OpenRaiser/Intern-Atlas huggingface dataset:
Degrees of Freedom of the MIMO 2x2 Interference Network with General Message Sets
Venue: arXiv cs.IT 2016
Year: 2016

We establish the DoF region for the MIMO 2x2 interference network with a general message set, consisting of nine messages, one for each pair of a subset of transmitters at which that message is known and a subset of receivers where that message is desired. An outer bound on the general nine-message network is obtained and then it is shown to be tight, establishing the DoF region for the most general antenna setting wherein all four nodes have an arbitrary number of antennas each. The DoF-optimal scheme is applicable to the MIMO 2x2 network with constant channel coefficients, and hence, a fortiori, to time/frequency varying channel scenarios. In particular, a linear precoding scheme is proposed that can achieve all the DoF tuples in the DoF region. In it, the precise roles played by transmit zero-forcing, interference alignment, random beamforming, symbol extensions and asymmetric complex signaling are delineated. For instance, we identify a class of antenna settings in which ACS is required to achieve the fractional-valued corner points. Evidently, the DoF regions of all previously unknown cases of the 2x2 interference network with a subset of the nine-messages are established as special cases of the general result of this paper. In particular, the DoF region of the well-known four-message (and even three-message) MIMO X channel is established. This problem had remained open despite previous studies which had found inner and outer bounds that were not tight in general. Hence, the DoF regions of all special cases obtained from the general DoF region of the nine-message 2x2 interference network of this work that include at least three of the four X channel messages are new, among many others. Our work sheds light on how the same physical 2x2 network could be used by a suitable choice of message sets to take most advantage of the channel resource in a flexible and efficient manner.
Capacity of Systems with Queue-Length Dependent Service Quality
Venue: arXiv cs.IT 2016
Year: 2016

We study the information-theoretic limit of reliable information processing by a server with queue-length dependent quality of service. We define the capacity for such a system as the number of bits reliably processed per unit time, and characterize it in terms of queuing system parameters. We also characterize the distributions of the arrival and service processes that maximize and minimize the capacity of such systems in a discrete-time setting. For arrival processes with at most one arrival per time slot, we observed a minimum around the memoryless distribution. We also studied the case of multiple arrivals per time slot, and observed that burstiness in arrival has adverse effects on the system. The problem is theoretically motivated by an effort to incorporate the notion of reliability in queueing systems, and is applicable in the contexts of crowdsourcing, multimedia communication, and stream computing.
Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture
Venue: arXiv cs.CV 2016
Year: 2016

Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive alternative to address this challenge. However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification. Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability. We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests.
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or using existing solutions for semantic segmentation. The result is a content-aware generative algorithm that offers meaningful control over the outcome. Thus, we increase the quality of images generated by avoiding common glitches, make the results look significantly more plausible, and extend the functional range of these algorithms---whether for portraits or landscapes, etc. Applications include semantic style transfer and turning doodles with few colors into masterful paintings!
On the Number of Cycles in a Graph
Venue: arXiv cs.DM 2016
Year: 2016

There is a sizable literature on investigating the minimum and maximum numbers of cycles in a class of graphs. However, the answer is known only for special classes. This paper presents a result on the smallest number of cycles in hamiltonian 3-connected cubic graphs. Further, it describes a proof technique that could improve an upper bound of the largest number of cycles in a hamiltonian graph.
Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)
Venue: arXiv cs.CL 2016
Year: 2016

An extended, revised form of Tim Buckwalter's Arabic lexical and morphological resource AraMorph, eXtended Revised AraMorph (henceforth XRAM), is presented which addresses a number of weaknesses and inconsistencies of the original model by allowing a wider coverage of real-world Classical and contemporary (both formal and informal) Arabic texts. Building upon previous research, XRAM enhancements include (i) flag-selectable usage markers, (ii) probabilistic mildly context-sensitive POS tagging, filtering, disambiguation and ranking of alternative morphological analyses, (iii) semi-automatic increment of lexical coverage through extraction of lexical and morphological information from existing lexical resources. Testing of XRAM through a front-end Python module showed a remarkable success level.
Generalization error bounds for learning to rank: Does the length of document lists matter?
Venue: arXiv cs.LG 2016
Year: 2016

We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is \emph{no} degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under $\ell_1$ regularization and faster convergence rates if the loss function is smooth.
Physical Layer Security for Massive MIMO Systems Impaired by Phase Noise
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate the impact of phase noise on the secrecy performance of downlink massive MIMO systems in the presence of a passive multiple-antenna eavesdropper. Thereby, for the base station (BS) and the legitimate users, the effect of multiplicative phase noise is taken into account, whereas the eavesdropper is assumed to employ ideal hardware. We derive a lower bound for the ergodic secrecy rate of a given user when matched filter data precoding and artificial noise transmission are employed at the BS. Based on the derived analytical expression, we investigate the impact of the various system parameters on the secrecy rate. Our analytical and simulation results reveal that distributively deployed local oscillators (LOs) can achieve a better performance than one common LO for all BS antennas as long as a sufficient amount of power is assigned for data transmission.
PageRank Pipeline Benchmark: Proposal for a Holistic System Benchmark for Big-Data Platforms
Venue: arXiv cs.PF 2016
Year: 2016

The rise of big data systems has created a need for benchmarks to measure and compare the capabilities of these systems. Big data benchmarks present unique scalability challenges. The supercomputing community has wrestled with these challenges for decades and developed methodologies for creating rigorous scalable benchmarks (e.g., HPC Challenge). The proposed PageRank pipeline benchmark employs supercomputing benchmarking methodologies to create a scalable benchmark that is reflective of many real-world big data processing systems. The PageRank pipeline benchmark builds on existing prior scalable benchmarks (Graph500, Sort, and PageRank) to create a holistic benchmark with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. The linear algebraic nature of PageRank makes it well suited to being implemented using the GraphBLAS standard. The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed PageRank pipeline benchmark is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance has been measured.
Bounds on the Voter Model in Dynamic Networks
Venue: arXiv cs.SI 2016
Year: 2016

In the voter model, each node of a graph has an opinion, and in every round each node chooses independently a random neighbour and adopts its opinion. We are interested in the consensus time, which is the first point in time where all nodes have the same opinion. We consider dynamic graphs in which the edges are rewired in every round (by an adversary) giving rise to the graph sequence $G_1, G_2, \dots $, where we assume that $G_i$ has conductance at least $ϕ_i$. We assume that the degrees of nodes don't change over time as one can show that the consensus time can become super-exponential otherwise. In the case of a sequence of $d$-regular graphs, we obtain asymptotically tight results. Even for some static graphs, such as the cycle, our results improve the state of the art. Here we show that the expected number of rounds until all nodes have the same opinion is bounded by $O(m/(d_{min} \cdot ϕ))$, for any graph with $m$ edges, conductance $ϕ$, and degrees at least $d_{min}$. In addition, we consider a biased dynamic voter model, where each opinion $i$ is associated with a probability $P_i$, and when a node chooses a neighbour with that opinion, it adopts opinion $i$ with probability $P_i$ (otherwise the node keeps its current opinion). We show for any regular dynamic graph, that if there is an $ε>0$ difference between the highest and second highest opinion probabilities, and at least $Ω(\log n)$ nodes have initially the opinion with the highest probability, then all nodes adopt w.h.p. that opinion. We obtain a bound on the convergences time, which becomes $O(\log n/ϕ)$ for static graphs.
Stochastic Collection and Replenishment (SCAR): Objective Functions
Venue: arXiv cs.RO 2016
Year: 2016

This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.
Identifying Randomly Activated Users via Sign-Compute-Resolve on Graphs
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we treat the problem of identification of a subset of active users in a set of a large number of potentially active users. The users from the subset are activated randomly, such that the access point (AP) does not know the subset or its size a priori. The active users are contending to report their activity to the AP over a multiple access channel. We devise a contention algorithm that assumes a combination of physical-layer network coding and K-out-of-N signature coding, allowing for multiple detection of up to K users at the access point. In addition, we rely on the principles of coded slotted ALOHA (CSA) and use of successive interference cancellation to enable subsequent resolution of the collisions that originally featured more than K users. The objective is to identify the subset of active users such that the target performance, e.g., probability of active user resolution and/or throughput is reached, which implies that the duration of the contention period is also not known a priori. In contrast to standard CSA approaches, in the proposed algorithm each user, active or not, has a predefined schedule of slots in which it sends its signature. We analyze the performance of the proposed algorithm both in the asymptotic and non-asymptotic settings. We also derive an estimator that, based on the observation of collision multiplicities, estimates how many users are active and thereby enables tuning of the length of the contention period.
Adaptive Visualisation System for Construction Building Information Models Using Saliency
Venue: arXiv cs.CV 2016
Year: 2016

Building Information Modeling (BIM) is a recent construction process based on a 3D model, containing every component related to the building achievement. Architects, structure engineers, method engineers, and others participant to the building process work on this model through the design-to-construction cycle. The high complexity and the large amount of information included in these models raise several issues, delaying its wide adoption in the industrial world. One of the most important is the visualization: professionals have difficulties to find out the relevant information for their job. Actual solutions suffer from two limitations: the BIM models information are processed manually and insignificant information are simply hidden, leading to inconsistencies in the building model. This paper describes a system relying on an ontological representation of the building information to label automatically the building elements. Depending on the user's department, the visualization is modified according to these labels by automatically adjusting the colors and image properties based on a saliency model. The proposed saliency model incorporates several adaptations to fit the specificities of architectural images.
Learning Shared Representations in Multi-task Reinforcement Learning
Venue: arXiv cs.AI 2016
Year: 2016

We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space. Since the environment does not change, there is potentially a lot of common ground amongst tasks and learning to solve them individually seems extremely wasteful. In this paper, we explicitly model and learn this shared structure as it arises in the state-action value space. We will show how one can jointly learn optimal value-functions by modifying the popular Value-Iteration and Policy-Iteration procedures to accommodate this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization. this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization.
A novel learning-based frame pooling method for Event Detection
Venue: arXiv cs.CV 2016
Year: 2016

Detecting complex events in a large video collection crawled from video websites is a challenging task. When applying directly good image-based feature representation, e.g., HOG, SIFT, to videos, we have to face the problem of how to pool multiple frame feature representations into one feature representation. In this paper, we propose a novel learning-based frame pooling method. We formulate the pooling weight learning as an optimization problem and thus our method can automatically learn the best pooling weight configuration for each specific event category. Experimental results conducted on TRECVID MED 2011 reveal that our method outperforms the commonly used average pooling and max pooling strategies on both high-level and low-level 2D image features.
Von Neumann Algebras form a Model for the Quantum Lambda Calculus
Venue: arXiv cs.LO 2016
Year: 2016

We present a model of Selinger and Valiron's quantum lambda calculus based on von Neumann algebras, and show that the model is adequate with respect to the operational semantics.
Wireless Information and Power Transfer in Full-Duplex Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the problem of maximizing the sum-rate for simultaneous wireless information and power transfer (SWIPT) in a full-duplex bi-directional communication system subject to energy harvesting and transmit power constraints at both nodes. We investigate the optimum design of the receive power splitters and transmit powers for SWIPT in full-duplex mode. Exploiting rate-split method, an iterative algorithm is derived to solve the non-convex problem. The effectiveness of the proposed algorithm is justified through numerical simulations.
In-Vehicle PLC: In-Car and In-Ship Channel Characterization
Venue: arXiv cs.OH 2016
Year: 2016

This paper deals with power line communication (PLC) in the context of in-vehicle data networks. This technology can provide high-speed data connectivity via the exploitation of the existing power network, with clear potential benefits in terms of cost and weight reduction. The focus is on two scenarios: an electric car and a cruise ship. An overview of the wiring infrastructure and the network topology in these two scenarios is provided. The main findings reported in the literature related to the channel characteristics are reported. Noise is also assessed with emphasis to the electric car context. Then, new results from the statistical analysis of measurements made in a compact electric car and in a large cruise ship are shown. The channel characteristics are analysed in terms of average channel gain, delay spread, coherence bandwidth and achievable transmission rate. Finally, an overall comparison is made, highlighting similarities and differences taking into account also the conventional (combustion engine) car and the largely investigated in-home scenario.
Optimal Link Adaptation for Multicarrier Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

Link adaptation is the terminology used to describe techniques that improve multicarrier communication systems performance by dynamically adapting the transmission parameters, i.e., transmit power and number of bits per subcarrier, to the changing quality of the wireless link. The research literature has focused on single objective optimization techniques to optimize the multicarrier communication systems performance, e.g., maximizing the throughput/capacity or minimizing the transmit power subject to a set of constraints. In this dissertation, we adopt a novel optimization concept, namely multiobjective optimization, where our objective is to simultaneously optimize the conflicting and incommensurable throughput and power objectives. More specifically, in some of the following Chapters, we propose novel algorithms that jointly maximize the multicarrier system throughput and minimize its total transmit power subject to quality-of-service, total transmit power, and maximum allocated bits per subcarrier constraints. The proposed algorithms require prior knowledge about the importance of the competing objective functions in terms of pre-determined weighting coefficients, or they can adapt the weighting coefficients during the solution process while meeting the constraints, in order to reduce the computational complexity. Simulation results show significant performance gains in terms of the achieved throughput and transmit power when compared to single optimization approaches, at the cost of no additional complexity.
Efficient Optimal Joint Channel Estimation and Data Detection for Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.
On Self-Reducibility and Reoptimization of Closest Substring Problem
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we define the reoptimization variant of the closest substring problem (CSP) under sequence addition. We show that, even with the additional information we have about the problem instance, the problem of finding a closest substring is still NP-hard. We investigate the combinatorial property of optimization problems called self-reducibility. We show that problems that are polynomial-time reducible to self-reducible problems also exhibits the same property. We illustrate this in the context of CSP. We used the property to show that although we cannot improve the approximability of the problem, we can improve the running time of the existing PTAS for CSP.