Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation

Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in reasoning about gripper-object interactions. In this work, we introduce Hybrid Actor-Critic Maps for Manipulation (HACMan), a reinforcement learning approach for 6D non-prehensile manipulation of objects using point cloud observations. HACMan proposes a temporally-abstracted and spatially-grounded object-centric action representation that consists of selecting a contact location from the object point cloud and a set of motion parameters describing how the robot will move after making contact. We modify an existing off-policy RL algorithm to learn in this hybrid discrete-continuous action representation. We evaluate HACMan on a 6D object pose alignment task in both simulation and in the real world. On the hardest version of our task, with randomized initial poses, randomized 6D goals, and diverse object categories, our policy demonstrates strong generalization to unseen object categories without a performance drop, achieving an 89% success rate on unseen objects in simulation and 50% success rate with zero-shot transfer in the real world. Compared to alternative action representations, HACMan achieves a success rate more than three times higher than the best baseline. With zero-shot sim2real transfer, our policy can successfully manipulate unseen objects in the real world for challenging non-planar goals, using dynamic and contact-rich non-prehensile skills. Videos can be found on the project website: https://hacman-2023.github.io

From Cancer Sequencing Data to Neoantigen Prediction: A Reusable Pipeline using Snakemake

Neoantigens are newly formed peptides formed by somatic mutations that are capable of inducing tumor-specific T-cell recognition. Because neoantigens are expressed specifically in tumor cells, prediction of these neoantigens can lead to personalized immunotherapies for the treatment of cancers. This process involves many steps, the most crucial of which is identification of expressed somatic mutations (or variants) using next generation sequencing data. After evaluating multiple bioinformatics tools for somatic mutation calling, we selected GATK (Genome Analysis ToolKit) for its ability to accurately call expected mutations. There are other steps that need to be performed before and after identification of somatic mutations as well and these include mapping, duplicate marking, annotation of mutation calls, and filtering of mutation calls. We developed a pipeline using the workflow management system Snakemake to perform these steps in order to identify somatic mutations from whole exome and RNA-Seq data. By making this into a snakemake workflow, we are able to easily extend upon it and add more steps as was done for neoantigen prediction. Furthermore, Snakemake submits slurm jobs for each individual step and can intelligently adjust the runtime and processing load for those jobs. This makes it simple to run even very large samples through the pipeline. We have evaluated this pipeline using RNA sequencing and whole exome sequencing data from 46 Multiple Myeloma cell lines and have identified hundreds of expressed mutations per cell line. This reusable and expandable pipeline can serve as a useful resource for other researchers looking to identify expressed mutations and make neoantigen predictions from cancer sequencing data

Multi-Effects Coupled Nanogenerators for Simultaneously Harvesting Solar, Thermal, and Mechanical Energies

As a result of the widespread use of small-scale and low-power electronic devices, the demand for micro-energy sources has increased, in particular the potential to harvest the wide variety of energy sources present in their surrounding environment. In this paper, a novel coupled nanogenerator that can realize energy harvesting for multiple energy sources is reported. Based on the unique electrical properties of ferroelectric Bi 0.5Na 0.5TiO 3 (BNT) materials, it is possible to combine a photovoltaic cell, pyroelectric nanogenerator, and triboelectric-piezoelectric nanogenerator in a single element to harvest light, heat, and mechanical energy simultaneously. To evaluate the effectiveness of coupling for different materials, a Yang coupling factor (k C,Q) is defined in terms of transferred charge, where BNT has the largest k C,Q of 1.29 during heating, indicating that BNT has the best coupling enhancement compared to common ferroelectric materials. This new criterion and novel device structure therefore provide a new basis for the future development of coupled nanogenerators which are capable of harvesting multiple sources of energy.</p

Multi-Effects Coupled Nanogenerators for Simultaneously Harvesting Solar, Thermal, and Mechanical Energies

As a result of the widespread use of small-scale and low-power electronic devices, the demand for micro-energy sources has increased, in particular the potential to harvest the wide variety of energy sources present in their surrounding environment. In this paper, a novel coupled nanogenerator that can realize energy harvesting for multiple energy sources is reported. Based on the unique electrical properties of ferroelectric Bi 0.5Na 0.5TiO 3 (BNT) materials, it is possible to combine a photovoltaic cell, pyroelectric nanogenerator, and triboelectric-piezoelectric nanogenerator in a single element to harvest light, heat, and mechanical energy simultaneously. To evaluate the effectiveness of coupling for different materials, a Yang coupling factor (k C,Q) is defined in terms of transferred charge, where BNT has the largest k C,Q of 1.29 during heating, indicating that BNT has the best coupling enhancement compared to common ferroelectric materials. This new criterion and novel device structure therefore provide a new basis for the future development of coupled nanogenerators which are capable of harvesting multiple sources of energy.</p

Dyslexia-related loci are significantly associated with language and literacy in Chinese–English bilingual Hong Kong Chinese twins

This study was partially funded by the Research Grants Council of the Hong Kong Special Administration Region (C4054-17WF) and the Theme-based Research Scheme from the Hong Kong Special Administrative Region Research Grants Council (T44-410/21-N).A recent genome-wide association study on dyslexia in 51,800 affected European adults and 1,087,070 controls detected 42 genome-wide significant single nucleotide variants (SNPs). The association between rs2624839 in SEMA3F and reading fluency was replicated in a Chinese cohort. This study explores the genetic overlap between Chinese and English word reading, vocabulary knowledge and spelling, and aims at replicating the association in a unique cohort of bilingual (Chinese–English) Hong Kong Chinese twins. Our result showed an almost complete genetic overlap in vocabulary knowledge (r2 = 0.995), and some genetic overlaps in word reading and spelling (r2 = 0.846, 0.687) across the languages. To investigate the region near rs2624839, we tested proxy SNPs (rs1005678, rs12632110 and rs12494414) at the population level (n = 305–308) and the within-twin level (n = 342–344 [171–172 twin pairs]). All the three SNPs showed significant associations with quantitative Chinese and English vocabulary knowledge (p PostprintPeer reviewe