The Necessity of Hot Metal Desiliconization Process

AbstractThe technology of hot metal pretreatment had tremendous improvements in past decades and was widely used by the overwhelming majority of steelworks in Japan. While, since the first hot metal pretreatment station was established in Taiyuan Iron and Steel Co., Ltd in the later period of 1980s, this process began to be applied in domestic steel plants. And now 30 years have passed, the desilionization and dephosphorization process are still rarely seldom used in China except desulphurization process in most carbon steel plants. So in this paper, the metallurgical principles and effects of hot metal desiliconization were analyzed in great details. Meanwhile, the optimum silicon content of hot metal between iron-making and steelmaking process was summarized and calculated. The necessity of hot metal desiliconizaiton was discussed for the iron and steel companies in China

DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text summarization). Multi-task training commonly receives input sequences of highly different lengths due to the diverse contexts of different tasks. Padding (to the same sequence length) or packing (short examples into long sequences of the same length) is usually adopted to prepare input samples for model training, which is nonetheless not space or computation efficient. This paper proposes a dynamic micro-batching approach to tackle sequence length variation and enable efficient multi-task model training. We advocate pipeline-parallel training of the large model with variable-length micro-batches, each of which potentially comprises a different number of samples. We optimize micro-batch construction using a dynamic programming-based approach, and handle micro-batch execution time variation through dynamic pipeline and communication scheduling, enabling highly efficient pipeline training. Extensive evaluation on the FLANv2 dataset demonstrates up to 4.39x higher training throughput when training T5, and 3.25x when training GPT, as compared with packing-based baselines. DynaPipe's source code is publicly available at https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines.Comment: 18 pages, 18 figure

Sensitivity of Space-based Gravitational-Wave Interferometers to Ultralight Bosonic Fields and Dark Matter

Ultralight bosonic fields (ULBFs) are predicted by various theories beyond the standard model of particle physics and are viable candidates of cold dark matter. There have been increasing interests to search for the ULBFs in physical and astronomical experiments. In this paper, we investigate the sensitivity of several planned space-based gravitational-wave interferometers to ultralight scalar and vector fields. Using time-delay interferometry (TDI) to suppress the overwhelming laser frequency noise, we derive the averaged transfer functions of different TDI combinations to scalar and vector fields, and estimate the impacts of bosonic field's velocities. We obtain the sensitivity curves for LISA, Taiji and TianQin, and explore their projected constraints on the couplings between ULBFs and standard model particles, illustrating with the ULBFs as dark matter.Comment: 33 pages, 8 figure