Architectural students’ year-out training experience in architectural ofces in the UK

This paper investigates architectural students’ ‘year-out’ learning experiences in architectural offices after completing RIBA Part I study within a UK university. By interviewing and analysing their reflections on the experience, the study examines how individual architecture students perceive and value their learning experience in architectural offices and how students understand and integrate what they have learned through two distinct elements of their training: in university and in offices. The architectural offices that students worked with vary in terms of workforce size and projects undertaken. The students’ training experience is not unified. The processes of engaging with concrete situations in real projects may permit students to follow opportunities that most inspire them and to develop their differing expertise, but their development in offices can also be restricted by the vicissitudes of market economics. This study has demonstrated that architectural students’ learning and development in architectural offices continued through ‘learning by doing’ and used drawings as primary design and communicative media. Working in offices gave weight to both explicit and tacit knowledge and used subjective judgments. A further understanding was also achieved about what architects are and what they do in practice. The realities of their architectural practice experience discouraged some Part I students from progressing into the next stage of architectural education, Part II, but for others it demonstrated that a career in architecture was ‘achievable’. This study argues that creative design, practical and technical abilities are not separate skill-sets that are developed in the university and in architectural offices respectively. They are linked and united in the learning process required to become a professional architect. The study also suggests that education in the university should do more to prepare students for their training in practice. Yun Gao is an architect and Senior Lecturer in the School of Art, Design, and Architecture at the University of Huddersfield. After earning a PhD from the University of Edinburgh in 1998, she practiced architecture in Bristol. Her research has explored teaching and learning in architectural education. Kevin Orr has been Senior Lecturer in the School of Education and Professional Development at the University of Huddersfield since 2006 where his research has mainly focused on work-based learning and professional development of teachers in the lifelong learning and skills sector

Architectural students' year-out training experience in architectal offices in the UK

This paper investigates architectural students’ ‘year-out’ learning experiences in architectural offices after completing RIBA Part I study within a UK university. By interviewing and analysing their reflections on the experience, the study examines how individual architecture students perceive and value their learning experience in architectural offices and how students understand and integrate what they have learned through two distinct elements of their training: in university and in offices. The architectural offices that students worked with vary in terms of workforce size and projects undertaken. The students’ training experience is not unified. The processes of engaging with concrete situations in real projects may permit students to follow opportunities that most inspire them and to develop their differing expertise, but their development in offices can also be restricted by the vicissitudes of market economics. This study has demonstrated that architectural students’ learning and development in architectural offices continued through ‘learning by doing’ and used drawings as primary design and communicative media. Working in offices gave weight to both explicit and tacit knowledge and used subjective judgments. A further understanding was also achieved about what architects are and what they do in practice. The realities of their architectural practice experience discouraged some Part I students from progressing into the next stage of architectural education, Part II, but for others it demonstrated that a career in architecture was ‘achievable’. This study argues that creative design, practical and technical abilities are not separate skill-sets that are developed in the university and in architectural offices respectively. They are linked and united in the learning process required to become a professional architect. The study also suggests that education in the university should do more to prepare students for their training in practice

Stochastic Answer Networks for Machine Reading Comprehension

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).Comment: 11 pages, 5 figures, Accepted to ACL 201

An experimental study of ultrasonic vibration and the penetration of granular material

This work investigates the potential use of direct ultrasonic vibration as an aid to penetration of granular material. Compared with non-ultrasonic penetration, required forces have been observed to reduce by an order of magnitude. Similarly, total consumed power can be reduced by up to 27%, depending on the substrate and ultrasonic amplitude used. Tests were also carried out in high-gravity conditions, displaying a trend that suggests these benefits could be leveraged in lower gravity regimes

Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field

Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction network is performant among SoTA deep learning methods. In this paper, we rethink dual-domain model design from the perspective of the receptive field, which is needed for image recovery and K-space interpolation problems. Further, we introduce domain-specific modules for dual-domain reconstruction, namely k-space global initialization and image-domain parallel local detail enhancement. We evaluate our modules by translating a SoTA method DuDoRNet under different conventions of MRI reconstruction including image-domain, dual-domain, and reference-guided reconstruction on the public IXI dataset. Our model DuDoRNet+ achieves significant improvements over competing deep learning methods.Comment: 2024 IEEE International Symposium on Biomedical Imaging (ISBI

Artificial Intelligence: The Future of Sustainable Agriculture? A Research Agenda

Global warming and the increasing food demand are problems of the current generation and require a change towards sustainable agriculture. In recent years, research in the field of artificial intelligence has made considerable progress. Thus, the use of artificial intelligence in agriculture can be a promising solution to ensure sufficient food supply on a global scale. To investigate the state-of-the-art in the use of artificial intelligence-based systems in agriculture, we provide a structured literature review. We show that research has been done in the field of irrigation and plant growth. In this regard, camera systems often provide images as training/input data for artificial intelligence-based systems. Finally, we provide a research agenda to pave the way for further research on the use of artificial intelligence in sustainable agriculture

The Benefits of Label-Description Training for Zero-Shot Text Classification

Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific label sets in downstream tasks. We propose a simple way to further improve zero-shot accuracies with minimal effort. We curate small finetuning datasets intended to describe the labels for a task. Unlike typical finetuning data, which has texts annotated with labels, our data simply describes the labels in language, e.g., using a few related terms, dictionary/encyclopedia entries, and short templates. Across a range of topic and sentiment datasets, our method is more accurate than zero-shot by 17-19% absolute. It is also more robust to choices required for zero-shot classification, such as patterns for prompting the model to classify and mappings from labels to tokens in the model's vocabulary. Furthermore, since our data merely describes the labels but does not use input texts, finetuning on it yields a model that performs strongly on multiple text domains for a given label set, even improving over few-shot out-of-domain classification in multiple settings.Comment: Accepted at the EMNLP 2023 main conference (long paper