arXiv subjects

Kobi Cohen

Publications and source records attributed to Kobi Cohen.

At least 55 records · Page 3Linked to original sources

arXiv2018

Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.

cs.NI↗

arXiv2018

Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks

We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on a spectrum and energy efficient transmission scheme used to reduce the spectrum usage and energy consumption during the detection task. We propose a Spectrum and Energy Efficient Multiple Access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in SEEMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. SEEMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmission savings and narrowband transmission over MAC. Second, it can be implemented by simple dumb sensors (oblivious to observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish a finite sample analysis and an asymptotic analysis of the error probability with respect to the network size and provide system design conditions to obtain the exponential decay of the error. Specific performance analysis is developed for common non-i.i.d. observation scenarios, including local i.i.d. observations, and Markovian correlated observations. Numerical examples demonstrate SEEMA performance.

cs.IT↗

arXiv2018

Active Anomaly Detection in Heterogeneous Processes

An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. The computational complexity of the proposed test is also of a significantly lower order.

cs.IT↗

arXiv2018

Information-Directed Random Walk for Rare Event Detection in Hierarchical Processes

The problem of detecting a few anomalous processes among a large number of data streams is considered. At each time, aggregated observations can be taken from a chosen subset of the processes, where the chosen subset conforms to a given tree structure. The random observations are drawn from a general distribution that may depend on the size of the chosen subset and the number of anomalous processes in the subset. We propose a sequential search strategy by devising an information-directed random walk (IRW) on the tree-structured observation hierarchy. Subject to a reliability constraint, the proposed policy is shown to be asymptotically optimal with respect to the detection accuracy. Furthermore, it achieves the optimal logarithmic-order sample complexity with respect to the size of the search space provided that the Kullback-Leibler divergence between aggregated observations in the presence and the absence of anomalous processes are bounded away from zero at all levels of the tree structure as the size of the search space approaches infinity. Sufficient conditions on the decaying rate of the aggregated observations to pure noise under which a sublinear scaling in the size of the search space is preserved are also identified for the Bernoulli case.

cs.IT↗

arXiv2018

Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

In recent years there has been a sharp rise in networking applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. Examples include analyzing network traffic under zero-day attacks, and computer vision tasks by sensor networks deployed in the field. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. Our contribution is threefold. First, we develop a novel Deep Reinforcement One-shot Learning (DeROL) framework to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields. Third, we present an extensive experimental study using the OMNIGLOT dataset for computer vision tasks and the UNSW-NB15 dataset for intrusion detection tasks that demonstrates the versatility and efficiency of the DeROL framework.

cs.LG↗

arXiv2018

Attractive dipolar coupling between stacked exciton fluids

The interaction between aligned dipoles is long-ranged and highly anisotropic: it changes from repulsive to attractive depending on the relative positions of the dipoles. We report on the observation of the attractive component of the dipolar coupling between excitonic dipoles in stacked semiconductor bilayers. We show that the presence of a dipolar exciton fluid in one bilayer modifies the spatial distribution and increases the binding energy of excitonic dipoles in a vertically remote layer. The binding energy changes are explained by a many-body polaron model describing the deformation of the exciton cloud due to its interaction with a remote dipolar exciton. The results open the way for the observation of theoretically predicted new and exotic collective phases, the realization of interacting dipolar lattices in semiconductor systems as well as for engineering and sensing their collective excitations.

cond-mat.quant-gas↗

arXiv2017

Nanoscale Control over Optical Dislocations

High-order optical vortices are inherently unstable, as they tend to split up under perturbation to a series of vortices with unity charge. Control over the perturbation opens up a new degree of freedom to control and tune their location in a 2D-space, with immediate implications on trapping, light-matter interactions and super-resolution imaging. Here, we present a continuous nano-scale tuning of the spatial location of plasmonic vortices on metal-air interface achieved by varying the polarization state of the light coupled to the surface plasmons through a spiral slit. We demonstrate such a control at nano-scale resolution using phase-resolved near-field microscopy.

physics.optics↗

arXiv2017

Transition from spin-orbit to hyperfine dominated spin relaxation in a cold fluid of dipolar excitons

We measure the spin-resolved transport of dipolar excitons in a biased GaAs double quantum well structure. From these measurements we extract both spin lifetime and mobility of the excitons. We find that below a temperature of $4.8$K, there is a sharp increase in the spin lifetime of the excitons, together with a sharp reduction in their mobility. Below a critical power the spin lifetime increases with increasing mobility and density, while above the critical power the opposite trend is observed. We interpret this transition as an evidence of the interplay between two different spin dephasing mechanisms: at low mobility the dephasing is dominated by the hyperfine interaction with the lattice nuclei spins, while at higher mobility the spin-orbit interaction dominates, and a Dyakonov-Perel spin relaxation takes over. The excitation power and temperature regime where the hyperfine interaction induced spin dephasing is observed correlates with the regime where a dark dipolar quantum liquid was reported recently on a similar sample.

cond-mat.quant-gas↗

arXiv2017

Condensation to a strongly correlated dark fluid of two dimensional dipolar excitons

Recently we reported on the condensation of cold, electrostatically trapped dipolar excitons in GaAs bilayer heterostructure into a new, dense and dark collective phase. Here we analyze and discuss in detail the experimental findings and the emerging evident properties of this collective liquid-like phase. We show that the phase transition is characterized by a sharp increase of the number of non-emitting dipoles, by a clear contraction of the fluid spatial extent into the bottom of the parabolic-like trap, and by spectral narrowing. We extract the total density of the condensed phase which we find to be consistent with the expected density regime of a quantum liquid. We show that there are clear critical temperature and excitation power onsets for the phase transition and that as the power further increases above the critical power, the strong darkening is reduced down until no clear darkening is observed. At this point another transition appears which we interpret as a transition to a strongly repulsive yet correlated $e$-$h$ plasma. Based on the experimental findings, we suggest that the physical mechanism that may be responsible for the transition is a dynamical final-state stimulation of the dipolar excitons to their dark spin states, which have a long lifetime and thus support the observed sharp increase in density. Further experiments and modeling will hopefully be able to unambiguously identify the physical mechanism behind these recent observations.

cond-mat.quant-gas↗

arXiv2016

Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks

We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.

cs.NI↗

arXiv2016

Radiative lifetimes of dipolar excitons in double quantum-wells

Spatially indirect excitons in semiconducting double quantum wells have been shown to exhibit rich collective many-body behavior that result from the nature of the extended dipole-dipole interactions between particles. For many spectroscopic studies of the emission from a system of such indirect excitons, it is crucial to separate the single particle properties of the excitons from the many-body effects arising from their mutual interactions. In particular, knowledge of the relation between the emission energy of indirect excitons and their radiative lifetime could be highly beneficial for control, manipulation, and analysis of such systems. Here we study a simple analytic approximate relation between the radiative lifetime of indirect excitons and their emission energy. We show, both numerically and experimentally, the validity and the limits of this approximate relation. This relation between the emission energy and the lifetime of indirect excitons can be used to tune and determine their lifetime and their resulting dynamics without the need of directly measuring it, and as a tool for design of indirect exciton based devices.

cond-mat.quant-gas↗

arXiv2016

On Projected Stochastic Gradient Descent Algorithm with Weighted Averaging for Least Squares Regression

The problem of least squares regression of a $d$-dimensional unknown parameter is considered. A stochastic gradient descent based algorithm with weighted iterate-averaging that uses a single pass over the data is studied and its convergence rate is analyzed. We first consider a bounded constraint set of the unknown parameter. Under some standard regularity assumptions, we provide an explicit $O(1/k)$ upper bound on the convergence rate, depending on the variance (due to the additive noise in the measurements) and the size of the constraint set. We show that the variance term dominates the error and decreases with rate $1/k$, while the term which is related to the size of the constraint set decreases with rate $\log k/k^2$. We then compare the asymptotic ratio $ρ$ between the convergence rate of the proposed scheme and the empirical risk minimizer (ERM) as the number of iterations approaches infinity. We show that $ρ\leq 4$ under some mild conditions for all $d\geq 1$. We further improve the upper bound by showing that $ρ\leq 4/3$ for the case of $d=1$ and unbounded parameter set. Simulation results demonstrate strong performance of the algorithm as compared to existing methods, and coincide with $ρ\leq 4/3$ even for large $d$ in practice.

cs.IT↗

arXiv2016

A dark incompressible dipolar liquid of excitons

The possible phases and the nano-scale particle correlations of two-dimensional interacting dipolar particles is a long-sought problem in many-body physics. Here we observe a spontaneous condensation of trapped two-dimensional dipolar excitons with internal spin degrees of freedom from an interacting gas into a high density, closely packed liquid state made mostly of dark dipoles. Another phase transition, into a bright, highly repulsive plasma is observed at even higher excitation powers. The dark liquid state is formed below a critical temperature $T_c \approx 4.8K$, and it is manifested by a clear spontaneous spatial condensation to a smaller and denser cloud, suggesting an attractive part to the interaction which goes beyond the purely repulsive dipole-dipole forces. Contributions from quantum mechanical fluctuations are expected to be significant in this strongly correlated, long living dark liquid. This is a new example of a two-dimensional atomic-like interacting dipolar quantum liquid, but where the coupling of light to its internal spin degrees of freedom plays a crucial role in the dynamical formation and the nature of resulting ground state.

cond-mat.quant-gas↗

arXiv2016

Vertically-coupled dipolar exciton molecules

While the interaction potential between two dipoles residing in a single plane is repulsive, in a system of two vertically adjacent layers of dipoles it changes from repulsive interaction in the long range to attractive interaction in the short range. Here we show that for dipolar excitons in semiconductor heterostructures, such a potential may give rise to bound states if two such excitons are excited in two separate layers, leading to the formation of vertically coupled dipolar exciton molecules. Our calculations prove the existence of such bound states and predict their binding energy as a function of the layers separation as well as their thermal distributions. We show that these molecules should be observed in realistic systems such as semiconductor coupled quantum well structures and the more recent van-der-Waals bound heterostructures. Formation of such molecules can lead to new effects such as a collective dipolar drag between layers and new forms of multi-particle correlations, as well as to the study of dipolar molecular dynamics in a controlled system.

cond-mat.quant-gas↗

arXiv2015

Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks

The problem of distributed rate maximization in multi-channel ALOHA networks is considered. First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization. We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games. The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility. Then, we consider the case where users are not restricted by transmission probability constraints. Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users. We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels. We show that our approach plays an important role in achieving the Nash bargaining solution among users. Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner. The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.

cs.NI↗

arXiv2014

Active Hypothesis Testing for Quickest Anomaly Detection

The problem of quickest detection of an anomalous process among M processes is considered. At each time, a subset of the processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. This problem can be considered as a special case of active hypothesis testing first considered by Chernoff in 1959 where a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically (as the error probability approaches zero) optimal. For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime. We further extend the problem to the case where multiple anomalous processes are present. In particular, we examine the case where only an upper bound on the number of anomalous processes is known.

cs.IT↗

arXiv2014

Asymptotically Optimal Anomaly Detection via Sequential Testing

Sequential detection of independent anomalous processes among K processes is considered. At each time, only M processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. Each anomalous process incurs a cost per unit time until its anomaly is identified and fixed. Switching across processes and state declarations are allowed at all times, while decisions are based on all past observations and actions. The objective is a sequential search strategy that minimizes the total expected cost incurred by all the processes during the detection process under reliability constraints. Low-complexity algorithms are established to achieve asymptotically optimal performance as the error constraints approach zero. Simulation results demonstrate strong performance in the finite regime.

cs.IT↗

arXiv2014

Optimal Index Policies for Anomaly Localization in Resource-Constrained Cyber Systems

The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observations from a probed component are realizations drawn from two different distributions depending on whether the component is normal or anomalous. The objective is a probing strategy that minimizes the total expected cost, incurred by all the components during the detection process, under reliability constraints. We consider both independent and exclusive models. In the former, each component can be abnormal with a certain probability independent of other components. In the latter, one and only one component is abnormal. We develop optimal simple index policies under both models. The proposed index policies apply to a more general case where a subset (more than one) of the components can be probed simultaneously and have strong performance as demonstrated by simulation examples. The problem under study also finds applications in spectrum scanning in cognitive radio networks and event detection in sensor networks.

cs.IT↗