SPS Newsletter Article

Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever-increasing spatio-temporal resolution. Multidimensional, as time series data are recorded at multiple locations (electrodes, voxels), from multiple subjects, under various conditions.

In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however, the approaches appear very different: current formulations of perceptual synthesis call on theories like predictive coding and Bayesian brain hypothesis. 

While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In order to gain increased expressive power while retaining efficiency, several recent works apply MPNNs to subgraphs of the original graph. 

Focus stacking is an effective approach to extending the depth of field of a camera, yet is challenging with regard to 1) controlling focal planes in forming a stack and 2) fusing the focal stack into composites that are free from defocusing, i.e., all-in-focus. 

Many tasks in medicine still involve substantial manual work. In many cases there is strong potential for intelligent automation by Artificial Intelligence (AI), leading possibly to a reduction in costs and man-hours, while increasing the quality of clinical service.

Non-line-of-sight (NLoS) imaging is an exciting technology which uses time-resolved sensors to see objects that are hidden from view. In essence, NLoS imaging lets someone turn a wall into a mirror. This talk will highlight recent advances in NLoS imaging, with an emphasis on the algorithms which underlie their operation. 

As an authentication method, biometrics offer unparalleled convenience and security. With very little for users to remember and do, there is also very little that they can do incorrectly, thus limiting the attack surface of an authentication system. Unfortunately, biometrics also present a challenging privacy/security tradeoff: biometric data...

Augmented reality devices of the future will likely fuse sensor data from several modalities, allowing multichannel speech enhancement algorithms to exploit, for example, head orientation and accurately estimated source directions. 

Separating the household aggregated energy consumption signal into its additive sub-components (the power signal from individual appliances), is called energy (power) disaggregation or non-intrusive load monitoring (NILM).

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.