Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 11 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available

Orbital angular momentum modes emission from a silicon photonic integrated device for km-scale data-carrying fiber transmission

We experimentally demonstrate orbital angular momentum (OAM) modes emission from a high emission efficiency OAM emitter for 20-Gbit/s quadrature phase-shift keying (QPSK) carrying data transmission in few-mode fiber (FMF). The device is capable of emitting vector optical vortices carrying well-defined OAM efficiently with the efficiency of the device >37%. Seven modes propagate through a 2-km two-mode and a 3.6-km three-mode FMF with measured optical signal-to-noise ratio (OSNR) penalties less than 4 dB at a bit-error rate (BER) of 2 x 10(-3). The demonstrations with favorable performance pave the way to incorporate silicon photonic integrated devices as transceivers in an OAM-enabled optical fiber communication link. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

New insights into the cortex-to-stele ratio show it to effectively indicate inter- and intraspecific function in the absorptive roots of temperate trees

The cortex-to-stele ratio (CSR), as it increases from thin- to thick-root species in angiosperms, is theorised to effectively reflect a compensation for the ‘lag’ of absorption behind transportation. But it is still not known if this compensatory effect exists in gymnosperm species or governs root structure and function within species. Here, anatomical, morphological, and tissue chemical traits of absorptive roots were measured in three temperate angiosperm and three gymnosperm species. Differences in the CSR and the above functional traits, as well as their intraspecific associations, were analyzed and then compared between angiosperms and gymnosperms. At the intraspecific level, the CSR decreased with increasing root order for all species. The expected functional indication of the CSR was consistent with decreases in specific root length (SRL) and N concentration and increases in the C to N ratio (C:N ratio) and the number of and total cross-sectional area of conduits with increasing root order, demonstrating that the CSR indicates the strength of absorption and transportation at the intraspecific level, but intraspecific changes are due to root development rather than the compensatory effect. These trends resulted in significant intraspecific associations between the CSR and SRL (R2 = 0.36 ~ 0.80), N concentration (R2 = 0.48 ~ 0.93), the C:N ratio (R2 = 0.47 ~ 0.91), and the number of (R2 = 0.21 ~ 0.78) and total cross-sectional area (R2 = 0.29 ~ 0.72) of conduits in each species (p< 0.05). The overall mean CSR of absorptive roots in angiosperms was four times greater than in gymnosperms, and in angiosperms, the CSR was significantly higher in thick- than in thin-rooted species, whereas in gymnosperms, the interspecific differences were not significant (p > 0.05). This suggests that the compensation for the lag of absorption via cortex thickness regulation was stronger in three angiosperm species than in three gymnosperm species. In addition, there was poor concordance between angiosperms and gymnosperms in the relationships between CSRs and anatomical, morphological, and tissue chemical traits. However, these gymnosperm species show a more stable intraspecific functional association compared to three angiosperm species. In general, absorptive root CSRs could manifest complex strategies in resource acquisition for trees at both intra- and interspecific levels

A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification

In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC). We employ pre-trained networks trained only on image data sets to extract video embedding; whereas for audio embedding models, we decide to train them from scratch. We explore different neural network architectures for joint modeling to effectively combine the video and audio modalities. Moreover, data augmentation strategies are investigated to increase audio-visual training set size. For the video modality the effectiveness of several operations in RandAugment is verified. An audio-video joint mixup scheme is proposed to further improve AVSC performances. Evaluated on the development set of TAU Urban Audio Visual Scenes 2021, our final system can achieve the best accuracy of 94.2% among all single AVSC systems submitted to DCASE 2021 Task 1b.Comment: 5 pages, 1 figure, submitted to INTERSPEECH 202

A new approach to overcoming antibiotic-resistant bacteria: Traditional Chinese medicine therapy based on the gut microbiota

With the irrational use of antibiotics and the increasing abuse of oral antibiotics, the drug resistance of gastrointestinal pathogens has become a prominent problem in clinical practice. Gut microbiota plays an important role in maintaining human health, and the change of microbiota also affects the activity of pathogenic bacteria. Interfering with antibiotic resistant bacteria by affecting gut microbiota has also become an important regulatory signal. In clinical application, due to the unique advantages of traditional Chinese medicine in sterilization and drug resistance, it is possible for traditional Chinese medicine to improve the gut microbial microenvironment. This review discusses the strategies of traditional Chinese medicine for the treatment of drug-resistant bacterial infections by changing the gut microenvironment, unlocking the interaction between microbiota and drug resistance of pathogenic bacteria

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. We propose CodeApex, a bilingual benchmark dataset focusing on the programming comprehension and code generation abilities of LLMs. CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks. Additionally, CodeApex utilizes algorithmic questions and corresponding test cases to assess the code quality generated by LLMs. We evaluate 14 state-of-the-art LLMs, including both general-purpose and specialized models. GPT exhibits the best programming capabilities, achieving approximate accuracies of 50% and 56% on the two tasks, respectively. There is still significant room for improvement in programming tasks. We hope that CodeApex can serve as a reference for evaluating the coding capabilities of LLMs, further promoting their development and growth. Datasets are released at https://github.com/APEXLAB/CodeApex.git. CodeApex submission website is https://apex.sjtu.edu.cn/codeapex/.Comment: 21 page