Algorithm and Architecture for Path Metric Aided Bit-Flipping Decoding of Polar Codes

Polar codes attract more and more attention of researchers in recent years, since its capacity achieving property. However, their error-correction performance under successive cancellation (SC) decoding is inferior to other modern channel codes at short or moderate blocklengths. SC-Flip (SCF) decoding algorithm shows higher performance than SC decoding by identifying possibly erroneous decisions made in initial SC decoding and flipping them in the sequential decoding attempts. However, it performs not well when there are more than one erroneous decisions in a codeword. In this paper, we propose a path metric aided bit-flipping decoding algorithm to identify and correct more errors efficiently. In this algorithm, the bit-flipping list is generated based on both log likelihood ratio (LLR) based path metric and bit-flipping metric. The path metric is used to verify the effectiveness of bit-flipping. In order to reduce the decoding latency and computational complexity, its corresponding pipeline architecture is designed. By applying these decoding algorithms and pipeline architecture, an improvement on error-correction performance can be got up to 0.25dB compared with SCF decoding at the frame error rate of $10^{-4}$, with low average decoding latency.Comment: 6 pages, 6 figures, IEEE Wireless Communications and Networking Conference (2019 WCNC

Activation of Protein Serine/Threonine Phosphatase PP2Cα Efficiently Prevents Liver Fibrosis

Over-activation of TGFβ signaling pathway and uncontrolled cell proliferation of hepatic stellate cells (HSCs) play pivotal roles in liver fibrogenesis, while the protein serine/threonine phosphatase PP2Cα was reported to negatively regulate TGFβ signaling pathway and cell cycle. Our study aimed to investigate the role of PP2Cα in liver fibrogenesis.The effects of PP2Cα activation on liver fibrosis were investigated in human HSCs and primary rat HSCs in vitro using western blotting, real-time PCR, nuclear translocation, cell viability and cell cycle analyses. The antifibrogenic effects in carbon tetrachloride (CCl(4))- and bile duct ligation (BDL)-induced mice in vivo were assessed using biochemical, histological and immunohistochemical analyses. The results demonstrated that activation of PP2Cα by overexpression or the new discovered small molecular activator NPLC0393 terminated TGFβ-Smad3 and TGFβ-p38 signaling pathways, induced cell cycle arrest in HSCs and decreased α-smooth muscle actin (α-SMA) expression, collagen deposition and hepatic hydroxyproline (HYP) level in CCl(4)- and BDL-induced mice.Our findings suggested that PP2Cα activation might be an attractive new strategy for treating liver fibrosis while the small molecular activator NPLC0393 might represent a lead compound for antifibrogenic drug development. Moreover, our study might provide the first evidence for the role of PP2C family members in the fibrotic disease

Robot Fleet Learning via Policy Merging

Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with ease. At the same time, teams of robots should co-acquire diverse skills through their heterogeneous experiences in varied settings. How can we enable such fleet-level learning without having to transmit or centralize fleet-scale data? In this paper, we investigate policy merging (PoMe) from such distributed heterogeneous datasets as a potential solution. To efficiently merge policies in the fleet setting, we propose FLEET-MERGE, an instantiation of distributed learning that accounts for the permutation invariance that arises when parameterizing the control policies with recurrent neural networks. We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment, with good performance on nearly all training tasks at test time. Moreover, we introduce a novel robotic tool-use benchmark, FLEET-TOOLS, for fleet policy learning in compositional and contact-rich robot manipulation tasks, to validate the efficacy of FLEET-MERGE on the benchmark.Comment: See the code https://github.com/liruiw/Fleet-Tools for more detail

NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis

Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors. In this work, we introduce SPARTN (Synthetic Perturbations for Augmenting Robot Trajectories via NeRF): a fully-offline data augmentation scheme for improving robot policies that use eye-in-hand cameras. Our approach leverages neural radiance fields (NeRFs) to synthetically inject corrective noise into visual demonstrations, using NeRFs to generate perturbed viewpoints while simultaneously calculating the corrective actions. This requires no additional expert supervision or environment interaction, and distills the geometric information in NeRFs into a real-time reactive RGB-only policy. In a simulated 6-DoF visual grasping benchmark, SPARTN improves success rates by 2.8$\times$ over imitation learning without the corrective augmentations and even outperforms some methods that use online supervision. It additionally closes the gap between RGB-only and RGB-D success rates, eliminating the previous need for depth sensors. In real-world 6-DoF robotic grasping experiments from limited human demonstrations, our method improves absolute success rates by $22.5\%$ on average, including objects that are traditionally challenging for depth-based methods. See video results at \url{https://bland.website/spartn}

SENP3 Aggravates Renal Tubular Epithelial Cell Apoptosis in Lipopolysaccharide-Induced Acute Kidney Injury via deSUMOylation of Drp1

Background: Sepsis causes acute kidney injury (AKI) in critically ill patients, although the mechanisms underlying the pathophysiology are not fully understood. SUMO-specific proteases 3 (SENP3), a member of the deSUMOylating enzyme family, is known as a redox sensor and could regulate multiple cellular signaling pathways. However, the role of SENP3 in septic AKI remains unclear. Objectives: The purpose of this study was to investigate the role of SENP3 in lipopolysaccharide (LPS)-induced AKI model. Methods: C57BL/6 mice were given intraperitoneal injection of LPS (10 mg/kg). NRK-52E cells were treated with LPS in vitro. The SENP3 protein expression was analyzed by Western blotting. The levels of reactive oxygen species (ROS) in cells were measured using DCFH-DA. SENP3-siRNA or SENP3-plasmid was, respectively, transfected into NRK-52E cells to knock down or overexpress the SENP3 expression. Western blotting was performed to analyze the protein expression of cleaved caspase 3, cytochrome c, and dynamin-related protein 1 (Drp1). The mitochondrial membrane potential was measured using JC-1 assay kit. Co-immunoprecipitation was used to determine the interaction of Drp1 and SMUO2/3. Results: SENP3 protein expression was obviously increased in renal tissues from the mouse model of LPS-induced AKI. Accordingly, SENP3 expression was upregulated in NRK-52E cells treated with LPS in a ROS-dependent manner in vitro. Knockdown of SENP3 dramatically ameliorated LPS-induced apoptosis of NRK-52E cells, whereas overexpression of SENP3 further aggravated LPS-induced apoptosis of NRK-52E cells. Mechanistically, SENP3 triggered Drp1 recruitment to mitochondria by increasing the deSUMOylation of Drp1. Conclusion: SENP3 aggravated renal tubular epithelial cell apoptosis in LPS-induced AKI via Drp1 deSUMOylation manner

GenSim: Generating Robotic Simulation Tasks via Large Language Models

Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, existing methods for data generation have generally focused on scene-level diversity (e.g., object instances and poses) rather than task-level diversity, due to the human effort required to come up with and verify novel tasks. This has made it challenging for policies trained on simulation data to demonstrate significant task-level generalization. In this paper, we propose to automatically generate rich simulation environments and expert demonstrations by exploiting a large language models' (LLM) grounding and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed generation, wherein a target task is given to the LLM and the LLM proposes a task curriculum to solve the target task, and exploratory generation, wherein the LLM bootstraps from previous tasks and iteratively proposes novel tasks that would be helpful in solving more complex tasks. We use GPT4 to expand the existing benchmark by ten times to over 100 tasks, on which we conduct supervised finetuning and evaluate several LLMs including finetuned GPTs and Code Llama on code generation for robotic simulation tasks. Furthermore, we observe that LLMs-generated simulation programs can enhance task-level generalization significantly when used for multitask policy training. We further find that with minimal sim-to-real adaptation, the multitask policies pretrained on GPT4-generated simulation tasks exhibit stronger transfer to unseen long-horizon tasks in the real world and outperform baselines by 25%. See the project website (https://liruiw.github.io/gensim) for code, demos, and videos.Comment: See our project website (https://liruiw.github.io/gensim), demo and datasets (https://huggingface.co/spaces/Gen-Sim/Gen-Sim), and code (https://github.com/liruiw/GenSim) for more detail

The impact of long-term care insurance in China on beneficiaries and caregivers: A systematic review

Background China’s long-term care insurance (LTCI) policy has been minimally evaluated. This systematic review aimed to assess the impact of China’s LTCI pilot on beneficiaries and their caregivers. Methods This review is based on a search of peer-reviewed studies in English (Embase, MEDLINE, Web of Science) and Chinese (China National Knowledge Infrastructure [CNKI], VIP, Wanfang) databases from January 2016 through July 2020, with all studies published in English or Chinese included. We included quantitative analyses of beneficiary-level data that assessed the impact of LTCI on beneficiaries and their caregivers, with no restriction placed on the outcomes studied. Results Nine studies met our inclusion criteria. One study was a randomised trial and two used quasi-experimental approaches. Four studies examined LTCI’s effect on beneficiaries’ quality of life, physical pain, and health service utilisation; one study reported the effect on beneficiaries’ healthcare expenditures; and one study evaluated the impact on caregivers’ care tasks. These studies generally found LTCI to be associated with an improvement in patients’ quality of life (including decreased physical pain), a reduction in the number of outpatient visits and hospitalisations, decreased patient-level health expenditures (e.g. one study reported a reduction in the length of stay, inpatient expenditures, and health insurance expenditures in tertiary hospitals by 41.0%, 17.7%, and 11.4%, respectively), and reduced informal care tasks for caregivers. In addition, four out of four studies that evaluated this outcome found that beneficiaries’ overall satisfaction with LTCI was high. Conclusion The current evidence base for the effects of LTCI in China on beneficiaries and their caregivers is sparse. Nonetheless, the existing studies suggest that LTCI has positive effects on beneficiaries and their caregivers. Further rigorous research on the impacts of LTCI in China is needed to inform the future expansion of the program