Author
Hawraa Salami
Other affiliations: University of California, American University of Beirut
Bio: Hawraa Salami is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Social learning & Signal. The author has an hindex of 4, co-authored 10 publications receiving 95 citations. Previous affiliations of Hawraa Salami include University of California & American University of Beirut.
Papers
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10 Feb 2017
TL;DR: It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Abstract: In this paper, we study diffusion social learning over weakly connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this paper, a scenario of total influence (or “mind-control”) arises where a set of influential agents ends up shaping the beliefs of noninfluential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.
59 citations
25 May 2017
TL;DR: An energy-efficient, implantable, real-time, blind Adaptive Stimulation Artifact Rejection (ASAR) engine is proposed, which enables concurrent neural stimulation and recording for state-of-the-art closed-loop neuromodulation systems.
Abstract: In this work we propose an energy-efficient, implantable, real-time, blind Adaptive Stimulation Artifact Rejection (ASAR) engine. This enables concurrent neural stimulation and recording for state-of-the-art closed-loop neuromodulation systems. Two engines, implemented in 40nm CMOS, achieve convergence of p-p by 49.2dB, without any prior knowledge of the stimulation pulse. The LFP and Spike ASAR designs occupy an area of 0.197mm2 and 0.209mm2, and consume 1.73µW and 3.02µW, respectively at 0.644V.
17 citations
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TL;DR: This article develops mechanisms by which influential agents can lead receiving agents to adopt certain beliefs and examines whether receiving agents can be driven to arbitrary beliefs and whether the network structure limits the scope of control by the influential agents.
Abstract: In diffusion social learning over weakly-connected graphs, it has been shown recently that influential agents shape the beliefs of non-influential agents. This paper analyzes this mechanism more closely and addresses two main questions. First, the article examines how much freedom influential agents have in controlling the beliefs of the receiving agents, namely, whether receiving agents can be driven to arbitrary beliefs and whether the network structure limits the scope of control by the influential agents. Second, even if there is a limit to what influential agents can accomplish, this article develops mechanisms by which they can lead receiving agents to adopt certain beliefs. These questions raise interesting possibilities about belief control over networked agents. Once addressed, one ends up with design procedures that allow influential agents to drive other agents to endorse particular beliefs regardless of their local observations or convictions. The theoretical findings are illustrated by means of examples.
10 citations
20 Mar 2016
TL;DR: It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Abstract: In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this work, a scenario of total influence (or "mind-control") arises where a set of influential agents ends up shaping the beliefs of non-influential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.
9 citations
Posted Content•
TL;DR: In this paper, the authors study diffusion social learning over weakly-connected graphs and show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations.
Abstract: In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this work, a scenario of total influence (or "mind-control") arises where a set of influential agents ends up shaping the beliefs of non-influential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.
9 citations
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TL;DR: In this article, the authors consider the problem of distributed learning where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes.
Abstract: We consider the problem of distributed learning , where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.
155 citations
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TL;DR: This work proposes a distributed algorithm and establishes consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses.
Abstract: We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a non-asymptotic, explicit and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.
130 citations
TL;DR: The state-of-the-art for artifact-preventing system configurations, resilient recording front-ends, and back-end signal processing for removing recorded artifacts are reviewed.
85 citations
TL;DR: In this paper, a review of the contributions made by Graph Signal Processing (GSP) concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms is presented.
Abstract: The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.
67 citations
10 Feb 2017
TL;DR: It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Abstract: In this paper, we study diffusion social learning over weakly connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this paper, a scenario of total influence (or “mind-control”) arises where a set of influential agents ends up shaping the beliefs of noninfluential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.
59 citations