join or leave without dependencies. Previous work showed that to achieve a coding gain $g$, i.e. $R \leq K (1-M/N)/g$ transmissions, each file has to be divided into number of subpackets that is exponential in $g$. In this work we propose a simple translation scheme that converts any constant rate centralized scheme into a random decentralized placement scheme that guarantees a target coding gain of $g$. If the file size in the original constant rate centralized scheme is subexponential in $K$, then the file size for the resulting scheme is subexponential in $g$. When new users join, the rest of the system remains the same. However, we require an additional communication overhead of $O(\log K)$ bits to determine the new user's cache state. We also show that the worst-case rate guarantee degrades only by a constant factor due to the dynamics of user arrival and departure.
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
Venue: arXiv cs.CL 2018
Year: 2018

The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users' daily lives by steering them towards behaviors that increase happiness. With the goal of building technology that can understand how people express their happy moments in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we make publicly available. This paper describes HappyDB and its properties, and outlines several important NLP problems that can be studied with the help of the corpus. We also apply several state-of-the-art analysis techniques to analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research.
Contact Localization through Spatially Overlapping Piezoresistive Signals
Venue: arXiv cs.RO 2018
Year: 2018

Achieving high spatial resolution in contact sensing for robotic manipulation often comes at the price of increased complexity in fabrication and integration. One traditional approach is to fabricate a large number of taxels, each delivering an individual, isolated response to a stimulus. In contrast, we propose a method where the sensor simply consists of a continuous volume of piezoresistive elastomer with a number of electrodes embedded inside. We measure piezoresistive effects between all pairs of electrodes in the set, and count on this rich signal set containing the information needed to pinpoint contact location with high accuracy using regression algorithms. In our validation experiments, we demonstrate submillimeter median accuracy in locating contact on a 10mm by 16mm sensor using only four electrodes (creating six unique pairs). In addition to extracting more information from fewer wires, this approach lends itself to simple fabrication methods and makes no assumptions about the underlying geometry, simplifying future integration on robot fingers.
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Since the convolutional neural network (CNN) is be- lieved to find right features for a given problem, the study of hand-crafted features is somewhat neglected these days. In this paper, we show that finding an appropriate feature for the given problem may be still important as they can en- hance the performance of CNN-based algorithms. Specif- ically, we show that feeding an appropriate feature to the CNN enhances its performance in some face related works such as age/gender estimation, face detection and emotion recognition. We use Gabor filter bank responses for these tasks, feeding them to the CNN along with the input image. The stack of image and Gabor responses can be fed to the CNN as a tensor input, or as a fused image which is a weighted sum of image and Gabor responses. The Gabor filter parameters can also be tuned depending on the given problem, for increasing the performance. From the extensive experiments, it is shown that the proposed methods provide better performance than the conventional CNN-based methods that use only the input images.
Block SOS Decomposition
Venue: arXiv cs.SC 2018
Year: 2018

A widely used method for solving SOS (Sum Of Squares) decomposition problem is to reduce it to the problem of semi-definite programs (SDPs) which can be efficiently solved in theory. In practice, although many SDP solvers can work out some problems of big scale, the efficiency and reliability of such method decrease greatly while the input size increases. Recently, by exploiting the sparsity of the input SOS decomposition problem, some preprocessing algorithms were proposed [5,17], which first divide the input problem satisfying special definitions or properties into smaller SDP problems and then pass the smaller ones to SDP solvers to obtain reliable results efficiently. A natural question is that to what extent the above mentioned preprocessing algorithms work. That is, how many polynomials satisfying those definitions or properties are there in the SOS polynomials? In this paper, we define a concept of block SOS decomposable polynomials which is a generalization of those special classes in [5] and [17]. Roughly speaking, it is a class of polynomials whose SOS decomposition problem can be transformed into smaller ones (in other words, the corresponding SDP matrices can be block-diagnolized) by considering their supports only (coefficients are not considered). Then we prove that the set of block SOS decomposable polynomials has measure zero in the set of SOS polynomials. That means if we only consider supports (not with coefficients) of polynomials, such algorithms decreasing the size of SDPs for those SDP-based SOS solvers can only work on very few polynomials. As a result, this shows that the SOS decomposition problems that can be optimized by the above mentioned preprocessing algorithms are very few.
Optimal Cache Leasing from a Mobile Network Operator to a Content Provider
Venue: arXiv cs.NI 2018
Year: 2018

Caching popular content at the wireless edge is recently proposed as a means to reduce congestion at the backbone of cellular networks. The two main actors involved are Mobile Network Operators (MNOs) and Content Providers (CPs). In this work, we consider the following arrangement: an MNO pre-installs memory on its wireless equipment (e.g. Base Stations) and invites a unique CP to use them, with monetary cost. The CP will lease memory space and place its content; the MNO will associate network users to stations. For a given association policy, the MNO may help (or not) the CP to offload traffic, depending on whether the association takes into account content placement. We formulate an optimization problem from the CP perspective, which aims at maximizing traffic offloading with minimum leasing costs. This is a joint optimization problem that can include any association policy, and can also derive the optimal one. We present a general exact solution using Benders decomposition. It iteratively updates decisions of the two actors separately and converges to the global optimum. We illustrate the optimal CP leasing/placement strategy and hit probability gains under different association policies. Performance is maximised when the MNO association follows CP actions.
An Algebraic Approach for Reasoning About Information Flow
Venue: arXiv cs.CR 2018
Year: 2018

This paper concerns the analysis of information leaks in security systems. We address the problem of specifying and analyzing large systems in the (standard) channel model used in quantitative information flow (QIF). We propose several operators which match typical interactions between system components. We explore their algebraic properties with respect to the security-preserving refinement relation defined by Alvim et al. and McIver et al. We show how the algebra can be used to simplify large system specifications in order to facilitate the computation of information leakage bounds. We demonstrate our results on the specification and analysis of the Crowds Protocol. Finally, we use the algebra to justify a new algorithm to compute leakage bounds for this protocol.
Depending on Session-Typed Processes
Venue: arXiv cs.PL 2018
Year: 2018

This work proposes a dependent type theory that combines functions and session-typed processes (with value dependencies) through a contextual monad, internalising typed processes in a dependently-typed lambda-calculus. The proposed framework, by allowing session processes to depend on functions and vice-versa, enables us to specify and statically verify protocols where the choice of the next communication action can depend on specific values of received data. Moreover, the type theoretic nature of the framework endows us with the ability to internally describe and prove predicates on process behaviours. Our main results are type soundness of the framework, and a faithful embedding of the functional layer of the calculus within the session-typed layer, showcasing the expressiveness of dependent session types.
Adversarial Classification on Social Networks
Venue: arXiv cs.MA 2018
Year: 2018

The spread of unwanted or malicious content through social media has become a major challenge. Traditional examples of this include social network spam, but an important new concern is the propagation of fake news through social media. A common approach for mitigating this problem is by using standard statistical classification to distinguish malicious (e.g., fake news) instances from benign (e.g., actual news stories). However, such an approach ignores the fact that malicious instances propagate through the network, which is consequential both in quantifying consequences (e.g., fake news diffusing through the network), and capturing detection redundancy (bad content can be detected at different nodes). An additional concern is evasion attacks, whereby the generators of malicious instances modify the nature of these to escape detection. We model this problem as a Stackelberg game between the defender who is choosing parameters of the detection model, and an attacker, who is choosing both the node at which to initiate malicious spread, and the nature of malicious entities. We develop a novel bi-level programming approach for this problem, as well as a novel solution approach based on implicit function gradients, and experimentally demonstrate the advantage of our approach over alternatives which ignore network structure.