ARGENCON 2022 - First CIS Chapter 20th anniversary celebration

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This event has three parts:

a) Lecturer: James M. Keller (CIS President)

Title: "Streaming Data Analytics: Clustering or Classification?"

As the volume and variety of temporally acquired data continues to grow, increased attention is being paid to streaming analysis of that data. Think of a drone flying over unknown terrain looking for specific objects which may present differently in different environments. Understanding the evolving environments is a critical component of a recognition system. With the explosion of ubiquitous continuous sensing (something Lotfi Zadeh predicted as one of the pillars of Recognition Technology in the late 1990s), this on-line streaming analysis is normally cast as a clustering problem. However, examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. These approaches model existing and newly discovered structures via summary information that we call footprints. Incoming data is routinely assigned crisp labels (into one of the structures) and that structure’s footprints are incrementally updated; the data is not saved for iterative assignments. 

b) Lecturer: Santiago Segarra

Title: "Graph Neural Networks with Applications to Wireless Communications"

As the availability of relational data continues to grow, graph-based modeling and processing techniques have become a mainstay in the current research landscape, cutting across fields of knowledge. Drawing from graph signal processing, in this talk we provide a first introduction to graph neural networks (GNNs), emphasizing their properties and connections to classical signal processing and machine learning. Furthermore, we present several applications of GNNs in wireless communications. More specifically, we discuss the use of GNNs in conjunction with algorithmic unfolding for quasi-optimal power allocation and distributed link scheduling.

c) Special celebration event: CIS President salutation, video recorded compilation with worldwide salutation. 



  Date and Time

  Location

  Hosts

  Registration



  • Start time: 07 Sep 2022 09:00 AM
  • End time: 09 Sep 2022 06:00 PM
  • All times are (UTC-03:00) Buenos Aires
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https://attend.ieee.org/argencon-2022/

  • Mitre 396 Este
  • San Juan - Argentina - (J5402CWH)
  • San Juan, San Juan
  • Argentina
  • Building: Edificio Central

  • Co-sponsored by Universidad Nacional de San Juan and Universidad de Palermo
  • Starts 07 July 2022 10:00 AM
  • Ends 09 September 2022 06:00 PM
  • All times are (UTC-03:00) Buenos Aires
  • No Admission Charge


  Speakers

James Keller

Topic:

Streaming Data Analytics: Clustering or Classification?

As the volume and variety of temporally acquired data continues to grow, increased attention is being paid to streaming analysis of that data. Think of a drone flying over unknown terrain looking for specific objects which may present differently in different environments. Understanding the evolving environments is a critical component of a recognition system. With the explosion of ubiquitous continuous sensing (something Lotfi Zadeh predicted as one of the pillars of Recognition Technology in the late 1990s), this on-line streaming analysis is normally cast as a clustering problem. However, examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. These approaches model existing and newly discovered structures via summary information that we call footprints. Incoming data is routinely assigned crisp labels (into one of the structures) and that structure’s footprints are incrementally updated; the data is not saved for iterative assignments. 

 

Address:Argentina

Santiago Segarra

Topic:

Graph Neural Networks with Applications to Wireless Communications

As the availability of relational data continues to grow, graph-based modeling and processing techniques have become a mainstay in the current research landscape, cutting across fields of knowledge. Drawing from graph signal processing, in this talk we provide a first introduction to graph neural networks (GNNs), emphasizing their properties and connections to classical signal processing and machine learning. Furthermore, we present several applications of GNNs in wireless communications. More specifically, we discuss the use of GNNs in conjunction with algorithmic unfolding for quasi-optimal power allocation and distributed link scheduling.