Numerical Study of Laterally Loaded Batter Pile Groups with the Application of Anisotropic Modified Cam-Clay Model

This study presents a series of numerical studies of laterally loaded batter pile groups based on data of the full-scale lateral load test on M19 eastbound pier foundation of the new I-10 Twin Span Bridge, Louisiana. The numerical studies include several continuum-based 3D finite element analyses on batter/vertical pile/pile groups and a FB-MultiPier analysis of the pile foundation. The Anisotropic Modified Cam Clay Model, has been implemented into UMAT and applied for describing clay behavior in all FE models. The explicit substepping scheme with modified Euler algorithm is selected to implement the model in ABAQUS software. The resultant UMAT shows good accuracy compared to the ABAQUS in-built Modified Cam Clay model. Also it exhibits wonderful computational stability and efficiency in the pile group analyses, which greatly accelerated the whole research processes. The results of FE analyses were compared with the measured field data from lateral load test and those predicted by FB-MultiPier. All of them showing good agreement on lateral deformation profiles and bending moment profiles. The comparison of the lateral deflection, bending moment, soil resistance and lateral/ vertical load distributions between different spacing batter/ vertical pile groups and single isolated pile illustrate that small spacing and the vertical piles will produce intensified group effect. The concept of “trapezoidal zone” is firstly proposed to explain the axial load distribution pattern of batter pile group foundation. An additional coupled pore fluid diffusion and stress analysis on a single pile model demonstrated that the resultant excessive pore pressure caused by lateral loads has limited influence on the result of FE analyses

Decoupled Model Schedule for Deep Learning Training

Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off between usability and performance. On one hand, DL frameworks such as PyTorch use dynamic graphs to facilitate model developers at a price of sub-optimal model training performance. On the other hand, practitioners propose various approaches to improving the training efficiency by sacrificing some of the flexibility, ranging from making the graph static for more thorough optimization (e.g., XLA) to customizing optimization towards large-scale distributed training (e.g., DeepSpeed and Megatron-LM). In this paper, we aim to address the tension between usability and training efficiency through separation of concerns. Inspired by DL compilers that decouple the platform-specific optimizations of a tensor-level operator from its arithmetic definition, this paper proposes a schedule language to decouple model execution from definition. Specifically, the schedule works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing. Compared to existing optimization solutions, we optimize the model as-needed through high-level primitives, and thus preserving programmability and debuggability for users to a large extent. Our evaluation results show that by scheduling the existing hand-crafted optimizations in a systematic way, we are able to improve training throughput by up to 3.35x on a single machine with 8 NVIDIA V100 GPUs, and by up to 1.32x on multiple machines with up to 64 GPUs, when compared to the out-of-the-box performance of DeepSpeed and Megatron-LM

Sequential Manipulation Planning on Scene Graph

We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning. Augmented with predicate-like attributes, this contact graph-based representation abstracts scene layouts with succinct geometric information and valid robot-scene interactions. Goal configurations, naturally specified on contact graphs, can be produced by a genetic algorithm with a stochastic optimization method. A task plan is then initialized by computing the Graph Editing Distance (GED) between the initial contact graphs and the goal configurations, which generates graph edit operations corresponding to possible robot actions. We finalize the task plan by imposing constraints to regulate the temporal feasibility of graph edit operations, ensuring valid task and motion correspondences. In a series of simulations and experiments, robots successfully complete complex sequential object rearrangement tasks that are difficult to specify using conventional planning language like Planning Domain Definition Language (PDDL), demonstrating the high feasibility and potential of robot sequential task planning on contact graph.Comment: 8 pages, 6 figures. Accepted by IROS 202

A dynamic emotion recognition system based on convolutional feature extraction and recurrent neural network

Over the past three decades, there has been sustained research activity in emotion recognition from faces, powered by the popularity of smart devices and the development of improved machine learning, resulting in the creation of recognition systems with high accuracy. While research has commonly focused on single images, recent research has also made use of dynamic video data. This paper presents CNN-RNN (Convolutional Neural Network - Recurrent Neural Network) based emotion recognition using videos from the ADFES database, and we present the results in the arousal-valence space, rather than assigning a discrete emotion. As well as traditional performance metrics, we also design a new performance metric, PN accuracy, to distinguish between positive and negative emotions. We demonstrate improved performance with a smaller RNN than the initial pre-trained model, and report a peak accuracy of 0.58, with peak PN accuracy of 0.76, which shows our approach is very capable distinguishing between positive and negative emotions. We also present a detailed analysis of system performance, using new valence-arousal domain temporal visualisations to show transitions in recognition over time, demonstrating the importance of context based information in emotion recognition

OceanGPT: A Large Language Model for Ocean Science Tasks

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website: https://zjunlp.github.io/project/OceanGPT

Emission accounting and drivers in East African countries

East Africa is typical of the less developed economies that have emerged since the 21st century, whose brilliant economic miracle has also triggered the rapid growth of energy consumption and carbon dioxide emissions. However, previous carbon accounting studies have never focused on the region. Based on multi-source data, this paper rebuilt the 45-sectors carbon emission inventories of eight East African countries from 2000 to 2017, and used index decomposition analysis to quantify the drivers of growth. Here we found that overall the CO2 emissions show a 'two-stage exponential growth' pattern, with significant heterogeneity between countries. In terms of the energy mix, technical progress in hydro and geothermal energy was almost offset by a growing appetite for oil and coal, making it the weak and valuable factor driving emissions reduction (−1.4Mt). But it was far from enough to overcome the pressure of economic and population growth, which brought about a 13Mt and 11Mt emission growth respectively from 2000 to 2017. Increasing energy intensity due to industrialization and transport development also contributed to an increment of 6.4Mt. Low-carbon policies should be tailored to local conditions and targeted at the improvement of energy efficiency and use of renewable energy so as to achieve a win-win situation between sustainable economic growth and emission reduction

Recent strategies for constructing efficient interfacial solar evaporation systems

Interfacial solar evaporation (ISE) is a promising technology to relieve worldwide freshwater shortages owing to its high energy conversion efficiency and environmentally sustainable potential. So far, many innovative materials and evaporators have been proposed and applied in ISE to enable highly controllable and efficient solar-to-thermal energy conversion. With rational design, solar evaporators can achieve excellent energy management for lowering energy loss, harvesting extra energy, and efficiently utilizing energy in the system to improve freshwater production. Beyond that, a strategy of reducing water vaporization enthalpy by introducing molecular engineering for water-state regulation has also been demonstrated as an effective approach to boost ISE. Based on these, this article discusses the energy nexus in two-dimensional (2D) and three-dimensional (3D) evaporators separately and reviews the strategies for design and fabrication of highly efficient ISE systems. The summarized work offers significant perspectives for guiding the future design of ISE systems with efficient energy management, which pave pathways for practical applications