OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Antenna Coded Caching
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we consider a single-cell downlink scenario where a multiple-antenna base station delivers contents to multiple cache-enabled user terminals. Based on the multicasting opportunities provided by the so-called Coded Caching technique, we investigate three delivery approaches. Our baseline scheme employs the coded caching technique on top of max-min fair multicasting. The second one consists of a joint design of Zero-Forcing (ZF) and coded caching, where the coded chunks are formed in the signal domain (complex field). The third scheme is similar to the second one with the difference that the coded chunks are formed in the data domain (finite field). We derive closed-form rate expressions where our results suggest that the latter two schemes surpass the first one in terms of Degrees of Freedom (DoF). However, at the intermediate SNR regime forming coded chunks in the signal domain results in power loss, and will deteriorate throughput of the second scheme. The main message of our paper is that the schemes performing well in terms of DoF may not be directly appropriate for intermediate SNR regimes, and modified schemes should be employed.
Node-Independent Spanning Trees in Gaussian Networks
Venue: arXiv cs.DC 2017
Year: 2017

Message broadcasting in networks could be carried over spanning trees. A set of spanning trees in the same network is node independent if two conditions are satisfied. First, all trees are rooted at node $r$. Second, for every node $u$ in the network, all trees' paths from $r$ to $u$ are node-disjoint, excluding the end nodes $r$ and $u$. Independent spanning trees have applications in fault-tolerant communications and secure message distributions. Gaussian networks and two-dimensional toroidal networks share similar topological characteristics. They are regular of degree four, symmetric, and node-transitive. Gaussian networks, however, have relatively lesser network diameter that could result in a better performance. This promotes Gaussian networks to be a potential alternative for two-dimensional toroidal networks. In this paper, we present constructions for node independent spanning trees in dense Gaussian networks. Based on these constructions, we design routing algorithms that can be used in fault-tolerant routing and secure message distribution. We also design fault-tolerant algorithms to construct these trees in parallel.
On the Tradeoff Region of Secure Exact-Repair Regenerating Codes
Venue: arXiv cs.IT 2017
Year: 2017

We consider the $(n,k,d,\ell)$ secure exact-repair regenerating code problem, which generalizes the $(n,k,d)$ exact-repair regenerating code problem with the additional constraint that the stored file needs to be kept information-theoretically secure against an eavesdropper, who can access the data transmitted to regenerate a total of $\ell$ different failed nodes. For all known results on this problem, the achievable tradeoff regions between the normalized storage capacity and repair bandwidth have a single corner point, achieved by a scheme proposed by Shah, Rashmi and Kumar (the SRK point). Since the achievable tradeoff regions of the exact-repair regenerating code problem without any secrecy constraints are known to have multiple corner points in general, these existing results suggest a phase-change-like behavior, i.e., enforcing a secrecy constraint ($\ell\geq 1$) immediately reduces the tradeoff region to one with a single corner point. In this work, we first show that when the secrecy parameter $\ell$ is sufficiently large, the SRK point is indeed the only corner point of the tradeoff region. However, when $\ell$ is small, we show that the tradeoff region can in fact have multiple corner points. In particular, we establish a precise characterization of the tradeoff region for the $(7,6,6,1)$ problem, which has exactly two corner points. Thus, a smooth transition, instead of a phase-change-type of transition, should be expected as the secrecy constraint is gradually strengthened.
Guaranteed Parameter Estimation for Discrete Energy Minimization
Venue: arXiv cs.CV 2017
Year: 2017

Structural learning, a method to estimate the parameters for discrete energy minimization, has been proven to be effective in solving computer vision problems, especially in 3D scene parsing. As the complexity of the models increases, structural learning algorithms turn to approximate inference to retain tractability. Unfortunately, such methods often fail because the approximation can be arbitrarily poor. In this work, we propose a method to overcome this limitation through exploiting the properties of the joint problem of training time inference and learning. With the help of the learning framework, we transform the inapproximable inference problem into a polynomial time solvable one, thereby enabling tractable exact inference while still allowing an arbitrary graph structure and full potential interactions. Our learning algorithm is guaranteed to return a solution with a bounded error to the global optimal within the feasible parameter space. We demonstrate the effectiveness of this method on two point cloud scene parsing datasets. Our approach runs much faster and solves a problem that is intractable for previous, well-known approaches.
Scalable Spectrum Allocation and User Association in Networks with Many Small Cells
Venue: arXiv cs.IT 2017
Year: 2017

A scalable framework is developed to allocate radio resources across a large number of densely deployed small cells with given traffic statistics on a slow timescale. Joint user association and spectrum allocation is first formulated as a convex optimization problem by dividing the spectrum among all possible transmission patterns of active access points (APs). To improve scalability with the number of APs, the problem is reformulated using local patterns of interfering APs. To maintain global consistency among local patterns, inter-cluster interaction is characterized as hyper-edges in a hyper-graph with nodes corresponding to neighborhoods of APs. A scalable solution is obtained by iteratively solving a convex optimization problem for bandwidth allocation with reduced complexity and constructing a global spectrum allocation using hyper-graph coloring. Numerical results demonstrate the proposed solution for a network with 100 APs and several hundred user equipments. For a given quality of service (QoS), the proposed scheme can increase the network capacity several fold compared to assigning each user to the strongest AP with full-spectrum reuse.
Investigating the role of musical genre in human perception of music stretching resistance
Venue: arXiv cs.MM 2017
Year: 2017

To stretch a music piece to a given length is a common demand in people's daily lives, e.g., in audio-video synchronization and animation production. However, it is not always guaranteed that the stretched music piece is acceptable for general audience since music stretching suffers from people's perceptual artefacts. Over-stretching a music piece will make it uncomfortable for human psychoacoustic hearing. The research on music stretching resistance attempts to estimate the maximum stretchability of music pieces to further avoid over-stretch. It has been observed that musical genres can significantly improve the accuracy of automatic estimation of music stretching resistance, but how musical genres are related to music stretching resistance has never been explained or studied in detail in the literature. In this paper, the characteristics of music stretching resistance are compared across different musical genres. It is found that music stretching resistance has strong intra-genre cohesiveness and inter-genre discrepancies in the experiments. Moreover, the ambiguity and the symmetry of music stretching resistance are also observed in the experimental analysis. These findings lead to a new measurement on the similarity between different musical genres based on their music stretching resistance. In addition, the analysis of variance (ANOVA) also supports the findings in this paper by verifying the significance of musical genre in shaping music stretching resistance.
Modularized Morphing of Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.
Heterogeneous Cellular Networks with LoS and NLoS Transmissions--The Role of Massive MIMO and Small Cells
Venue: arXiv cs.IT 2017
Year: 2017

We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Using stochastic geometry, we derive tight approximation of achievable downlink rate that enables us to compare the performance between densifying small cells and expanding BS antenna arrays. Interestingly, we find that adding small cells into the network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always benefit the capacity until an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Furthermore, we find that allocating part of antennas to distributed small cell BSs works better than centralizing all antennas at the macro BS, and the optimal allocation proportion is also given for practical configuration reference. In summary, this work provides a further understanding on how to leverage small cells and massive MIMO in future heterogeneous cellular networks deployment.
Second Order Analysis for Joint Source-Channel Coding with Markovian Source
Venue: arXiv cs.IT 2017
Year: 2017

We derive the second order rates of joint source-channel coding, whose source obeys an irreducible and ergodic Markov process when the channel is a discrete memoryless, while a previous study solved it only in a special case. We also compare the joint source-channel scheme with the separation scheme in the second order regime while a previous study made a notable comparison only with numerical calculation. To make these two notable progress, we introduce two kinds of new distribution families, switched Gaussian convolution distribution and *-product distribution, which are defined by modifying the Gaussian distribution.
From First-Order Logic to Assertional Logic
Venue: arXiv cs.AI 2017
Year: 2017

First-Order Logic (FOL) is widely regarded as one of the most important foundations for knowledge representation. Nevertheless, in this paper, we argue that FOL has several critical issues for this purpose. Instead, we propose an alternative called assertional logic, in which all syntactic objects are categorized as set theoretic constructs including individuals, concepts and operators, and all kinds of knowledge are formalized by equality assertions. We first present a primitive form of assertional logic that uses minimal assumed knowledge and constructs. Then, we show how to extend it by definitions, which are special kinds of knowledge, i.e., assertions. We argue that assertional logic, although simpler, is more expressive and extensible than FOL. As a case study, we show how assertional logic can be used to unify logic and probability, and more building blocks in AI.
A Data-Oriented Model of Literary Language
Venue: arXiv cs.CL 2017
Year: 2017

We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 % of the variation in literary ratings.
Cost estimation of a fixed network deployment over an urban territory
Venue: arXiv cs.NI 2017
Year: 2017

Using theoretical results presented in former papers, two prototypes were developed, aiming at estimating within minutes the cost of a fibre network deployment on a given territory. The first one helps defining the limit of an urban territory and computes mathematical parameters representing its street system. The second one gives global information on a fixed network deployment on this territory, namely the probability distributions of distances and attenuation from a node of the network to the final customer, and an evaluation of the deployment cost, once given an architecture and engineering rules. This allows a final user to easily design various network architectures in the tool, to compare different deployment scenarios, and to optimize the budget and the efficiency of the network in a few minutes. The results were compared on two real French urban territories (in Tours and Rouen) to those given by an optimization tool currently used by Orange.
Joint Dictionary Learning for Example-based Image Super-resolution
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a new joint dictionary learning method for example-based image super-resolution (SR), using sparse representation. The low-resolution (LR) dictionary is trained from a set of LR sample image patches. Using the sparse representation coefficients of these LR patches over the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing the reconstruction error of HR sample patches. The error criterion used here is the mean square error. In this way we guarantee that the HR patches have the same sparse representation over HR dictionary as the LR patches over the LR dictionary, and at the same time, these sparse representations can well reconstruct the HR patches. Simulation results show the effectiveness of our method compared to the state-of-art SR algorithms.
Subgaussian Tail Bounds via Stability Arguments
Venue: arXiv cs.DM 2017
Year: 2017

Sums of independent, bounded random variables concentrate around their expectation approximately as well a Gaussian of the same variance. Well known results of this form include the Bernstein, Hoeffding, and Chernoff inequalities and many others. We present an alternative proof of these tail bounds based on what we call a stability argument, which avoids bounding the moment generating function or higher-order moments of the distribution. Our stability argument is inspired by recent work on the generalization properties of differential privacy and their connection to adaptive data analysis (Bassily et al., STOC 2016).
Functional Decomposition using Principal Subfields
Venue: arXiv cs.SC 2017
Year: 2017

Let $f\in K(t)$ be a univariate rational function. It is well known that any non-trivial decomposition $g \circ h$, with $g,h\in K(t)$, corresponds to a non-trivial subfield $K(f(t))\subsetneq L \subsetneq K(t)$ and vice-versa. In this paper we use the idea of principal subfields and fast subfield-intersection techniques to compute the subfield lattice of $K(t)/K(f(t))$. This yields a Las Vegas type algorithm with improved complexity and better run times for finding all non-equivalent complete decompositions of $f$.
Integer-Forcing Linear Receivers: A Design Criterion for Full-Diversity STBCs
Venue: arXiv cs.IT 2017
Year: 2017

In multiple-input multiple-output (MIMO) fading channels, the design criterion for full-diversity space-time block codes (STBCs) is primarily determined by the decoding method at the receiver. Although constructions of STBCs have predominantly matched the maximum-likelihood (ML) decoder, design criteria and constructions of full-diversity STBCs have also been reported for low-complexity linear receivers. A new receiver architecture called Integer-Forcing (IF) linear receiver has been proposed to MIMO channels by Zhan et al. which showed promising results for the high-rate V-BLAST encoding scheme. In this work we address the design of full-diversity STBCs for IF linear receivers. We derive an upper bound on the probability of decoding error, and show that STBCs that satisfy the non-vanishing singular value (NVS) property provide full-diversity for the IF receiver. We also present simulation results to demonstrate that linear designs with NVS property provide full diversity for IF receiver. As a special case of our analysis on STBCs, we present an upper bound on the error probability for the V-BLAST architecture presented by Zhan \emph{et al.}, and demonstrate that the IF linear receivers provide full receive diversity. Our results supplement the existing outage probability based results for the IF receiver.
Fault Tolerant Control of Automotive Air Conditioning Systems using a GIMC Structure
Venue: arXiv cs.SY 2017
Year: 2017

Although model-based fault tolerant control (FTC) has become prevalent in various engineering fields, its application to air-conditioning systems is limited due to the lack of control-oriented models to characterize the phase change of refrigerant in the vapor compression cycle. The emergence of moving boundary method (MBM) illuminates a promising way for FTC design. In this paper, we exploit a control-oriented nonlinear model comparable to MBM to design an FTC framework with a generalized internal model control (GIMC) approach. A fault detector and isolator (FDI) is developed to identify potential actuator and sensor faults. A fault compensator is employed to compensate these faults if detected. Comprehensive simulations are carried out to evaluate the developed FTC framework with promising results. Plant variations are explicitly considered to enhance the gain-scheduled FTC developments.
Fast Reconstruction of High-qubit Quantum States via Low Rate Measurements
Venue: arXiv cs.IT 2017
Year: 2017

Due to the exponential complexity of the resources required by quantum state tomography (QST), people are interested in approaches towards identifying quantum states which require less effort and time. In this paper, we provide a tailored and efficient method for reconstructing mixed quantum states up to $12$ (or even more) qubits from an incomplete set of observables subject to noises. Our method is applicable to any pure or nearly pure state $ρ$, and can be extended to many states of interest in quantum information processing, such as multi-particle entangled $W$ state, GHZ state and cluster states that are matrix product operators of low dimensions. The method applies the quantum density matrix constraints to a quantum compressive sensing optimization problem, and exploits a modified Quantum Alternating Direction Multiplier Method (Quantum-ADMM) to accelerate the convergence. Our algorithm takes $8,35$ and $226$ seconds respectively to reconstruct superposition state density matrices of $10,11,12$ qubits with acceptable fidelity, using less than $1 \%$ of measurements of expectation. To our knowledge it is the fastest realization that people can achieve using a normal desktop. We further discuss applications of this method using experimental data of mixed states obtained in an ion trap experiment of up to $8$ qubits.
On the links between argumentation-based reasoning and nonmonotonic reasoning
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in [9] with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by ASPIC+ under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by ASPIC+ is - according to the axioms of [9] - non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems they considered possible. This weakness underpins ASPIC+'s success in modelling other reasoning systems, and we conclude by considering the relationship between ASPIC+ and other weak logical systems.
Optimal Reachability in Divergent Weighted Timed Games
Venue: arXiv cs.GT 2017
Year: 2017

Weighted timed games are played by two players on a timed automaton equipped with weights: one player wants to minimise the accumulated weight while reaching a target, while the other has an opposite objective. Used in a reactive synthesis perspective, this quantitative extension of timed games allows one to measure the quality of controllers. Weighted timed games are notoriously difficult and quickly undecidable, even when restricted to non-negative weights. Decidability results exist for subclasses of one-clock games, and for a subclass with non-negative weights defined by a semantical restriction on the weights of cycles. In this work, we introduce the class of divergent weighted timed games as a generalisation of this semantical restriction to arbitrary weights. We show how to compute their optimal value, yielding the first decidable class of weighted timed games with negative weights and an arbitrary number of clocks. In addition, we prove that divergence can be decided in polynomial space. Last, we prove that for untimed games, this restriction yields a class of games for which the value can be computed in polynomial time.
An Auction Approach to Spectrum Management in HetNets
Venue: arXiv cs.NI 2017
Year: 2017

The growing demand in mobile Internet access calls for high capacity and energy efficient cellular access with better cell coverage. The in-band relaying solution, proposed in LTE-Advanced, improves coverage without requiring additional spectrum for backhauling, making its deployment more economical and practical. However, in-band relay without careful management incurs low spectrum utilization and reduces the system capacity. We propose auction-based solutions that aim at dynamic spectrum resource sharing, maximizing the utilization of precious spectrum resources. We first present a truthful auction that ensures a theoretical performance guarantee in terms of social welfare. Then in an extended system model that focuses on addressing the heterogeneity of resource blocks, we design a more practical auction mechanism. We implement our proposed auctions under large scale real-world settings. Simulation results verify the efficacy of proposed auctions, showing improvements in both cell coverage and spectrum efficiency.
The Velocity of the Propagating Wave for General Coupled Scalar Systems
Venue: arXiv cs.IT 2017
Year: 2017

We consider spatially coupled systems governed by a set of scalar density evolution equations. Such equations track the behavior of message-passing algorithms used, for example, in coding, sparse sensing, or constraint-satisfaction problems. Assuming that the "profile" describing the average state of the algorithm exhibits a solitonic wave-like behavior after initial transient iterations, we derive a formula for the propagation velocity of the wave. We illustrate the formula with two applications, namely Generalized LDPC codes and compressive sensing.