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1. Pangenomic genotyping with the marker array

   https://doi.org/10.1186/s13015-023-00225-3  

   Mun, Taher; Vaddadi, Naga Sai Kavya; Langmead, Ben (May 2023, Algorithms for Molecular Biology)

   Abstract We present a new method and software tool called that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool available athttps://github.com/alshai/rowbowt. 

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2. Adaptive ergodic search with energy-aware scheduling for persistent multi-robot missions

   https://doi.org/10.1007/s10514-025-10215-6  

   Naveed, Kaleb_Ben; Agrawal, Devansh_R; Kumar, Rahul; Panagou, Dimitra (September 2025, Autonomous Robots)

   Abstract Autonomous robots are increasingly deployed for long-term information-gathering tasks, which pose two key challenges: planning informative trajectories in environments that evolve across space and time, and ensuring persistent operation under energy constraints. This paper presents a unified framework, , that addresses both challenges through adaptive ergodic search and energy-aware scheduling in multi-robot systems. Our contributions are two-fold: (1) we model real-world variability using stochastic spatiotemporal environments, where the underlying information evolves continuously over space and time under process noise. To guide exploration, we construct a target information spatial distribution (TISD) based on clarity, a metric that captures the decay of information in the absence of observations and highlights regions of high uncertainty; and (2) we introduce ( ), an online scheduling method that enables persistent operation by coordinating rechargeable robots sharing a single mobile charging station. Unlike prior work, our approach avoids reliance on preplanned schedules, static or dedicated charging stations, and simplified robot dynamics. Instead, the scheduler supports general nonlinear models, accounts for uncertainty in the estimated position of the charging station, and handles central node failures. The proposed framework is validated through real-world hardware experiments, and feasibility guarantees are provided under specific assumptions.[Code: https://github.com/kalebbennaveed/mEclares-main.git][Experiment Video: https://www.youtube.com/watch?v=dmaZDvxJgF8] 

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3. Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks

   https://doi.org/10.1186/s13007-023-00996-1  

   Saeed, Farah; Sun, Shangpeng; Rodriguez-Sanchez, Javier; Snider, John; Liu, Tianming; Li, Changying (December 2023, Plant Methods)

   Abstract BackgroundPlant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits. ResultsThe Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R²value of more than 0.8 and mean absolute percentage error of less than 10% were attained. ConclusionThis plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available athttps://github.com/UGA-BSAIL/plant_3d_deep_learning. 

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4. dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

   https://doi.org/10.1101/2023.06.15.545182  

   Huang, Xin; Struck, Travis J; Davey, Sean W; Gutenkunst, Ryan N (June 2023, bioRxiv)

   Abstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/. 

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5. TAPA: A Scalable Task-parallel Dataflow Programming Framework for Modern FPGAs with Co-optimization of HLS and Physical Design

   https://doi.org/10.1145/3609335  

   Guo, Licheng; Chi, Yuze; Lau, Jason; Song, Linghao; Tian, Xingyu; Khatti, Moazin; Qiao, Weikang; Wang, Jie; Ustun, Ecenur; Fang, Zhenman; et al (December 2023, ACM Transactions on Reconfigurable Technology and Systems)

   In this article, we propose TAPA, an end-to-end framework that compiles a C++ task-parallel dataflow program into a high-frequency FPGA accelerator. Compared to existing solutions, TAPA has two major advantages. First, TAPA provides a set of convenient APIs that allows users to easily express flexible and complex inter-task communication structures. Second, TAPA adopts a coarse-grained floorplanning step during HLS compilation for accurate pipelining of potential critical paths. In addition, TAPA implements several optimization techniques specifically tailored for modern HBM-based FPGAs. In our experiments with a total of 43 designs, we improve the average frequency from 147 MHz to 297 MHz (a 102% improvement) with no loss of throughput and a negligible change in resource utilization. Notably, in 16 experiments, we make the originally unroutable designs achieve 274 MHz, on average. The framework is available athttps://github.com/UCLA-VAST/tapaand the core floorplan module is available athttps://github.com/UCLA-VAST/AutoBridge 

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