SPS Feed

Voice and face are two most popular biometrics for person verification, usually used in speaker verification and face verification tasks. It has already been observed that simply combining the information from these two modalities can lead to a more powerful and robust person verification system.

Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don't fully exploit different functions of short-term neighboring and long-term periodic temporal patterns.

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. 

Constant-modulus sequence set with low peak side-lobe level is a necessity for enhancing the performance of modern active sensing systems like Multiple Input Multiple Output (MIMO) RADARs. In this paper, we consider the problem of designing a constant-modulus sequence set by minimizing the peak side-lobe level, which can be cast as a non-convex minimax problem, and propose a Majorization-Minimization technique based iterative monotonic algorithm named as the PSL minimizer.