Synchronization in random networks with given expected degree sequences

Synchronization in random networks with given expected degree sequences is studied. We also investigate in details the synchronization in networks whose topology is described by classical random graphs, power-law random graphs and hybrid graphs when N goes to infinity. In particular, we show that random graphs almost surely synchronize. We also show that adding small number of global edges to a local graph makes the corresponding hybrid graph to synchroniz

Equilibrium analysis of cellular neural networks

Cellular neural networks are dynamical systems, described by a large set of coupled nonlinear differential equations. The equilibrium point analysis is an important step for understanding the global dynamics and for providing design rules. We yield a set of sufficient conditions (and a simple algorithm for checking them) ensuring the existence of at least one stable equilibrium point. Such conditions give rise to simple constraints, that extend the class of CNN, for which the existence of a stable equilibrium point is rigorously proved. In addition, they are suitable for design and easy to check, because they are directly expressed in term of the template elements

Synchronization in random networks with given expected degree sequences

Synchronization in random networks with given expected degree sequences is studied. We also investigate in details the synchronization in networks whose topology is described by classical random graphs, power-law random graphs and hybrid graphs when N goes to infinity. In particular, we show that random graphs almost surely synchronize. We also show that adding small number of global edges to a local graph makes the corresponding hybrid graph to synchronize

AI-assisted peer review

The scientific literature peer review workflow is under strain because of the constant growth of submission volume. One response to this is to make initial screening of submissions less time intensive. Reducing screening and review time would save millions of working hours and potentially boost academic productivity. Many platforms have already started to use automated screening tools, to prevent plagiarism and failure to respect format requirements. Some tools even attempt to flag the quality of a study or summarise its content, to reduce reviewers’ load. The recent advances in artificial intelligence (AI) create the potential for (semi) automated peer review systems, where potentially low-quality or controversial studies could be flagged, and reviewer-document matching could be performed in an automated manner. However, there are ethical concerns, which arise from such approaches, particularly associated with bias and the extent to which AI systems may replicate bias. Our main goal in this study is to discuss the potential, pitfalls, and uncertainties of the use of AI to approximate or assist human decisions in the quality assurance and peer-review process associated with research outputs. We design an AI tool and train it with 3300 papers from three conferences, together with their reviews evaluations. We then test the ability of the AI in predicting the review score of a new, unobserved manuscript, only using its textual content. We show that such techniques can reveal correlations between the decision process and other quality proxy measures, uncovering potential biases of the review process. Finally, we discuss the opportunities, but also the potential unintended consequences of these techniques in terms of algorithmic bias and ethical concerns

Integrating FATE/critical data studies into data science curricula : where are we going and how do we get there?

There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Education in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community

Surface nanobubbles as a function of gas type

We experimentally investigate the nucleation of surface nanobubbles on PFDTS-coated silicon as a function of the specific gas dissolved in the water. In each case we restrict ourselves to equilibrium conditions ($c=100$, $T_{liquid} = T_{substrate}$). Not only is nanobubble nucleation a strong function of gas type, but there also exists an optimal system temperature of $\sim 35-40\mathrm{^oC}$ where nucleation is maximized, which is weakly dependent on gas type. We also find that contact angle is a function of nanobubble radius of curvature for all gas types investigated. Fitting this data allows us to describe a line tension which is dependent on the type of gas, indicating that the nanobubbles are sat on top of adsorbed gas molecules. The average line tension was $\tau \sim -0.8 \mathrm{nN}$

Imola: A decentralised learning-driven protocol for multi-hop White-Fi

In this paper we tackle the digital exclusion problem in developing and remote locations by proposing Imola, an inexpensive learning-driven access mechanism for multi-hop wireless networks that operate across TV white-spaces (TVWS). Stations running Imola only rely on passively acquired neighbourhood information to achieve scheduled-like operation in a decentralised way, without explicit synchronisation. Our design overcomes pathological circumstances such as hidden and exposed terminals that arise due to carrier sensing and are exceptionally problematic in low frequency bands. We present a prototype implementation of our proposal and conduct experiments in a real test bed, which confirms the practical feasibility of deploying our solution in mesh networks that build upon the IEEE 802.11af standard. Finally, the extensive system level simulations we perform demonstrate that Imola achieves up to 4x\u97 more throughput than the channel access protocol defined by the standard and reduces frame loss rate by up to 100%