Marginal integration for nonparametric causal inference

We consider the problem of inferring the total causal effect of a single variable intervention on a (response) variable of interest. We propose a certain marginal integration regression technique for a very general class of potentially nonlinear structural equation models (SEMs) with known structure, or at least known superset of adjustment variables: we call the procedure S-mint regression. We easily derive that it achieves the convergence rate as for nonparametric regression: for example, single variable intervention effects can be estimated with convergence rate $n^{-2/5}$ assuming smoothness with twice differentiable functions. Our result can also be seen as a major robustness property with respect to model misspecification which goes much beyond the notion of double robustness. Furthermore, when the structure of the SEM is not known, we can estimate (the equivalence class of) the directed acyclic graph corresponding to the SEM, and then proceed by using S-mint based on these estimates. We empirically compare the S-mint regression method with more classical approaches and argue that the former is indeed more robust, more reliable and substantially simpler.Comment: 40 pages, 14 figure

The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components

G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/similar to ismel/GPCR-Nets/index.html

Fully integrated millimeter-wave CMOS phased arrays

A decade ago, RF CMOS, even at low gigahertz frequencies, was considered an oxymoron by all but the most ambitious and optimistic. Today, it is a dominating force in most commercial wireless applications (e.g., cellular, WLAN, GPS, BlueTooth, etc.) and has proliferated into areas such as watt level power amplifiers (PA) [1] that have been the undisputed realm of compound semiconductors. This seemingly ubiquitous embracement of silicon and particularly CMOS is no accident. It stems from the reliable nature of silicon process technologies that make it possible to integrated hundreds of millions of transistors on a single chip without a single device failure, as evident in today’s microprocessors. Applied to microwave and millimeter wave applications, silicon opens the door for a plethora of new topologies, architectures, and applications. This rapid adoption of silicon is further facilitated by one’s ability to integrate a great deal of in situ digital signal processing and calibration [2]. Integration of high-frequency phased-array systems in silicon (e.g., CMOS) promises a future of low-cost radar and gigabit-per-second wireless communication networks. In communication applications, phased array provides an improved signal-to-noise ratio via formation of a beam and reduced interference generation for other users. The practically unlimited number of active and passive devices available on a silicon chip and their extremely tight control and excellent repeatability enable new architectures (e.g., [3]) that are not practical in compound semiconductor module-based approaches. The feasibility of such approaches can be seen through the discussion of an integrated 24GHz 4-element phased-array transmitter in 0.18μm CMOS [2], capable of beam forming and rapid beam steering for radar applications. On-chip power amplifiers (PA), with integrated 50Ω output matching, make this a fully-integrated transmitter. This CMOS transmitter and the 8-element phased-array SiGe receiver in [5], demonstrate the feasibility of 24GHz phased-array systems in silicon-based processes

A methodological approach to BISDN signalling performance

Sophisticated signalling protocols are required to properly handle the complex multimedia, multiparty services supported by the forthcoming BISDN. The implementation feasibility of these protocols should be evaluated during their design phase, so that possible performance bottlenecks are identified and removed. In this paper we present a methodology for evaluating the performance of BISDN signalling systems under design. New performance parameters are introduced and their network-dependent values are extracted through a message flow model which has the capability to describe the impact of call and bearer control separation on the signalling performance. Signalling protocols are modelled through a modular decomposition of the seven OSI layers including the service user to three submodels. The workload model is user descriptive in the sense that it does not approximate the direct input traffic required for evaluating the performance of a layer protocol; instead, through a multi-level approach, it describes the actual implications of user signalling activity for the general signalling traffic. The signalling protocol model is derived from the global functional model of the signalling protocols and information flows using a network of queues incorporating synchronization and dependency functions. The same queueing approach is followed for the signalling transfer network which is used to define processing speed and signalling bandwidth requirements and to identify possible performance bottlenecks stemming from the realization of the related protocols

On Network Science and Mutual Information for Explaining Deep Neural Networks

In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.Comment: ICASSP 2020 (shorter version appeared at AAAI-19 Workshop on Network Interpretability for Deep Learning