Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

Overexpression of a Water-Forming NADH Oxidase Improves the Metabolism and Stress Tolerance of Saccharomyces cerevisiae in Aerobic Fermentation

Recognising that the world into which students emerge upon graduation is characterised by constant change, we embrace a critical pedagogy that can be implemented in the classroom through the use of freehand drawing. Freehand drawing is a technique that can stimulate a critical stance, as visual representations allow us to comprehend the world differently, while permitting us see how others understand the world. First year students, in their first lecture, were asked to draw their interpretations of Irish politics and to explain in writing what they had drawn. The students were then placed in groups and asked to note what they saw in each other’s drawings, allowing for the identification of general patterns and themes. In this context, freehand drawing facilitates our ability to: ‘see’ how we understand a topic and that there are multiple ways of understanding; test theories, orthodoxies and accepted truths; scrutinise tacit assumptions; and ponder other possibilities. In employing freehand drawing in this manner, our aim is to create a learning environment where students develop their capacity for critical self-reflection

DBS: Dynamic Batch Size For Distributed Deep Neural Network Training

Synchronous strategies with data parallelism, such as the Synchronous StochasticGradient Descent (S-SGD) and the model averaging methods, are widely utilizedin distributed training of Deep Neural Networks (DNNs), largely owing to itseasy implementation yet promising performance. Particularly, each worker ofthe cluster hosts a copy of the DNN and an evenly divided share of the datasetwith the fixed mini-batch size, to keep the training of DNNs convergence. In thestrategies, the workers with different computational capability, need to wait foreach other because of the synchronization and delays in network transmission,which will inevitably result in the high-performance workers wasting computation.Consequently, the utilization of the cluster is relatively low. To alleviate thisissue, we propose the Dynamic Batch Size (DBS) strategy for the distributedtraining of DNNs. Specifically, the performance of each worker is evaluatedfirst based on the fact in the previous epoch, and then the batch size and datasetpartition are dynamically adjusted in consideration of the current performanceof the worker, thereby improving the utilization of the cluster. To verify theeffectiveness of the proposed strategy, extensive experiments have been conducted,and the experimental results indicate that the proposed strategy can fully utilizethe performance of the cluster, reduce the training time, and have good robustnesswith disturbance by irrelevant tasks. Furthermore, rigorous theoretical analysis hasalso been provided to prove the convergence of the proposed strategy.Comment: The latest version of this article has been accepted by IEEE TETC

Overexpression of THI4 and HAP4 Improves Glucose Metabolism and Ethanol Production in Saccharomyces cerevisiae

Redox homeostasis is essential to the maintenance of cell metabolism. Changes in the redox state cause global metabolic and transcriptional changes. Our previous study indicated that the overexpression of NADH oxidase in Saccharomyces cerevisiae led to increased glucose consumption and ethanol production. Gene expression related to thiamine synthesis and osmotolerance as well as HAP4 expression was increased in response to redox change caused by the overexpression of NADH oxidase. To identify detailed relationships among cofactor levels, thiamine synthesis, expression of HAP4, and osmotolerance, and to determine whether these changes are interdependent, THI4 and HAP4 were overexpressed in S. cerevisiae BY4741. The glucose consumption rate of THI4-overexpressing strain (thi4-OE) was the highest, followed by HAP4-overexpressing strain (hap4-OE) > NADH oxidase-overexpressing strain (nox-OE) > control strain (con), while strain hap4-OE showed the highest concentration of ethanol after 26 h of fermentation. Reduced glycerol production and increased osmotolerance were observed in thi4-OE and hap4-OE, as well as in nox-OE. HAP4 globally regulated thiamine synthesis, biomass synthesis, respiration, and osmotolerance of cells, which conferred the recombinant strain hap4-OE with faster glucose metabolism and enhanced stress resistance. Moreover, overexpression of HAP4 might extend the life span of cells under caloric restriction by lowering the NADH level. Although overexpression of THI4 and HAP4 induced various similar changes at both the metabolic and the transcriptional level, the regulatory effect of THI4 was more limited than that of HAP4, and was restricted to the growth phase of cells. Our findings are expected to benefit the bio-ethanol industry

Is A 15-minute City within Reach in the United States? An Investigation of Activity-Based Mobility Flows in the 12 Most Populous US Cities

Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The innovative concept of the 15-minute city, with a focus on proximity-based planning, holds promise in minimizing unnecessary travel and advancing the progress toward achieving carbon neutrality. However, an important research question that remains insufficiently explored is: to what extent is a 15-minute city concept within reach for US cities? This paper establishes a comprehensive framework to evaluate the 15-minute city concept using SafeGraph Point of Interest (POI) check-in data in the 12 most populous US cities. The results reveal that residents are more likely to rely on cars due to the fact that most of their essential activities are located beyond convenient walking, cycling, and public transit distances. However, there is significant potential for the implementation of the 15-minute city concept, as most residents' current activities can be accommodated within a 15-minute radius by the aforementioned low-emission modes of transportation. Our findings can offer policymakers insight into how far US cities are away from the 15-minute city and the potential CO2 emission reduction they can expect if the concept is successfully implemented

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later