skip to main content

Title: Signal Processing on Directed Graphs: The Role of Edge Directionality When Processing and Learning From Network Data
Award ID(s):
1750428 1809356 1934962
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Signal Processing Magazine
Page Range / eLocation ID:
99 to 116
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Mitochondria carry the remnant of an ancestral bacterial chromosome and express those genes with a system separate and distinct from the nucleus. Mitochondrial genes are transcribed as poly-cistronic primary transcripts which are post-transcriptionally processed to create individual translationally competent mRNAs. Algae post-transcriptional processing has only been explored in Chlamydomonas reinhardtii (Class: Chlorophyceae) and the mature mRNAs are different than higher plants, having no 5′ UnTranslated Regions (UTRs), much shorter and more variable 3′ UTRs and polycytidylated mature mRNAs. In this study, we analyzed transcript termini using circular RT-PCR and PacBio Iso-Seq to survey the 3′ and 5′ UTRs and termini for two green algae, Pediastrum duplex (Class: Chlorophyceae) and Chara vulgaris (Class: Charophyceae). This enabled the comparison of processing in the chlorophyte and charophyte clades of green algae to determine if the differences in mitochondrial mRNA processing pre-date the invasion of land by embryophytes. We report that the 5′ mRNA termini and non-template 3′ termini additions in P. duplex resemble those of C. reinhardtii, suggesting a conservation of mRNA processing among the chlorophyceae. We also report that C. vulgaris mRNA UTRs are much longer than chlorophytic examples, lack polycytidylation, and are polyadenylated similar to embryophytes. This demonstrates that some mitochondrial mRNA processing events diverged with the split between chlorophytic and streptophytic algae. 
    more » « less
  2. null (Ed.)
    Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management. 
    more » « less