Kiwa (Germany)

Abstract Für ein typisches Überführungsbauwerk einer Bundesautobahn wurden im Rahmen einer Machbarkeitsstudie verschiedene alternative Bauweisen miteinander verglichen und hinsichtlich ihrer Ökobilanz bewertet. Neben den heutigen Standardbauweisen in Stahlverbund und Spannbeton wurden auch neue, innovative Lösungen mit Einsatz von Holz, hochfestem bzw. Carbonbeton betrachtet, um deren Potenzial in Bezug auf die Nachhaltigkeit zu analysieren. Die ökobilanzielle Bewertung wurde ergänzend zu technischen und ökonomischen Kriterien herangezogen, um eine ganzheitliche Betrachtung der Varianten zu ermöglichen. Der Vergleich erfolgte für eine konkrete Wirtschaftswegüberführung im Zuge des geplanten Neubaus der A 20 in Schleswig‐Holstein. Für die verschiedenen Varianten wurde durch spezialisierte Fachplaner die technische Lösung entwickelt und dazu die konkreten Mengen ermittelt. Die Bearbeitung erfolgte unter der Maßgabe, die alternativen Varianten baulich zu realisieren, um Erfahrungen in der baupraktischen Umsetzung zu sammeln. Im folgenden Beitrag werden die Varianten in Bezug auf die technische Lösung vorgestellt. Anschließend wird die angewandte Methode der ökobilanziellen Bewertung vorgestellt und auf das Potenzial der neuen Varianten eingegangen.

Scalable training of large models (like BERT and GPT-3) requires careful optimization rooted in model design, architecture, and system capabilities. From a system standpoint, communication has become a major bottleneck, especially on commodity systems with standard TCP interconnects that offer limited network bandwidth. Communication compression is an important technique to reduce training time on such systems. One of the most effective methods is error-compensated compression, which offers robust convergence speed even under 1-bit compression. However, state-of-the-art error compensation techniques only work with basic optimizers like SGD and momentum SGD, which are linearly dependent on the gradients. They do not work with non-linear gradient-based optimizers like Adam, which offer state-of-the-art convergence efficiency and accuracy for models like BERT. In this paper, we propose 1-bit Adam that reduces the communication volume by up to $5\times$, offers much better scalability, and provides the same convergence speed as uncompressed Adam. Our key finding is that Adam's variance (non-linear term) becomes stable (after a warmup phase) and can be used as a fixed precondition for the rest of the training (compression phase). Experiments on up to 256 GPUs show that 1-bit Adam enables up to $3.3\times$ higher throughput for BERT-Large pre-training and up to $2.9\times$ higher throughput for SQuAD fine-tuning. In addition, we provide theoretical analysis for our proposed work.

Generating accurate descriptions for online fashion items is important not only for enhancing customers' shopping experiences, but also for the increase of online sales. Besides the need of correctly presenting the attributes of items, the expressions in an enchanting style could better attract customer interests. The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning. Different from popular work on image captioning, it is hard to identify and describe the rich attributes of fashion items. We seed the description of an item by first identifying its attributes, and introduce attribute-level semantic (ALS) reward and sentence-level semantic (SLS) reward as metrics to improve the quality of text descriptions. We further integrate the training of our model with maximum likelihood estimation (MLE), attribute embedding, and Reinforcement Learning (RL). To facilitate the learning, we build a new FAshion CAptioning Dataset (FACAD), which contains 993K images and 130K corresponding enchanting and diverse descriptions. Experiments on FACAD demonstrate the effectiveness of our model.

Aim To evaluate the climate sensitivity of model-based forest productivity estimates using a continental-scale tree-ring network.

This chapter contains sections titled: Effect of Magnetic Field on Electrodeposition of Nano-sized Copper Clusters DSC Study of the Precipitation Processes in a Cu10Ni3Al Alloy Protection of Copper Surface Against Corrosion by Cationic Surfactant in Seawater Atmospheric Corrosion of Copper in Cold Polar, Moderate, Subtropical and Tropical Climate. The Modelling of Atmospheric Corrosion Rate Development of a New Dezincification-Resistant Leaded Brass Family for hot Forging Prediction of the Long-term Relaxation Behaviour of the Copper-Base Connectic Alloys