OpenRaiser/Intern-Atlas huggingface dataset:
SCMA for Downlink Multiple Access of 5G Wireless Networks
Venue: arXiv cs.IT 2014
Year: 2014

Sparse code multiple access (SCMA) is a new frequency domain non-orthogonal multiple-access technique which can improve spectral efficiency of wireless radio access. With SCMA, different incoming data streams are directly mapped to codewords of different multi-dimensional cookbooks, where each codeword represents a spread transmission layer. Multiple SCMA layers share the same time-frequency resources of OFDMA. The sparsity of codewords makes the near-optimal detection feasible through iterative message passing algorithm (MPA). Such low complexity of multi-layer detection allows excessive codeword overloading in which the dimension of multiplexed layers exceeds the dimension of codewords. Optimization of overloading factor along with modulation-coding levels of layers provides a more flexible and efficient link-adaptation mechanism. On the other hand, the signal spreading feature of SCMA can improve link-adaptation as a result of less colored interference. In this paper a technique is developed to enable multi-user SCMA (MU-SCMA) for downlink wireless access. User pairing, power sharing, rate adjustment, and scheduling algorithms are designed to improve the downlink throughput of a heavily loaded network. The advantage of SCMA spreading for lightly loaded networks is also evaluated.
A Formal Analysis of Required Cooperation in Multi-agent Planning
Venue: arXiv cs.AI 2014
Year: 2014

Research on multi-agent planning has been popular in recent years. While previous research has been motivated by the understanding that, through cooperation, multi-agent systems can achieve tasks that are unachievable by single-agent systems, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multi-agent systems. In this paper, we provide such a formal discussion from the planning aspect. We first show that determining whether there is required cooperation (RC) is intractable is general. Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes. We establish that when none of these identified conditions hold, the problem is single-agent solvable. Furthermore, with a few assumptions, we provide an upper bound on the minimum number of agents required for RC problems with homogeneous agents. This study not only provides new insights into multi-agent planning, but also has many applications. For example, in human-robot teaming, when a robot cannot achieve a task, it may be due to RC. In such cases, the human teammate should be informed and, consequently, coordinate with other available robots for a solution.
The Complexity of Ergodic Mean-payoff Games
Venue: arXiv cs.GT 2014
Year: 2014

We study two-player (zero-sum) concurrent mean-payoff games played on a finite-state graph. We focus on the important sub-class of ergodic games where all states are visited infinitely often with probability 1. The algorithmic study of ergodic games was initiated in a seminal work of Hoffman and Karp in 1966, but all basic complexity questions have remained unresolved. Our main results for ergodic games are as follows: We establish (1) an optimal exponential bound on the patience of stationary strategies (where patience of a distribution is the inverse of the smallest positive probability and represents a complexity measure of a stationary strategy); (2) the approximation problem lie in FNP; (3) the approximation problem is at least as hard as the decision problem for simple stochastic games (for which NP intersection coNP is the long-standing best known bound). We present a variant of the strategy-iteration algorithm by Hoffman and Karp; show that both our algorithm and the classical value-iteration algorithm can approximate the value in exponential time; and identify a subclass where the value-iteration algorithm is a FPTAS. We also show that the exact value can be expressed in the existential theory of the reals, and establish square-root sum hardness for a related class of games.
Codynamic Fitness Landscapes of Coevolutionary Minimal Substrates
Venue: arXiv cs.NE 2014
Year: 2014

Coevolutionary minimal substrates are simple and abstract models that allow studying the relationships and codynamics between objective and subjective fitness. Using these models an approach is presented for defining and analyzing fitness landscapes of coevolutionary problems. We devise similarity measures of codynamic fitness landscapes and experimentally study minimal substrates of test--based and compositional problems for both cooperative and competitive interaction.
Using Triangles to Improve Community Detection in Directed Networks
Venue: arXiv cs.SI 2014
Year: 2014

In a graph, a community may be loosely defined as a group of nodes that are more closely connected to one another than to the rest of the graph. While there are a variety of metrics that can be used to specify the quality of a given community, one common theme is that flows tend to stay within communities. Hence, we expect cycles to play an important role in community detection. For undirected graphs, the importance of triangles -- an undirected 3-cycle -- has been known for a long time and can be used to improve community detection. In directed graphs, the situation is more nuanced. The smallest cycle is simply two nodes with a reciprocal connection, and using information about reciprocation has proven to improve community detection. Our new idea is based on the four types of directed triangles that contain cycles. To identify communities in directed networks, then, we propose an undirected edge-weighting scheme based on the type of the directed triangles in which edges are involved. We also propose a new metric on quality of the communities that is based on the number of 3-cycles that are split across communities. To demonstrate the impact of our new weighting, we use the standard METIS graph partitioning tool to determine communities and show experimentally that the resulting communities result in fewer 3-cycles being cut. The magnitude of the effect varies between a 10 and 50% reduction, and we also find evidence that this weighting scheme improves a task where plausible ground-truth communities are known.
A Compressive Multi-Mode Superresolution Display
Venue: arXiv cs.ET 2014
Year: 2014

Compressive displays are an emerging technology exploring the co-design of new optical device configurations and compressive computation. Previously, research has shown how to improve the dynamic range of displays and facilitate high-quality light field or glasses-free 3D image synthesis. In this paper, we introduce a new multi-mode compressive display architecture that supports switching between 3D and high dynamic range (HDR) modes as well as a new super-resolution mode. The proposed hardware consists of readily-available components and is driven by a novel splitting algorithm that computes the pixel states from a target high-resolution image. In effect, the display pixels present a compressed representation of the target image that is perceived as a single, high resolution image.
Secure MIMO Communications under Quantized Channel Feedback in the presence of Jamming
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of secure communications in a MIMO setting in the presence of an adversarial jammer equipped with $n_j$ transmit antennas and an eavesdropper equipped with $n_e$ receive antennas. A multiantenna transmitter, equipped with $n_t$ antennas, desires to secretly communicate a message to a multiantenna receiver equipped with $n_r$ antennas. We propose a transmission method based on artificial noise and linear precoding and a two-stage receiver method employing beamforming. Under this strategy, we first characterize the achievable secrecy rates of communication and prove that the achievable secure degrees-of-freedom (SDoF) is given by $d_s = n_r - n_j$ in the perfect channel state information (CSI) case. Second, we consider quantized CSI feedback using Grassmannian quantization of a function of the direct channel matrix and derive sufficient conditions for the quantization bit rate scaling as a function of transmit power for maintaining the achievable SDoF $d_s$ with perfect CSI and for having asymptotically zero secrecy rate loss due to quantization. Numerical simulations are also provided to support the theory.
One weird trick for parallelizing convolutional neural networks
Venue: arXiv cs.NE 2014
Year: 2014

I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
Venue: arXiv cs.AI 2014
Year: 2014

Data clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important clustering technique based on fuzzy logic. Also we have some hard clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy clustering algorithm and hard clustering algorithm
Ordered Navigation on Multi-attributed Data Words
Venue: arXiv cs.LO 2014
Year: 2014

We study temporal logics and automata on multi-attributed data words. Recently, BD-LTL was introduced as a temporal logic on data words extending LTL by navigation along positions of single data values. As allowing for navigation wrt. tuples of data values renders the logic undecidable, we introduce ND-LTL, an extension of BD-LTL by a restricted form of tuple-navigation. While complete ND-LTL is still undecidable, the two natural fragments allowing for either future or past navigation along data values are shown to be Ackermann-hard, yet decidability is obtained by reduction to nested multi-counter systems. To this end, we introduce and study nested variants of data automata as an intermediate model simplifying the constructions. To complement these results we show that imposing the same restrictions on BD-LTL yields two 2ExpSpace-complete fragments while satisfiability for the full logic is known to be as hard as reachability in Petri nets.
Rough Clustering Based Unsupervised Image Change Detection
Venue: arXiv cs.CV 2014
Year: 2014

This paper introduces an unsupervised technique to detect the changed region of multitemporal images on a same reference plane with the help of rough clustering. The proposed technique is a soft-computing approach, based on the concept of rough set with rough clustering and Pawlak's accuracy. It is less noisy and avoids pre-deterministic knowledge about the distribution of the changed and unchanged regions. To show the effectiveness, the proposed technique is compared with some other approaches.
An Approximate "Law of One Price" in Random Assignment Games
Venue: arXiv cs.GT 2014
Year: 2014

Assignment games represent a tractable yet versatile model of two-sided markets with transfers. We study the likely properties of the core of randomly generated assignment games. If the joint productivities of every firm and worker are i.i.d bounded random variables, then with high probability all workers are paid roughly equal wages, and all firms make similar profits. This implies that core allocations vary significantly in balanced markets, but that there is core convergence in even slightly unbalanced markets. For the benchmark case of uniform distribution, we provide a tight bound for the workers' share of the surplus under the firm-optimal core allocation. We present simulation results suggesting that the phenomena analyzed appear even in medium-sized markets. Finally, we briefly discuss the effects of unbounded distributions and the ways in which they may affect wage dispersion.
Cognitive Radio Networks with Probabilistic Relaying: Stable Throughput and Delay Tradeoffs
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we study and analyze fundamental throughput and delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. Moreover, admission control is introduced at the relay queue, whereby a PU's packet is admitted to the relay queue with an admission probability. The proposed policy introduces a fundamental tradeoff between the delays of the PU and SU. Consequently, it opens room for trading the PU delay for enhanced SU delay and vice versa. Thus, the system could be tuned according to the demands of the intended application. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. The effect of varying the service and admission probabilities on the system's throughput and delay is thoroughly investigated. Results show that cooperation expands the stable throughput region. Moreover, numerical simulation results assert the extreme accuracy of the analytically derived delay expressions. In addition, we provide a criterion for the SU based on which it decides whether cooperation is beneficial to the PU or not. Furthermore, we show the impact of controlling the flow of data at the relay queue using the admission probability.
A Parallel Task-based Approach to Linear Algebra
Venue: arXiv cs.DC 2014
Year: 2014

Processors with large numbers of cores are becoming commonplace. In order to take advantage of the available resources in these systems, the programming paradigm has to move towards increased parallelism. However, increasing the level of concurrency in the program does not necessarily lead to better performance. Parallel programming models have to provide flexible ways of defining parallel tasks and at the same time, efficiently managing the created tasks. OpenMP is a widely accepted programming model for shared-memory architectures. In this paper we highlight some of the drawbacks in the OpenMP tasking approach, and propose an alternative model based on the Glasgow Parallel Reduction Machine (GPRM) programming framework. As the main focus of this study, we deploy our model to solve a fundamental linear algebra problem, LU factorisation of sparse matrices. We have used the SparseLU benchmark from the BOTS benchmark suite, and compared the results obtained from our model to those of the OpenMP tasking approach. The TILEPro64 system has been used to run the experiments. The results are very promising, not only because of the performance improvement for this particular problem, but also because they verify the task management efficiency, stability, and flexibility of our model, which can be applied to solve problems in future many-core systems.
Synthesising Succinct Strategies in Safety Games
Venue: arXiv cs.LO 2014
Year: 2014

Finite turn-based safety games have been used for very different problems such as the synthesis of linear temporal logic (LTL), the synthesis of schedulers for computer systems running on multiprocessor platforms, and also for the determinisation of timed automata. In these contexts, games are implicitly defined, and their size is at least exponential in the size of the input. Nevertheless, there are natural relations between states of arenas of such games. We first formalise the properties that we expect on the relation between states, thanks to the notion of alternating simulation. Then, we show how such simulations can be exploited to (1) improve the running time of the OTFUR algorithm to compute winning strategies and (2) obtain a succinct representation of a winning strategy. We also show that our general theory applies to the three applications mentioned above.
Determination of the functional state of the fruits by parameters of the electric impedance
Venue: arXiv cs.SY 2014
Year: 2014

Introduction. To assess the freshness of various products are often used measuring impedance module. But due to the structure of plant foods diagnostic value should have exactly a complex component of impedance. Article tasked with developing criteria for assessing the functional state of the products subject to a comprehensive component of the impedance. Research methodology. To determine the functional status of the fruit were measured module and phase of impedance at the three frequencies of 20, 100 and 500 kHz. Criteria for recognition of functional status determined by the dynamics of changes in the parameters of the complex impedance due to destructive processes caused by dehydration and putrefaction processes. Data processing and analysis. On the basis of experimental data obtained at three frequencies modeled frequency and phase response and their changes during losing of freshness and appearance of destructive processes. Discussion and conclusions. In fresh and stale fruit modulus and phase of the impedance at low and high frequencies have characteristic differences. But this is especially evident on the phase-frequency characteristic, which can be seen that the value of the phase with the loss of freshness at low frequency decreases and increases at high more than twice during one week. Therefore, to assess the functional state of fresh and stale products we suggest use phase portraits of phase response.
Scalable Similarity Learning using Large Margin Neighborhood Embedding
Venue: arXiv cs.CV 2014
Year: 2014

Classifying large-scale image data into object categories is an important problem that has received increasing research attention. Given the huge amount of data, non-parametric approaches such as nearest neighbor classifiers have shown promising results, especially when they are underpinned by a learned distance or similarity measurement. Although metric learning has been well studied in the past decades, most existing algorithms are impractical to handle large-scale data sets. In this paper, we present an image similarity learning method that can scale well in both the number of images and the dimensionality of image descriptors. To this end, similarity comparison is restricted to each sample's local neighbors and a discriminative similarity measure is induced from large margin neighborhood embedding. We also exploit the ensemble of projections so that high-dimensional features can be processed in a set of lower-dimensional subspaces in parallel without much performance compromise. The similarity function is learned online using a stochastic gradient descent algorithm in which the triplet sampling strategy is customized for quick convergence of classification performance. The effectiveness of our proposed model is validated on several data sets with scales varying from tens of thousands to one million images. Recognition accuracies competitive with the state-of-the-art performance are achieved with much higher efficiency and scalability.
A DoF-Optimal Scheme for the two-user X-channel with Synergistic Alternating CSIT
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, the degrees of freedom (DoF) of the two-user single input single output (SISO) X-channel are investigated. Three cases are considered for the availability of channel state information at the transmitters (CSIT); perfect, delayed, and no-CSIT. A new achievable scheme is proposed to elucidate the potency of interference creation-resurrection (IRC) when the available CSIT alternates between these three cases. For some patterns of alternating CSIT, the proposed scheme achieves $4/3$ DoF, and hence, coincides with the information theoretic upper bound on the DoF of the two-user X-channel with perfect and instantaneous CSIT. The CSIT alternation patterns are investigated where the patterns that provide extraordinary synergistic gain and dissociative ones are identified.
Bloscpack: a compressed lightweight serialization format for numerical data
Venue: arXiv cs.MS 2014
Year: 2014

This paper introduces the Bloscpack file format and the accompanying Python reference implementation. Bloscpack is a lightweight, compressed binary file-format based on the Blosc codec and is designed for lightweight, fast serialization of numerical data. This article presents the features of the file-format and some some API aspects of the reference implementation, in particular the ability to handle Numpy ndarrays. Furthermore, in order to demonstrate its utility, the format is compared both feature- and performance-wise to a few alternative lightweight serialization solutions for Numpy ndarrays. The performance comparisons take the form of some comprehensive benchmarks over a range of different artificial datasets with varying size and complexity, the results of which are presented as the last section of this article.
CATOS: Computer Aided Training/Observing System
Venue: arXiv cs.CE 2014
Year: 2014

In animal behavioral biology, there are several cases in which an autonomous observing/training system would be useful. 1) Observation of certain species continuously, or for documenting specific events, which happen irregularly; 2) Longterm intensive training of animals in preparation for behavioral experiments; and 3) Training and testing of animals without human interference, to eliminate potential cues and biases induced by humans. The primary goal of this study is to build a system named CATOS (Computer Aided Training/Observing System) that could be used in the above situations. As a proof of concept, the system was built and tested in a pilot experiment, in which cats were trained to press three buttons differently in response to three different sounds (human speech) to receive food rewards. The system was built in use for about 6 months, successfully training two cats. One cat learned to press a particular button, out of three buttons, to obtain the food reward with over 70 percent correctness.
FO(C) and Related Modelling Paradigms
Venue: arXiv cs.LO 2014
Year: 2014

Recently, C-Log was introduced as a language for modelling causal processes. Its formal semantics has been defined together with introductory examples, but the study of this language is far from finished. In this paper, we compare C-Log to other declarative modelling languages. More specifically, we compare to first-order logic (FO), and argue that C-Log and FO are orthogonal and that their integration, FO(C), is a knowledge representation language that allows for clear and succinct models. We compare FO(C) to E-disjunctive logic programming with the stable semantics, and define a fragment on which both semantics coincide. Furthermore, we discuss object-creation in FO(C), relating it to mathematics, business rules systems, and data base systems.
A Hybrid Graph Representation for Exact Graph Algorithms
Venue: arXiv cs.DS 2014
Year: 2014

Many exact search algorithms for NP-hard graph problems adopt the old Davis-Putman branch-and-reduce paradigm. The performance of these algorithms often suffers from the increasing number of graph modifications, such as vertex/edge deletions, that reduce the problem instance and have to be "taken back" frequently during the search process. We investigate practical implementation-based aspects of exact graph algorithms by providing a simple hybrid graph representation that trades space for time to address the said take-back challenge. Experiments on three well studied problems show consistent significant improvements over classical methods.