Graph-homomorphic perturbations for private decentralized learning

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to share raw data due to privacy concerns. Nevertheless, in the absence of additional privacy-preserving mechanisms, the exchange of local estimates, which are generated based on private data can allow for the inference of the data itself. The most common mechanism for guaranteeing privacy is the addition of perturbations to local estimates before broadcasting. These perturbations are generally chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible (to first order in the step-size) to the network centroid, while preserving privacy guarantees. The analysis allows for general nonconvex loss functions, and is hence applicable to a large number of machine learning and signal processing problems, including deep learning

Regularized diffusion adaptation via conjugate smoothing

The purpose of this work is to develop and study a decentralized strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution while the regularizers are assumed deterministic, but are not required to be differentiable or even continuous. The individual, regularized, cost functions are distributed across a strongly-connected network of agents and the Pareto optimal solution is sought by appealing to a multi-agent diffusion strategy. To this end, the regularizers are smoothed by means of infimal convolution and it is shown that the Pareto solution of the approximate, smooth problem can be made arbitrarily close to the solution of the original, non-smooth problem. Performance bounds are established under conditions that are weaker than assumed before in the literature, and hence applicable to a broader class of adaptation and learning problems

Network classifiers based on social learning

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results

Social learning under inferential attacks

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model

Networked signal and information processing

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources

Real-Time Stereopsis

The computerized development always was on the center of attraction. More accurate result generation in shorter time period has done by computers, make humans eager to givescomputers moreresponsibility and tasks. Computer vision is one of thesetasks that for past centuries take lots of timeand effortone the rodeofperfection. One of the areas in the computer vision is stereo vision or Stereopsis. This area start in early I970's and still up to day is one of the mysteries part of computer vision. Peoples try to make computer sees as human see. Up today there are manyalgorithm developed and invented by scientist but it's long way to go. The main purpose of this report is to shows how it's possible for computer to calculate depth irom 2D images and then base on some algorithms, it tries to construct 3D result. But whywe need to make all these efforts andwhy it's so important to make computer sees as human see. One ofthe strongest effects ofthis process is for 3D animation development. Generating 3D libraries and assist game developers or generally graphic developers. Imagine for development of one hour movie 60 computers work for one year and see how fast it will be to have all the models available in very short time. Another effect of this system if for virtual realities systems. Beside 3D modeling which require real-time rendering it also require certain amount oftracking for user interaction. This process normally handle by sensors, where its limited to few sensors and also it makes users uncomfortable and its harder for virtual environment to be more realistic for users

Video streaming in urban vehicular environments: Junction-aware multipath approach

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In multipath video streaming transmission, the selection of the best vehicle for video packet forwarding considering the junction area is a challenging task due to the several diversions in the junction area. The vehicles in the junction area change direction based on the different diversions, which lead to video packet drop. In the existing works, the explicit consideration of different positions in the junction areas has not been considered for forwarding vehicle selection. To address the aforementioned challenges, a Junction-Aware vehicle selection for Multipath Video Streaming (JA-MVS) scheme has been proposed. The JA-MVS scheme considers three different cases in the junction area including the vehicle after the junction, before the junction and inside the junction area, with an evaluation of the vehicle signal strength based on the signal to interference plus noise ratio (SINR), which is based on the multipath data forwarding concept using greedy-based geographic routing. The performance of the proposed scheme is evaluated based on the Packet Loss Ratio (PLR), Structural Similarity Index (SSIM) and End-to-End Delay (E2ED) metrics. The JA-MVS is compared against two baseline schemes, Junction-Based Multipath Source Routing (JMSR) and the Adaptive Multipath geographic routing for Video Transmission (AMVT), in urban Vehicular Ad-Hoc Networks (VANETs)

Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-009-0354-x[EN] This paper describes a pipelined parallel algorithm for the MMSE-OSIC decoding procedure proposed in V-BLAST wireless MIMO systems, for heterogeneous networks of processors. It is based on a block version of the square-root Kalman Filter algorithm that was initially devised to solve the RLS problem. It has been parallelized in a pipelined way obtaining a good efficiency and scalability. The optimum load balancing for this parallel algorithm is dynamic, but we derive a static load balancing scheme with good performance. © 2009 Springer Science+Business Media, LLC.This work has been supported by the Generalitat Valenciana, project 20080811, by the Universidad Politecnica de Valencia, project 20080009, by the Conserjería de Educacion de la Región de Murcia (Fundacion Séneca, 08763/PI/08), and by the Ministerio de Ciencia e Innovacion (TIN2008-06570-C04-02).Martínez Zaldívar, FJ.; Vidal Maciá, AM.; Giménez Cánovas, D. (2011). Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem. Journal of Supercomputing. 58(2):235-243. https://doi.org/10.1007/s11227-009-0354-xS235243582Foschini GJ (1996) Layered space-time architecture for wireless communications in a fading environment when using multiple antennas. Bell Labs Techn J 1:41–59Hassibi B (2000) An efficient square-root algorithm for BLAST. In: IEEE international conference on acoustics, speech and signal processing 2000, vol 2, pp II737–II740Zhu H, Lei Z, Chin FPS (2004) An improved square-root algorithm for BLAST. IEEE Signal Process Lett 11(9)Choi Y-S, Voltz PJ, Cassara FA (2001) On channel estimation and detection for multicarrier signals in fast and selective Rayleigh fading channels. IEEE Trans Commun 49(8)Burg A, Haene S, Perels D, Luethi P, Felber N, Fichtner W (2006) Algorithm and VLSI architecture for linear MMSE detection in MIMO-OFDM systems. In: Proceedings of the IEEE int symp on circuits and systems, May 2006Martínez Zaldívar FJ (2007) Algoritmos paralelos segmentados para los problemas de Mínimos Cuadrados Recursivos (RLS) y de Detección por Cancelación Ordenada y Sucesiva de Interferencia (OSIC). PhD thesis, Facultad de Informática, Universidad Politécnica de Valencia, SpainSayed AH, Kailath T (1994) A state-space approach to adaptive RLS filtering. IEEE Signal Process Mag 11(3):18–60Kumar V, Gram A, Gupta A, Karypis G (2003) An introduction to parallel computing: design and analysis of algorithms, Chap 4, 2nd edn. Addison-Wesley, Harlow

Robust Framework for PET Image Reconstruction Incorporating System and Measurement Uncertainties

In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts

Molecular identification of adenoviruses associated with respiratory infection in Egypt from 2003 to 2010.

BACKGROUND: Human adenoviruses of species B, C, and E (HAdV-B, -C, -E) are frequent causative agents of acute respiratory infections worldwide. As part of a surveillance program aimed at identifying the etiology of influenza-like illness (ILI) in Egypt, we characterized 105 adenovirus isolates from clinical samples collected between 2003 and 2010. METHODS: Identification of the isolates as HAdV was accomplished by an immunofluorescence assay (IFA) and confirmed by a set of species and type specific polymerase chain reactions (PCR). RESULTS: Of the 105 isolates, 42% were identified as belonging to HAdV-B, 60% as HAdV-C, and 1% as HAdV-E. We identified a total of six co-infections by PCR, of which five were HAdV-B/HAdV-C co-infections, and one was a co-infection of two HAdV-C types: HAdV-5/HAdV-6. Molecular typing by PCR enabled the identification of eight genotypes of human adenoviruses; HAdV-3 (n = 22), HAdV-7 (n = 14), HAdV-11 (n = 8), HAdV-1 (n = 22), HAdV-2 (20), HAdV-5 (n = 15), HAdV-6 (n = 3) and HAdV-4 (n = 1). The most abundant species in the characterized collection of isolates was HAdV-C, which is concordant with existing data for worldwide epidemiology of HAdV respiratory infections. CONCLUSIONS: We identified three species, HAdV-B, -C and -E, among patients with ILI over the course of 7 years in Egypt, with at least eight diverse types circulating