Products of Farey graphs are totally geodesic in the pants graph

We show that for a surface S, the subgraph of the pants graph determined by fixing a collection of curves that cut S into pairs of pants, once-punctured tori, and four-times-punctured spheres is totally geodesic. The main theorem resolves a special case of a conjecture made by Aramayona, Parlier, and Shackleton and has the implication that an embedded product of Farey graphs in any pants graph is totally geodesic. In addition, we show that a pants graph contains a convex n-flat if and only if it contains an n-quasi-flat.Comment: v2: 25 pages, 16 figures. Completely rewritten, several figures added for clarit

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.Comment: Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W) on Explainable Computer Vision, 201

Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction

Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes. This is a major shortcoming that prevents the widespread application of deep learning to domains with regulatory processes such as finance. As such, industries such as finance have to rely on traditional models like decision trees that are much more interpretable but less effective than deep learning for complex problems. In this paper, we propose CLEAR-Trade, a novel financial AI visualization framework for deep learning-driven stock market prediction that mitigates the interpretability issue of deep learning methods. In particular, CLEAR-Trade provides a effective way to visualize and explain decisions made by deep stock market prediction models. We show the efficacy of CLEAR-Trade in enhancing the interpretability of stock market prediction by conducting experiments based on S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can provide significant insight into the decision-making process of deep learning-driven financial models, particularly for regulatory processes, thus improving their potential uptake in the financial industry

An experimental and finite element study of the low-cycle fatigue failure of a galvanised steel lighting column

This paper presents the results of a low-cycle fatigue test on a lighting column. The wind induced vibration phenomena responsible for low cycle fatigue in such structures is discussed and the failure mechanism is examined. It was initially thought that poor quality weld detail was the major influence on the fatigue life of such columns. However, the significant role of the galvanised coating in the failure process is also highlighted. The experimental results are compared with those from a detailed 3D finite element model. Various methods of calculating hot-spot stresses at welded joints are examined and use of a simple peak stress removal approach is shown to produce significantly different values compared with the other methods examined

Performance of the resurfaced hip. Part 1: the influence of the prosthesis size and positioning on the remodelling and fracture of the femoral neck

Hip resurfacing is an established treatment for osteoarthritis in young active patients. Failure modes include femoral neck fracture and prosthesis loosening, which may be associated with medium-term bone adaptation, including femoral neck narrowing and densification around the prosthesis stem.Finite element modelling was used to indicate the effects of prosthesis sizing and positioning on the bone remodelling and fracture strength under a range of normal and traumatic loads, with the aim of understanding these failure modes better.The simulations predicted increased superior femoral neck stress shielding in young patients with small prostheses, which required shortening of the femoral neck to give an acceptable implant–bone interface. However, with a larger prosthesis, natural femoral head centre recreation in the implanted state was possible; therefore stress shielding was restricted to the prosthesis interior, and its extent was less sensitive to prosthesis orientation. With valgus orientation, the implanted neck strength was, at worst, within 3 per cent of its intact strength.The study suggests that femoral neck narrowing may be linked to a reduction in the horizontal femoral offset, occurring if the prosthesis is excessively undersized. As such, hip resurfacing should aim to reproduce the natural femoral head centre, and, for valgus prosthesis orientation, to avoid femoral neck fracture

Food Innovation Consultancy Challenge: 'Live' Learning and Professional Development with an Industry Client

Abstract What makes you stand out in the market for that great graduate job? In the competitive market for graduate jobs, securing a good degree no longer sets you apart from other candidates – this workshop will show academics how to improve student employability through 'real life' learning in collaboration with leading UK industry businesses. The Food Innovation Consultancy Challenge is part of Sheffield Business School's 'live' strategic pillar offering students consultancy opportunities collaborating with key industry partners. The module forms one-third of students final year grading and has a significant impact on their degree classification. It is designed to improve professional skills through applied learning from a topical real life challenge. Through seamless teaching it provides integrated, exponential, problem-based and active learning, and develops team working and individual skills to get students 'career ready'. Over eighty students worked with Asda Stores plc on a sugar reduction project on Asda Brand biscuits in line with Public Health England targets to help reduce childhood obesity. Students were presented with a brief by the client and using primary and secondary research, along with commercial evaluations provided recommendations for Asda Bourbons, Custard Cream and Milk Chocolate Digestives. The project allowed students to reflect on their career development and future plans, and by using appropriate models and career management theory provide individual input to a group task. The module included a speed dating session with Asda management and culminated with the students presenting their recommendations at Asda House to an academic and Asda panel. The author would like to share the success of the student experience and recommendations to the client with BAM delegates. This academic model is one that satisfies student and industry requirements by providing work experience and self-reflection, action planning and improving student employability. The module was such a success that it appeared in the press in The Grocer and on the BBC. It has become the focus of 'live' module delivery across the whole of the courses within SBS