The IEEE Signal Processing Society is seeking nominations for future leaders! Nominations for several SPS leadership roles are open now, and we are now soliciting for the following roles with terms beginning 1 January 2023.

In this talk, we discuss a new transform technique for solving fractional programming (FP), i.e., a family of optimization problems with ratio terms. The classic FP techniques such as the Charnes-Cooper and Dinkelbach’s methods typically deal with a single ratio, and in general, do not work for multiple ratios. 

IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

The IEEE Signal Processing Society (SPS) invites nominations for the position of Editor-in-Chief for the following journals: IEEE Signal Processing Letters for a 3-year term starting 1 January 2023.

The COVID-19 pandemic has prompted a global shift in meetings and events, many moving from in-person gatherings to a more virtual implementation. SPS embraces these changes not only during these unprecedentedly uncertain times, but to inspire future potential for its members and Chapters to expand their reach, engaging and empowering members and potential members to connect locally and globally, especially at a time when timely research and connection are of such great importance.

IEEE Transactions on Mobile Computing seeks applicants for the position of Editor-in-Chief. The EIC term is three years, nonrenewable. The Steering Committee of the Transactions on Mobile Computing invites applications, which should be sent to Cathy Martin by 1 March 2022.

Dr. Oriol Vinyals is a Principal Scientist at DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. 

We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.

As part of our ongoing video series to demonstrate the applications of signal processing in everyday life, the IEEE Signal Processing Society Computational Imaging Technical Committee has created a new video, Computational Imaging in Everyday Life.

Many important application domains generate distributed collections of heterogeneous local datasets. These local datasets are related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets. Networked federated learning aims at learning a tailored local model for each local dataset.