Bulletin of Mathematical Biology - facts, figures and comparisons

The Society for Mathematical Biology (SMB) owns the Bulletin of Mathematical Biology (BMB). This is an international journal devoted to the interface of mathematics and biology. At the 2003 SMB annual meeting in Dundee the Society asked the editor of the BMB to produce an analysis of impact factor, subject matter of papers, submission rates etc. Other members of the society were interested in the handling times of articles and wanted comparisons with other (appropriate) journals. In this article we present a brief history of the journal and report on how the journal impact factor has grown substantially in the last few years. We also present an analysis of subject areas of published papers over the past two years. We finally present data on times from receipt of paper to acceptance, acceptance to print (and to online publication) and compare these data with some other journals

An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft windo

Principal Component Analysis with Noisy and/or Missing Data

We present a method for performing Principal Component Analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that compared to classic PCA, the resulting eigenvectors are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data is simply the limiting case of weight=0. The underlying algorithm is a noise weighted Expectation Maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution. We present applications of this method on simulated data and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to bibliograph

Childhood predictors of successful self-reported delinquents

The main aim of this research is to investigate the childhood predictors of successful self-reported delinquents, defined as those who were not convicted. In the Cambridge Study in Delinquent Development (CSDD), 411 London males have been followed up from age 8 to age 61. Self-reported offending was measured for the whole sample for ages 10–14, 15–18, 27–32, and 42–47, for five crimes: burglary, theft of a vehicle, theft from a vehicle, shoplifting, and vandalism. The prevalence of self-reported offending was 64% at ages 10–18 and 11% at ages 27–47, compared with the prevalence of convictions of 23% and 8% respectively. Successful self-reported delinquents were defined as those who offended between ages 10 and 18 but were not convicted up to age 26; 126 successful delinquents were compared with 120 convicted delinquents. Sixteen childhood factors, including attainment, self-control, socioeconomic, parental, family and behavioural factors, predicted successful self-reported delinquents. The most important independent predictors were committing less serious and fewer offences as well as high school attainment, unconvicted parents, low risk-taking, and unseparated families

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

Significance analysis and statistical mechanics: an application to clustering

This paper addresses the statistical significance of structures in random data: Given a set of vectors and a measure of mutual similarity, how likely does a subset of these vectors form a cluster with enhanced similarity among its elements? The computation of this cluster p-value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.Comment: to appear in Phys. Rev. Let

‘Lots of little jobs’ – building local skills ecosystems for the precarious worker

‘The world needs a wash and a week’s rest,’ wrote W.H. Auden in his 1947 poem, The Age of Anxiety. Almost three-quarters of a century later, that is the reality for many whose fulltime work ideas have fragmented into several little short-term jobs, exacerbated by COVID19. The polarisation between those who enjoy security and prosperity and those who do not has increased (Allas et al 2020). Scholars have raised concerns over the impact on the (particularly marginalised) worker of the expansion of non-standard employment, poverty cycles, and lack of training and development (Egdell and Beck 2020), resulting in dualisation, the division between workers with stable jobs and insecure jobs (Chung 2018). By marginalised, we refer to workers who tend to be at the lower or outer edge of the labour market in uncertain, unpredictable, and risky work, from the worker’s perspective (Kalleberg 2012). We argue that in light of Brexit, increased poverty, and weak skills development, understanding and involvement by employers in their local ecosystem is even more imperative. A skills ecosystem is a community of interacting living parts comprising producers, consumers, and decomposers and non-living components that define the ecosystem’s environment. We share the human resource development (HRD) interventions undertaken jointly by a university and a non-governmental organisation (NGO) between 2016 and 2019 within the City of Liverpool. The context of the research in a skills ecosystem is relevant. We worked with a local NGO based in Toxteth, Liverpool, a highly diverse area characterised by very high levels of multiple deprivation (McCurdy 2020). We found little research in HRD that has challenged the life chances of education and training (Simmons et al 2014) for those in the lower socio-economic groups or, indeed, been involved in offering solutions for those in this growing group of workers. We share our understanding of the lived experience of one of the most disadvantaged groups in the UK, the Roma (Cromarty 2019). Virtually all of the Roma in this study were in irregular, insecure work with high work–labour ratios. This may infer the participants worked in small, less regulated environments; instead, many worked in FTSE 100 UK companies. Participants’ work was generally deemed independent (in contractual terms noted as self-employment) and organised through labour market intermediaries, commonly termed agencies, with evidence of some ‘abusive’ and ‘exploitative’ practice such as poor working conditions, rather than directly with an employer

Elastodynamics of radially inhomogeneous spherically anisotropic elastic materials in the Stroh formalism

A method is presented for solving elastodynamic problems in radially inhomogeneous elastic materials with spherical anisotropy, i.e.\ materials such that $c_{ijkl}= c_{ijkl}(r)$ in a spherical coordinate system ${r,\theta,\phi}$. The time harmonic displacement field $\mathbf{u}(r,\theta ,\phi)$ is expanded in a separation of variables form with dependence on $\theta,\phi$ described by vector spherical harmonics with $r$-dependent amplitudes. It is proved that such separation of variables solution is generally possible only if the spherical anisotropy is restricted to transverse isotropy with the principal axis in the radial direction, in which case the amplitudes are determined by a first-order ordinary differential system. Restricted forms of the displacement field, such as $\mathbf{u}(r,\theta)$, admit this type of separation of variables solutions for certain lower material symmetries. These results extend the Stroh formalism of elastodynamics in rectangular and cylindrical systems to spherical coordinates.Comment: 15 page

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions