Designing an interactive laboratory experiment to investigate buoyancy

Laboratory classes provide a visual and practical way of supplementing traditional teaching through lectures and tutorial classes. A criticism of laboratories in our School is that they are largely based on demonstration with insufficient participation by students. This provided the motivation to create a new laboratory experiment which would be interactive, encourage student enthusiasm with the subject and improve the quality of student learning.

The topic of the laboratory is buoyancy. While this is a key topic in the first-year fluids module, the laboratory has been designed in such a way that prior knowledge of the topic is unnecessary and therefore it would be accessible by secondary school pupils. The laboratory climaxes in a design challenge. However, it begins with a simple task involving students identifying some theoretical background information using given websites. They then have to apply their knowledge by developing some equations. Next, given some materials (a sheet of tinfoil, card and blu-tack), they have to design a vessel to carry the greatest mass without sinking. Thus, they are given an open-ended problem and have to provide a mathematical justification for their design. Students are expected to declare the maximum mass for their boat in advance of it being tested to create a sense of competition and fun. Overall, the laboratory involves tasks which begin at a low level and progressively get harder, incorporating understanding, applying, evaluating and designing (with reference to Bloom’s taxonomy).

The experiment has been tested in a modern laboratory with wall-mounted screens and access to the internet. Students enjoyed the hands-on aspect and thought the format helped their learning.

The use of cheap materials which are readily available means that many students can be involved at one time. Support documentation has been produced, both for the student participants and the facilitator. The latter is given advice on how to guide the students (without simply giving them the answer) and given some warning about potential problems the students might have.

The authors believe that the laboratory can be adapted for use by secondary school pupils and hope that it will be used to promote engineering in an engaging and enthusing way to a wider audience. To this end, contact has already been made with the Widening Participation Unit at the University to gain advice on possible next steps.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.