A good teacher can adjust the curriculum based on students' learning history. By analogy, in this paper, we study the dynamics of a deep neural network's (DNN) performance on individual samples during its learning process. The observed properties allow us to develop an adaptive curriculum that leads to faster learning of more accurate models. We introduce dynamic instance hardness (DIH), the exponential moving average of a sample's instantaneous hardness (e.g., a loss, or a change in outputs) over the training history. A low DIH indicates that a model retains knowledge about a sample over time, and implies a flat loss landscape for that sample. Moreover, for DNNs, we find that a sample's DIH early in training predicts its DIH in later stages.

This characteristic of DIH might be helpful to detect noisy data. We will discuss it in our future work. We will leave explanation of this phenomenon to our future works. Moreover, we provide a comparison of the smoothness between DIH and instantaneous loss on individual samples in Figure 1. We use cosine annealing learning rate schedule for multiple epochs.

A good teacher can adjust a curriculum based on students' learning history.

A good teacher can adjust a curriculum based on students' learning history.

A good teacher can adjust the curriculum based on students' learning history. By analogy, in this paper, we study the dynamics of a deep neural network's (DNN) performance on individual samples during its learning process. The observed properties allow us to develop an adaptive curriculum that leads to faster learning of more accurate models. We introduce dynamic instance hardness (DIH), the exponential moving average of a sample's instantaneous hardness (e.g., a loss, or a change in outputs) over the training history. A low DIH indicates that a model retains knowledge about a sample over time, and implies a flat loss landscape for that sample. Moreover, for DNNs, we find that a sample's DIH early in training predicts its DIH in later stages.

Then, we describe how hypernymy can be represented in FDS in 3. In 4, we discuss how Functional Distributional Semantics (FDS; Emerson existential and universal quantifications support or and Copestake, 2016; Emerson, 2018) suggests undermine the Distributional Inclusion Hypothesis that the meaning of a word can be modelled as a (DIH), how FDS can handle both quantifications, truth-conditional function, whose parameters can and how FDS models can learn hypernymy under be learnt using the distributional information in a the DIH, and the reverse of it when equipped with corpus (Emerson, 2020a; Lo et al., 2023).

The fast-changing customer demands in modern society seek flexibility, innovation and a rapid response from manufacturers and organisations that, in order to respond to market needs, are creating tools and processes in order to adopt an approach that welcomes change. That approach is found to be Agile Manufacturing – and the Trinity project is the magnet that connects every segment of agile with everyone involved, creating a network that supports people, organisations, production and processes. The main objective of TRINITY is to create a network of multidisciplinary and synergistic local digital innovation hubs (DIHs) composed of research centres, companies, and university groups that cover a wide range of topics that can contribute to agile production: advanced robotics as the driving force and digital tools, data privacy and cyber security technologies to support the introduction of advanced robotic systems in the production processes. The Trinity project is funded by Horizon 2020 the European Union research and innovation programme. Currently, Trinity brings together a network of 16 Digital Innovation Hubs (DIHs) and so far has 37 funded projects with 8.1 million euros in funding.

The Inspection and Maintenance (I&M) Industry represents a large economic activity spanning across multiple sectors such as energy, oil & gas, water supply, transport, civil engineering, and infrastructure. RIMA project aims at bringing together Digital Innovation Hubs and Facilitators operating under a common network that allow them to join forces and competences in promoting I&M robotics in Europe. The BIS Research projects' analysis of the Inspection and Maintenance Robot Industry forecasts that the I&M market will grow at a significant CAGR of 12.73% on the basis of value from 2020 to 2025. In 2019, Europe dominated the 40% of the global inspection and maintenance robot market (BIS322A, Mar 2020). Although the European Union hosts most of the I&M robotics offer – being France, Germany, and Spain (and U.K. until 2021 Brexit) the leading manufacturing countries, there is still a bottleneck connecting this offer to the market and high potential applications.

Robotics4EU is a 3-years-long EU-funded project which advocates for the wider adoption of AI-based robots in 4 sectors: healthcare, inspection and maintenance of infrastructure, agri-food, and agile production. Thus, one of the ways in which Robotics4EU raises awareness about non-technological aspects in robotics is delivering a series of workshops to involve the research community, industry representatives and citizens. Role of Digital Innovation Hubs (DIHs) in I&M Robotics" which took place on the 23rd of February, 2022 analysed the role and contribution of Digital Innovation Hubs (DIHs) to the widespread adoption of robotics in society. How can they enhance the implementation of robotics by SMEs and startups into their daily operations? How can they help to close the knowledge gap of non-technological issues of robotics in Inspection & Maintenance (I&M)? These questions were analysed by five experts: Ebert von Vonderen, Ladislav Vargovcik, Maria Roca, Roi Rodriguez de Bernardo and ...