OpenRaiser/Intern-Atlas huggingface dataset:
Multiple Antenna Assisted Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2018
Year: 2018

Non-orthogonal multiple access (NOMA) is potentially capable of circumventing the limitations of the classic orthogonal multiple access schemes, hence it has recently received significant research attention both in industry and academia. This article is focused on exploiting multiple antenna techniques in NOMA networks, with an emphasis on investigating the rate region of multiple-input multiple-output (MIMO)-NOMA, whist reviewing two popular multiple antennas aided NOMA structures, as well as underlining resource management problems of both single-carrier and multi-carrier MIMO-NOMA networks. This article also points out several effective methods of tackling the practical implementation constraints of multiple antenna NOMA networks. Finally, some promising open research directions are provided in context of multiple antenna aided NOMA.
Stochastic Successive Convex Optimization for Two-timescale Hybrid Precoding in Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid precoding, which consists of an RF precoder and a baseband precoder, is a popular precoding architecture for massive MIMO due to its low hardware cost and power consumption. In conventional hybrid precoding, both RF and baseband precoders are adaptive to the real-time channel state information (CSI). As a result, an individual RF precoder is required for each subcarrier in wideband systems, leading to high implementation cost. To overcome this issue, two-timescale hybrid precoding (THP), which adapts the RF precoder to the channel statistics, has been proposed. Since the channel statistics are approximately the same over different subcarriers, only a single RF precoder is required in THP. Despite the advantages of THP, there lacks a unified and efficient algorithm for its optimization due to the non-convex and stochastic nature of the problem. Based on stochastic successive convex approximation (SSCA), we propose an online algorithmic framework called SSCA-THP for general THP optimization problems, in which the hybrid precoder is updated by solving a quadratic surrogate optimization problem whenever a new channel sample is obtained. Then we prove the convergence of SSCA-THP to stationary points. Finally, we apply SSCA-THP to solve three important THP optimization problems and verify its advantages over existing solutions.
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods
Venue: arXiv cs.AI 2018
Year: 2018

This paper investigates estimating the variance of a temporal-difference learning agent's update target. Most reinforcement learning methods use an estimate of the value function, which captures how good it is for the agent to be in a particular state and is mathematically expressed as the expected sum of discounted future rewards (called the return). These values can be straightforwardly estimated by averaging batches of returns using Monte Carlo methods. However, if we wish to update the agent's value estimates during learning--before terminal outcomes are observed--we must use a different estimation target called the λ-return, which truncates the return with the agent's own estimate of the value function. Temporal difference learning methods estimate the expected λ-return for each state, allowing these methods to update online and incrementally, and in most cases achieve better generalization error and faster learning than Monte Carlo methods. Naturally one could attempt to estimate higher-order moments of the λ-return. This paper is about estimating the variance of the λ-return. Prior work has shown that given estimates of the variance of the λ-return, learning systems can be constructed to (1) mitigate risk in action selection, and (2) automatically adapt the parameters of the learning process itself to improve performance. Unfortunately, existing methods for estimating the variance of the λ-return are complex and not well understood empirically. We contribute a method for estimating the variance of the λ-return directly using policy evaluation methods from reinforcement learning. Our approach is significantly simpler than prior methods that independently estimate the second moment of the λ-return. Empirically our new approach behaves at least as well as existing approaches, but is generally more robust.
Toward Cross-Layer Design for Non-Orthogonal Multiple Access: A Quality-of-Experience Perspective
Venue: arXiv cs.NI 2018
Year: 2018

Recent years have seen proliferation in versatile mobile devices and an upsurge in the growth of data-consuming application services. Orthogonal multiple access (OMA) technologies in today's mobile systems fall inefficient in the presence of such massive connectivity and traffic demands. In this regards, non-orthogonal multiple access (NOMA) has been advocated by the research community to embrace unprecedented requirements. Current NOMA designs have been demonstrated to largely improve conventional system performance in terms of throughput and latency, while their impact on the end users' perceived experience has not yet been comprehensively understood. We envision that quality-of-experience (QoE) awareness is a key pillar for NOMA designs to fulfill versatile user demands in the 5th generation (5G) wireless communication systems. This article systematically investigates QoE-aware NOMA designs that translate the physical-layer benefits of NOMA to the improvement of users' perceived experience in upper layers. We shed light on design principles and key challenges in realizing QoE-aware NOMA designs. With these principles and challenges in mind, we develop a general architecture with a dynamic network scheduling scheme. We provide some implications for future QoE-aware NOMA designs by conducting a case study in video streaming applications.
Probabilistic Planning by Probabilistic Programming
Venue: arXiv cs.AI 2018
Year: 2018

Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is defined over a set of actions which change that state in one way or another. Planning in many real-world settings, however, is much more involved: an agent's knowledge is almost never simply a set of facts that are true, and actions that the agent intends to execute never operate the way they are supposed to. Thus, probabilistic planning attempts to incorporate stochastic models directly into the planning process. In this article, we briefly report on probabilistic planning through the lens of probabilistic programming: a programming paradigm that aims to ease the specification of structured probability distributions. In particular, we provide an overview of the features of two systems, HYPE and ALLEGRO, which emphasise different strengths of probabilistic programming that are particularly useful for complex modelling issues raised in probabilistic planning. Among other things, with these systems, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.
Finitary-based Domain Theory in Coq: An Early Report
Venue: arXiv cs.LO 2018
Year: 2018

In domain theory every finite computable object can be represented by a single mathematical object instead of a set of objects, using the notion of finitary-basis. In this article we report on our effort to formalize domain theory in Coq in terms of finitary-basis.
Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we model a pixel as a linear combination of endmembers sampled from probability distributions of Gaussian mixture models (GMM). The parameters of the GMM distributions are estimated using spectral libraries. Abundances are estimated based on the distribution parameters. The advantage of this algorithm is that the model size grows very slowly as a function of the library size. To validate this method, we used data collected by the AVIRIS sensor over the Santa Barbara region: two 16 m spatial resolution and two 4 m spatial resolution images. 64 validated regions of interest (ROI) (180 m by 180 m) were used to assess estimate accuracy. Ground truth was obtained using 1 m images leading to the following 6 classes: turfgrass, non-photosynthetic vegetation (NPV), paved, roof, soil, and tree. Spectral libraries were built by manually identifying and extracting pure spectra from both resolution images, resulting in 3,287 spectra at 16 m and 15,426 spectra at 4 m. We then unmixed ROIs of each resolution using the following unmixing algorithms: the set-based algorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, and BCM. The original libraries were used for the distribution-based algorithms whereas set-based methods required a sophisticated reduction method, resulting in reduced libraries of 61 spectra at 16 m and 95 spectra at 4 m. The results show that GMM performs best among the distribution-based methods, producing comparable accuracy to MESMA, and may be more robust across datasets.
Rollercoasters and Caterpillars
Venue: arXiv cs.CG 2018
Year: 2018

A rollercoaster is a sequence of real numbers for which every maximal contiguous subsequence, that is increasing or decreasing, has length at least three. By translating this sequence to a set of points in the plane, a rollercoaster can be defined as a polygonal path for which every maximal sub-path, with positive- or negative-slope edges, has at least three points. Given a sequence of distinct real numbers, the rollercoaster problem asks for a maximum-length subsequence that is a rollercoaster. It was conjectured that every sequence of $n$ distinct real numbers contains a rollercoaster of length at least $\lceil n/2\rceil$ for $n>7$, while the best known lower bound is $Ω(n/\log n)$. In this paper we prove this conjecture. Our proof is constructive and implies a linear-time algorithm for computing a rollercoaster of this length. Extending the $O(n\log n)$-time algorithm for computing a longest increasing subsequence, we show how to compute a maximum-length rollercoaster within the same time bound. A maximum-length rollercoaster in a permutation of $\{1,\dots,n\}$ can be computed in $O(n \log \log n)$ time. The search for rollercoasters was motivated by orthogeodesic point-set embedding of caterpillars. A caterpillar is a tree such that deleting the leaves gives a path, called the spine. A top-view caterpillar is one of degree 4 such that the two leaves adjacent to each vertex lie on opposite sides of the spine. As an application of our result on rollercoasters, we are able to find a planar drawing of every $n$-node top-view caterpillar on every set of $\frac{25}{3}n$ points in the plane, such that each edge is an orthogonal path with one bend. This improves the previous best known upper bound on the number of required points, which is $O(n \log n)$. We also show that such a drawing can be obtained in linear time, provided that the points are given in sorted order.
Efficient Adaptive Detection of Complex Event Patterns
Venue: arXiv cs.DB 2018
Year: 2018

Complex event processing (CEP) is widely employed to detect occurrences of predefined combinations (patterns) of events in massive data streams. As new events are accepted, they are matched using some type of evaluation structure, commonly optimized according to the statistical properties of the data items in the input stream. However, in many real-life scenarios the data characteristics are never known in advance or are subject to frequent on-the-fly changes. To modify the evaluation structure as a reaction to such changes, adaptation mechanisms are employed. These mechanisms typically function by monitoring a set of properties and applying a new evaluation plan when significant deviation from the initial values is observed. This strategy often leads to missing important input changes or it may incur substantial computational overhead by over-adapting. In this paper, we present an efficient and precise method for dynamically deciding whether and how the evaluation structure should be reoptimized. This method is based on a small set of constraints to be satisfied by the monitored values, defined such that a better evaluation plan is guaranteed if any of the constraints is violated. To the best of our knowledge, our proposed mechanism is the first to provably avoid false positives on reoptimization decisions. We formally prove this claim and demonstrate how our method can be applied on known algorithms for evaluation plan generation. Our extensive experimental evaluation on real-world datasets confirms the superiority of our strategy over existing methods in terms of performance and accuracy.
Structuring Spreadsheets with the "Lish" Data Model
Venue: arXiv cs.SE 2018
Year: 2018

A spreadsheet is remarkably flexible in representing various forms of structured data, but the individual cells have no knowledge of the larger structures of which they may form a part. This can hamper comprehension and increase formula replication, increasing the risk of error on both scores. We explore a novel data model (called the "lish") that could form an alternative to the traditional grid in a spreadsheet-like environment. Its aim is to capture some of these higher structures while preserving the simplicity that makes a spreadsheet so attractive. It is based on cells organised into nested lists, in each of which the user may optionally employ a template to prototype repeating structures. These template elements can be likened to the marginal "cells" in the borders of a traditional worksheet, but are proper members of the sheet and may themselves contain internal structure. A small demonstration application shows the "lish" in operation.
Interactive Diversity Optimization of Environments
Venue: arXiv cs.HC 2018
Year: 2018

The design of a building requires an architect to balance a wide range of constraints: aesthetic, geometric, usability, lighting, safety, etc. At the same time, there are often a multiplicity of diverse designs that can meet these constraints equally well. Architects must use their skills and artistic vision to explore these rich but highly constrained design spaces. A number of computer-aided design tools use automation to provide useful analytical data and optimal designs with respect to certain fitness criteria. However, this automation can come at the expense of a designer's creative control. We propose uDOME, a user-in-the-loop system for computer-aided design exploration that balances automation and control by efficiently exploring, analyzing, and filtering the space of environment layouts to better inform an architect's decision-making. At each design iteration, uDOME provides a set of diverse designs which satisfy user-defined constraints and optimality criteria within a user defined parameterization of the design space. The user then selects a design and performs a similar optimization with the same or different parameters and objectives. This exploration process can be repeated as many times as the designer wishes. Our user studies indicates that \DOME, with its diversity-based approach, improves the efficiency and effectiveness of even novice users with minimal training, without compromising the quality of their designs.
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
Venue: arXiv cs.AI 2018
Year: 2018

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.
Enhancing Byzantine fault tolerance using MD5 checksum and delay variation in Cloud services
Venue: arXiv cs.DC 2018
Year: 2018

Cloud computing management are beyond typical human narratives. However if a virtual system is not effectively designed to tolerate Byzantine faults, it could lead to a faultily executed mission rather than a cloud crash. The cloud could recover from the crash but it could not recover from the loss of credibility. Moreover no amount of replication or fault handling measures can be helpful in facing a Byzantine fault unless the virtual system is designed to detect, tolerate and eliminate such faults. However research efforts that are made to address Byzantine faults have not provided convincing solutions vastly due to their limited capabilities in detecting the Byzantine faults. As a result, in this paper the Cloud system is modeled as a discrete system to determine the virtual system behavior at varying time intervals. A delay variation variable as a measure of deviation for the expected processing delay associated with the virtual nodes takes values from the set of P {low, normal, high, extreme}. Similarly, a check sum error variable which is even computed for intra nodes that have no attachment to TCP/IP stack takes values from the set of P {no error, error}. These conditions are then represented by the occurrence of faulty events that cause specific component mode transition from fail safe to fail-stop or byzantine prone.
Invariant Checking of NRA Transition Systems via Incremental Reduction to LRA with EUF
Venue: arXiv cs.LO 2018
Year: 2018

Model checking invariant properties of designs, represented as transition systems, with non-linear real arithmetic (NRA), is an important though very hard problem. On the one hand NRA is a hard-to-solve theory; on the other hand most of the powerful model checking techniques lack support for NRA. In this paper, we present a counterexample-guided abstraction refinement (CEGAR) approach that leverages linearization techniques from differential calculus to enable the use of mature and efficient model checking algorithms for transition systems on linear real arithmetic (LRA) with uninterpreted functions (EUF). The results of an empirical evaluation confirm the validity and potential of this approach.
MengeROS: a Crowd Simulation Tool for Autonomous Robot Navigation
Venue: arXiv cs.RO 2018
Year: 2018

While effective navigation in large, crowded environments is essential for an autonomous robot, preliminary testing of algorithms to support it requires simulation across a broad range of crowd scenarios. Most available simulation tools provide either realistic crowds without robots or realistic robots without realistic crowds. This paper introduces MengeROS, a 2-D simulator that realistically integrates multiple robots and crowds. MengeROS provides a broad range of settings in which to test the capabilities and performance of navigation algorithms designed for large crowded environments.
Election campaigning on social media: Politicians, audiences and the mediation of political communication on Facebook and Twitter
Venue: arXiv cs.CY 2018
Year: 2018

Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.
3D Scanning: A Comprehensive Survey
Venue: arXiv cs.CV 2018
Year: 2018

This paper provides an overview of 3D scanning methodologies and technologies proposed in the existing scientific and industrial literature. Throughout the paper, various types of the related techniques are reviewed, which consist, mainly, of close-range, aerial, structure-from-motion and terrestrial photogrammetry, and mobile, terrestrial and airborne laser scanning, as well as time-of-flight, structured-light and phase-comparison methods, along with comparative and combinational studies, the latter being intended to help make a clearer distinction on the relevance and reliability of the possible choices. Moreover, outlier detection and surface fitting procedures are discussed concisely, which are necessary post-processing stages.
Pointwise Partial Information Decomposition using the Specificity and Ambiguity Lattices
Venue: arXiv cs.IT 2018
Year: 2018

What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.