SUMMARY: Stacked barplots, though widely used in microbiome studies, can obscure important patterns in microbiome data. They omit rare taxa and can mask shifts that emerge at finer taxonomic levels. To address this issue, we introduce phylobar, an R package that interactively links stacked barplots with overview phylogenetic or taxonomic hierarchies. The interface allows users to collapse or expand subtrees, paint color palettes interactively, and search for specific taxa. This allows comparison across taxonomic resolutions that are hidden in static overviews. phylobar works with any omics data with hierarchical organization, including cell type hierarchies, as we demonstrate in a case study of immune cell composition in COVID-19 patients. AVAILABILITY AND IMPLEMENTATION: phylobar is available as an R package on GitHub. It uses the htmlwidgets library to link interactive D3 visualizations with R. The interactive plots can be embedded within R Markdown or Quarto notebooks, and views can be exported as vector graphics files. The package is open source and documented at https://mkdiro-O.github.io/phylobar.

FPGA-based acceleration has been explored to address performance challenges in architectures incorporating federated learning (FL) and homomorphic encryption (HE). However, model updates, which involve HE, are infrequent in FL applications; thus, static allocation of FPGA resources for HE can lead to inefficiency. In response, this paper presents a work-in-progress FPGA-based FL accelerator that leverages dynamic partial reconfiguration to accelerate HE operations flexibly.

High-quality compilation of Digital Signal Processing (DSP) algorithms is crucial for achieving real-time performance and optimizing resource utilization. Traditional compilers often struggle to effectively optimize DSP applications since their optimization passes mainly deal with low-level intermediate representations. This paper introduces DSP-MLIR – a comprehensive framework for DSP application development and optimization. DSP-MLIR comprises i) a Python-like domain-specific language (DSL) (named DSP-DSL) for intuitive and easier programming of DSP applications, ii) a dedicated MLIR dialect (named DSP-dialect) with 90+ operations and 16 optimizations at the level of DSP operations, and iii) lowerings to the Affine and standard MLIR dialects for high-quality compilation flow for DSP applications. The effectiveness of the proposed DSP-MLIR is evaluated by comparing the runtimes of the binaries generated by the various compilation flows, including GCC, Clang, Hexagon-Clang, and existing MLIR passes. Experiments on 20 DSP applications collected from various sources demonstrate an average performance improvement of 12% over state-of-the-art compilation flows with a 10% reduction in the generated binary size and no significant variation in compilation time. Further, expressing DSP applications in the proposed DSP-DSL reduces the code complexity and development time of DSP applications (as measured in lines of code (LOC)) by an average of 5x over their specification in the programming language, “C”.