Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

Networks are a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a generative approach to network inference (RCweb) for the case when the network is sparse and the latent (not observed) variables affect the observed ones. From all possible factor analysis (FA) decompositions explaining the variance in the data, RCweb selects the FA decomposition that is consistent with a sparse underlying network. The sparsity constraint is imposed by a novel method that significantly outperforms (in terms of accuracy, robustness to noise, complexity scaling, and computational efficiency) Bayesian methods and MLE methods using l1 norm relaxation such as K-SVD and l1--based sparse principle component analysis (PCA). Results from simulated models demonstrate that RCweb recovers exactly the model structures for sparsity as low (as non-sparse) as 50% and with ratio of unobserved to observed variables as high as 2. RCweb is robust to noise, with gradual decrease in the parameter ranges as the noise level increases.Comment: 8 pages, 5 figure

Towards an approximate graph entropy measure for identifying incidents in network event data

A key objective of monitoring networks is to identify potential service threatening outages from events within the network before service is interrupted. Identifying causal events, Root Cause Analysis (RCA), is an active area of research, but current approaches are vulnerable to scaling issues with high event rates. Elimination of noisy events that are not causal is key to ensuring the scalability of RCA. In this paper, we introduce vertex-level measures inspired by Graph Entropy and propose their suitability as a categorization metric to identify nodes that are a priori of more interest as a source of events. We consider a class of measures based on Structural, Chromatic and Von Neumann Entropy. These measures require NP-Hard calculations over the whole graph, an approach which obviously does not scale for large dynamic graphs that characterise modern networks. In this work we identify and justify a local measure of vertex graph entropy, which behaves in a similar fashion to global measures of entropy when summed across the whole graph. We show that such measures are correlated with nodes that generate incidents across a network from a real data set

EXIT-charts-aided hybrid multiuser detector for multicarrier interleave-division multiple access

A generically applicable hybrid multiuser detector (MUD) concept is proposed by appropriately activating different MUDs in consecutive turbo iterations based on the mutual information (MI) gain. It is demonstrated that the proposed hybrid MUD is capable of approaching the optimal Bayesian MUD's performance despite its reduced complexity, which is at a modestly increased complexity in comparison with that of the suboptimum soft interference cancellation (SoIC) MU

Generalized Adaptive Network Coding Aided Successive Relaying Based Noncoherent Cooperation

A generalized adaptive network coding (GANC) scheme is conceived for a multi-user, multi-relay scenario, where the multiple users transmit independent information streams to a common destination with the aid of multiple relays. The proposed GANC scheme is developed from adaptive network coded cooperation (ANCC), which aims for a high flexibility in order to: 1) allow arbitrary channel coding schemes to serve as the cross-layer network coding regime; 2) provide any arbitrary trade-off between the throughput and reliability by adjusting the ratio of the source nodes and the cooperating relay nodes. Furthermore, we incorporate the proposed GANC scheme in a novel successive relaying aided network (SRAN) in order to recover the typical 50% half-duplex relaying-induced throughput loss. However, it is unrealistic to expect that in addition to carrying out all the relaying functions, the relays could additionally estimate the source-to-relay channels. Hence noncoherent detection is employed in order to obviate the power-hungry channel estimation. Finally, we intrinsically amalgamate our GANC scheme with the joint network-channel coding (JNCC) concept into a powerful three-stage concatenated architecture relying on iterative detection, which is specifically designed for the destination node (DN). The proposed scheme is also capable of adapting to rapidly time-varying network topologies, while relying on energy-efficient detection

Cooperative differential space-time spreading for the asynchronous relay aided CDMA uplink using interference rejection spreading code

Abstract—This letter proposes a differential Space–Time Coding (STC) scheme designed for asynchronous cooperative networks, where neither channel estimation nor symbol-level synchroniza-tion is required at the cooperating nodes. More specifically, our system employs differential encoding during the broadcast phase and a Space–Time Spreading (STS)-based amplify-and-forward scheme during the cooperative phase in conjunction with inter-ference rejection direct sequence spreading codes, namely Loosely Synchronized (LS) codes. Our simulation results demonstrate that the proposed Cooperative Differential STS (CDSTS) scheme is ca-pable of combating the effects of asynchronous uplink transmis-sions without any channel state information. Index Terms—Asynchronous cooperation, cooperative diversity, differential space–time spreading, loosely synchronized codes. I

Experimental nonlocality-based network diagnostics of mutipartite entangled states

Quantum networks of growing complexity play a key role as resources for quantum computation; the ability to identify the quality of their internal correlations will play a crucial role in addressing the buiding stage of such states. We introduce a novel diagnostic scheme for multipartite networks of entangled particles, aimed at assessing the quality of the gates used for the engineering of their state. Using the information gathered from a set of suitably chosen multiparticle Bell tests, we identify conditions bounding the quality of the entangled bonds among the elements of a register. We demonstrate the effectiveness, flexibility, and diagnostic power of the proposed methodology by characterizing a quantum resource engineered combining two-photon hyperentanglement and photonic-chip technology. Our approach is feasible for medium-sized networks due to the intrinsically modular nature of cluster states, and paves the way to section-by-section analysis of large photonics resources.Comment: 5 pages, 3 figures, RevTex4-

Load fluctuations drive actin network growth

The growth of actin filament networks is a fundamental biological process that drives a variety of cellular and intracellular motions. During motility, eukaryotic cells and intracellular pathogens are propelled by actin networks organized by nucleation-promoting factors, which trigger the formation of nascent filaments off the side of existing filaments in the network. A Brownian ratchet (BR) mechanism has been proposed to couple actin polymerization to cellular movements, whereby thermal motions are rectified by the addition of actin monomers at the end of growing filaments. Here, by following actin--propelled microspheres using three--dimensional laser tracking, we find that beads adhered to the growing network move via an object--fluctuating BR. Velocity varies with the amplitude of thermal fluctuation and inversely with viscosity as predicted for a BR. In addition, motion is saltatory with a broad distribution of step sizes that is correlated in time. These data point to a model in which thermal fluctuations of the microsphere or entire actin network, and not individual filaments, govern motility. This conclusion is supported by Monte Carlo simulations of an adhesion--based BR and suggests an important role for membrane tension in the control of actin--based cellular protrusions.Comment: To be published in PNA