OpenRaiser/Intern-Atlas huggingface dataset:
Cache-enabled Uplink Transmission in Wireless Small Cell Networks
Venue: arXiv cs.IT 2016
Year: 2016

It is starting to become a big trend in the era of social networking that people produce and upload user-generated contents to Internet via wireless networks, bringing a significant burden on wireless uplink networks. In this paper, we contribute to designing and theoretical understanding of wireless cache-enabled upload transmission in a delay-tolerant small cell network to relieve the burden, and then propose the corresponding scheduling policies for the small base station (SBS) under the infinite and finite cache sizes. Specifically, the cache ability introduced by SBS enables SBS to eliminate the redundancy among the upload contents from users. This strategy not only alleviates the wireless backhual traffic congestion from SBS to a macro base station (MBS), but also improves the transmission efficiency of SBS. We then investigate the scheduling schemes of SBS to offload more data traffic under caching size constraint. Moreover, two operational regions for the wireless cache-enabled upload network, namely, the delay-limited region and the cache-limited region, are established to reveal the fundamental tradeoff between the delay tolerance and the cache ability. Finally, numerical results are provided to demonstrate the significant performance gains of the proposed wireless cache-enabled upload network.
Approximate Recursive Identification of Autoregressive Systems with Skewed Innovations
Venue: arXiv cs.SY 2016
Year: 2016

We propose a novel recursive system identification algorithm for linear autoregressive systems with skewed innovations. The algorithm is based on the variational Bayes approximation of the model with a multivariate normal prior for the model coefficients, multivariate skew-normally distributed innovations, and matrix-variate-normal - inverse-Wishart prior for the parameters of the innovation distribution. The proposed algorithm simultaneously estimates the model coefficients as well as the parameters of the innovation distribution, which are both allowed to be slowly time-varying. Through computer simulations, we compare the proposed method with a variational algorithm based on the normally-distributed innovations model, and show that modelling the skewness can provide improvement in identification accuracy.
Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes
Venue: arXiv cs.AI 2016
Year: 2016

This paper presents a new method to learn online policies in continuous state, continuous action, model-free Markov decision processes, with two properties that are crucial for practical applications. First, the policies are implementable with a very low computational cost: once the policy is computed, the action corresponding to a given state is obtained in logarithmic time with respect to the number of samples used. Second, our method is versatile: it does not rely on any a priori knowledge of the structure of optimal policies. We build upon the Fitted Q-iteration algorithm which represents the $Q$-value as the average of several regression trees. Our algorithm, the Fitted Policy Forest algorithm (FPF), computes a regression forest representing the Q-value and transforms it into a single tree representing the policy, while keeping control on the size of the policy using resampling and leaf merging. We introduce an adaptation of Multi-Resolution Exploration (MRE) which is particularly suited to FPF. We assess the performance of FPF on three classical benchmarks for reinforcement learning: the "Inverted Pendulum", the "Double Integrator" and "Car on the Hill" and show that FPF equals or outperforms other algorithms, although these algorithms rely on the use of particular representations of the policies, especially chosen in order to fit each of the three problems. Finally, we exhibit that the combination of FPF and MRE allows to find nearly optimal solutions in problems where $ε$-greedy approaches would fail.
A Unit Selection Methodology for Music Generation Using Deep Neural Networks
Venue: arXiv cs.SD 2016
Year: 2016

Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. We do this by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forced-choiced ranking task. We compare our model to a note-level generative baseline that consists of a stacked LSTM trained to predict forward by one note.
DeepMind Lab
Venue: arXiv cs.AI 2016
Year: 2016

DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.
Double Successive Rough Set Approximations
Venue: arXiv cs.LO 2016
Year: 2016

We examine double successive approximations on a set, which we denote by $L_2L_1, \ U_2U_1, U_2L_1,$ $L_2U_1$ where $L_1, U_1$ and $L_2, U_2$ are based on generally non-equivalent equivalence relations $E_1$ and $E_2$ respectively, on a finite non-empty set $V.$ We consider the case of these operators being given fully defined on its powerset $\mathscr{P}(V).$ Then, we investigate if we can reconstruct the equivalence relations which they may be based on. Directly related to this, is the question of whether there are unique solutions for a given defined operator and the existence of conditions which may characterise this. We find and prove these characterising conditions that equivalence relation pairs should satisfy in order to generate unique such operators.
Unified Framework for the Effective Rate Analysis of Wireless Communication Systems over MISO Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple inputs single output (MISO) fading channels, which uses moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared to the probability density function (PDF) based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox's H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma and generalized K fading channels, are readily available. In addition, asymptotic approximations are provided for the proposed H transform and MGF based approach as well as for the effective rate over i.n.i.d. MISO hyper Fox's H fading channels. Simulations under various fading scenarios are also presented, which support the validity of the proposed method.
Autoencoder-based holographic image restoration
Venue: arXiv cs.CV 2016
Year: 2016

We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and QR codes by using the proposed method.
DizzyRNN: Reparameterizing Recurrent Neural Networks for Norm-Preserving Backpropagation
Venue: arXiv cs.LG 2016
Year: 2016

The vanishing and exploding gradient problems are well-studied obstacles that make it difficult for recurrent neural networks to learn long-term time dependencies. We propose a reparameterization of standard recurrent neural networks to update linear transformations in a provably norm-preserving way through Givens rotations. Additionally, we use the absolute value function as an element-wise non-linearity to preserve the norm of backpropagated signals over the entire network. We show that this reparameterization reduces the number of parameters and maintains the same algorithmic complexity as a standard recurrent neural network, while outperforming standard recurrent neural networks with orthogonal initializations and Long Short-Term Memory networks on the copy problem.
Keeping Continuous Deliveries Safe
Venue: arXiv cs.SE 2016
Year: 2016

Allowing swift release cycles, Continuous Delivery has become popular in application software development and is starting to be applied in safety-critical domains such as the automotive industry. These domains require thorough analysis regarding safety constraints, which can be achieved by formal verification and the execution of safety tests resulting from a safety analysis on the product. With continuous delivery in place, such tests need to be executed with every build to ensure the latest software still fulfills all safety requirements. Even more though, the safety analysis has to be updated with every change to ensure the safety test suite is still up-to-date. We thus propose that a safety analysis should be treated no differently from other deliverables such as source-code and dependencies, formulate guidelines on how to achieve this and advert areas where future research is needed.
Copycat: A High Precision Real Time NAND Simulator
Venue: arXiv cs.AR 2016
Year: 2016

In this paper, we describe the design and implementation of a high precision real time NAND simulator called Copycat that runs on a commodity multi-core desktop environment. This NAND simulator facilitates the development of embedded flash memory management software such as the flash translation layer (FTL). The simulator also allows a comprehensive fault injection for testing the reliability of the FTL. Compared against a real FPGA implementation, the simulator's response time deviation is under 0.28% on average, with a maximum of 10.12%.
Spatial multi-LRU: Distributed Caching for Wireless Networks with Coverage Overlaps
Venue: arXiv cs.NI 2016
Year: 2016

This article introduces a novel family of decentralised caching policies, applicable to wireless networks with finite storage at the edge-nodes (stations). These policies, that are based on the Least-Recently-Used replacement principle, are here referred to as spatial multi-LRU. They update cache inventories in a way that provides content diversity to users who are covered by, and thus have access to, more than one station. Two variations are proposed, the multi-LRU-One and -All, which differ in the number of replicas inserted in the involved caches. We analyse their performance under two types of traffic demand, the Independent Reference Model (IRM) and a model that exhibits temporal locality. For IRM, we propose a Che-like approximation to predict the hit probability, which gives very accurate results. Numerical evaluations show that the performance of multi-LRU increases the more the multi-coverage areas increase, and it is close to the performance of centralised policies, when multi-coverage is sufficient. For IRM traffic, multi-LRU-One is preferable to multi-LRU-All, whereas when the traffic exhibits temporal locality the -All variation can perform better. Both variations outperform the simple LRU. When popularity knowledge is not accurate, the new policies can perform better than centralised ones.
Improving Neural Language Models with a Continuous Cache
Venue: arXiv cs.CL 2016
Year: 2016

We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks.
Millimeter Wave Communications with OAM-SM Scheme for Future Mobile Networks
Venue: arXiv cs.NI 2016
Year: 2016

The orbital angular momentum (OAM) technique provides a new degree of freedom for information transmissions in millimeter wave communications. Considering the spatial distribution characteristics of OAM beams, a new OAM spatial modulation (OAM-SM) millimeter wave communication system is first proposed for future mobile networks. Furthermore, the capacity, average bit error probability and energy efficiency of OAM-SM millimeter wave communication systems are analytically derived for performance analysis. Compared with the conventional multi-input multi-output (MIMO) millimeter wave communication systems, the maximum capacity and energy efficiency of OAM-SM millimeter wave communication systems are improved by 36% and 472.3%, respectively. Moreover, numerical results indicate that the proposed OAM-SM millimeter wave communication systems are more robust to path-loss attenuations than the conventional MIMO millimeter wave communication systems, which makes it suitable for long-range transmissions. Therefore, OAM-SM millimeter wave communication systems provide a great growth space for future mobile networks.
Web-based Argumentation
Venue: arXiv cs.AI 2016
Year: 2016

Assumption-Based Argumentation (ABA) is an argumentation framework that has been proposed in the late 20th century. Since then, there was still no solver implemented in a programming language which is easy to setup and no solver have been interfaced to the web, which impedes the interests of the public. This project aims to implement an ABA solver in a modern programming language that performs reasonably well and interface it to the web for easier access by the public. This project has demonstrated the novelty of development of an ABA solver, that computes conflict-free, stable, admissible, grounded, ideal, and complete semantics, in Python programming language which can be used via an easy-to-use web interface for visualization of the argument and dispute trees. Experiments were conducted to determine the project's best configurations and to compare this project with proxdd, a state-of-the-art ABA solver, which has no web interface and computes less number of semantics. From the results of the experiments, this project's best configuration is achieved by utilizing "pickle" technique and tree caching technique. Using this project's best configuration, this project achieved a lower average runtime compared to proxdd. On other aspect, this project encountered more cases with exceptions compared to proxdd, which might be caused by this project computing more semantics and hence requires more resources to do so. Hence, it can be said that this project run comparably well to the state-of-the-art ABA solver proxdd. Future works of this project include computational complexity analysis and efficiency analysis of algorithms implemented, implementation of more semantics in argumentation framework, and usability testing of the web interface.
Smart Contract Templates: essential requirements and design options
Venue: arXiv cs.CY 2016
Year: 2016

Smart Contract Templates support legally-enforceable smart contracts, using operational parameters to connect legal agreements to standardised code. In this paper, we explore the design landscape of potential formats for storage and transmission of smart legal agreements. We identify essential requirements and describe a number of key design options, from which we envisage future development of standardised formats for defining and manipulating smart legal agreements. This provides a preliminary step towards supporting industry adoption of legally-enforceable smart contracts.
New few weight codes from trace codes over a local Ring
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, new few weights linear codes over the local ring $R=\mathbb{F}_p+u\mathbb{F}_p+v\mathbb{F}_p+uv\mathbb{F}_p,$ with $u^2=v^2=0, uv=vu,$ are constructed by using the trace function defined over an extension ring of degree $m.$ %In fact, These codes are punctured from the linear code is defined in \cite{SWLP} up to coordinate permutations. These trace codes have the algebraic structure of abelian codes. Their weight distributions are evaluated explicitly by means of Gaussian sums over finite fields. Two different defining sets are explored. Using a linear Gray map from $R$ to $\mathbb{F}_p^4,$ we obtain several families of new $p$-ary codes from trace codes of dimension $4m$. For the first defining set: when $m$ is even, or $m$ is odd and $p\equiv3 ~({\rm mod} ~4),$ we obtain a new family of two-weight codes, which are shown to be optimal by the application of the Griesmer bound; when $m$ is even and under some special conditions, we obtain two new classes of three-weight codes. For the second defining set: we obtain a new class of two-weight codes and prove that it meets the Griesmer bound. In addition, we give the minimum distance of the dual code. Finally, applications of the $p$-ary image codes in secret sharing schemes are presented.
A Lazy Language Needs a Lazy Type System: Introducing Polymorphic Contexts
Venue: arXiv cs.PL 2016
Year: 2016

Most type systems that support polymorphic functions are based on a version of System-F. We argue that this limits useful programming paradigms for languages with lazy evaluation. We motivate an extension of System-F alleviating this limitation. First, using a sequence of examples, we show that for lazily evaluated languages current type systems may force one to write a program in an unnatural way; we in particular argue that in such languages the relationship between polymorphic and existential types can be made more systematic by allowing to pass back (part of) an existential result of a function call as an argument to the the function call that produced that value. After presenting our extension to System-F we show how we can implement the strict-state thread monad $\mathrm{ST}$ by using a returned existential type in specialising the polymorphic function which returns that type. Currently this monad is built-in into the runtime system of GHC and as such has become part of the language. Our proposed language extension, i.e. the introduction of polymorphic contexts, reverses the relationship between the context of a function call and the called function with respect to where it is decided with which type to instantiate a type variable.
Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization
Venue: arXiv cs.NE 2016
Year: 2016

It is believed that hippocampus functions as a memory allocator in brain, the mechanism of which remains unrevealed. In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set of activated neuroids with stable size. Valiant proposed three requirements for the hippocampal circuit to become a stable memory allocator (SMA): stability, continuity and orthogonality. The functionality of SMA in hippocampus is essential in further computation within cortex, according to Valiant's model. In this paper, we put these requirements for memorization functions into rigorous mathematical formulation and introduce the concept of capacity, based on the probability of erroneous allocation. We prove fundamental limits for the capacity and error probability of SMA, in both data-independent and data-dependent settings. We also establish an example of stable memory allocator that can be implemented via neuroidal circuits. Both theoretical bounds and simulation results show that the neural SMA functions well.
Lightweight compression with encryption based on Asymmetric Numeral Systems
Venue: arXiv cs.IT 2016
Year: 2016

Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses Asymmetric Numeral Systems -- an innovative approach to entropy coding which can be used for compression with encryption. It provides compression ratio comparable with arithmetic coding at similar speed as Huffman coding, hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the Asymmetric Numeral System makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing additional encryption layer if reaching a satisfactory protection level.
Distributed area coverage control with imprecise robot localization: Simulation and experimental studies
Venue: arXiv cs.SY 2016
Year: 2016

This article examines the area coverage problem for a network of mobile robots with imprecise agents' localization. Each robot has uniform radial sensing ability, governed by first order kinodynamics. The convex-space is partitioned based on the Guaranteed Voronoi (GV) principle and each robot's area of responsibility corresponds to its GV-cell, bounded by hyperbolic arcs. The proposed control law is distributed, demanding the positioning information about its GV-Delaunay neighbors. Simulation and experimental studies are offered to highlight the efficiency of the proposed control law.
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network
Venue: arXiv cs.CV 2016
Year: 2016

In this article, we propose a super-resolution method to resolve the problem of image low spatial because of the limitation of imaging devices. We make use of the strong non-linearity mapped ability of the back-propagation neural networks(BPNN). Training sample images are got by undersampled method. The elements chose as the inputs of the BPNN are pixels referred to Non-local means(NL-Means). Making use of the self-similarity of the images, those inputs are the pixels which are pixels gained from modified NL-means which is specific for super-resolution. Besides, small change on core function of NL-means has been applied in the method we use in this article so that we can have a clearer edge in the shrunk image. Experimental results gained from the Peak Signal to Noise Ratio(PSNR) and the Equivalent Number of Look(ENL), indicate that adding the similar pixels as inputs will increase the results than not taking them into consideration.