The ethics of forgetting in an age of pervasive computing

In this paper, we examine the potential of pervasive computing to create widespread sousveillance, that will complement surveillance, through the development of lifelogs; socio-spatial archives that document every action, every event, every conversation, and every material expression of an individual’s life. Examining lifelog projects and artistic critiques of sousveillance we detail the projected mechanics of life-logging and explore their potential implications. We suggest, given that lifelogs have the potential to convert exterior generated oligopticons to an interior panopticon, that an ethics of forgetting needs to be developed and built into the development of life-logging technologies. Rather than seeing forgetting as a weakness or a fallibility we argue that it is an emancipatory process that will free pervasive computing from burdensome and pernicious disciplinary effects

ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out- performs all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners’ performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a plan- ner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Chemical Properties from Graph Neural Network-Predicted Electron Densities

According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not training to predict atomic charges, the model is able to predict atomic charges with an order of magnitude lower error than a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions in these tasks. These results pave the way for an alternative path in atomistic machine learning, where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner

The discomforting rise of ' public geographies': a 'public' conversation.

In this innovative and provocative intervention, the authors explore the burgeoning ‘public turn’ visible across the social sciences to espouse the need to radically challenge and reshape dominant and orthodox visions of ‘the academy’, academic life, and the role and purpose of the academic

Immunogenicity of a low-dose diphtheria, tetanus and acellular pertussis combination vaccine with either inactivated or oral polio vaccine compared to standard-dose diphtheria, tetanus, acellular pertussis when used as a pre-school booster in UK children : a 5-year follow-up of a randomised controlled study

This serological follow up study assessed the kinetics of antibody response in children who previously participated in a single centre, open-label, randomised controlled trial of low-dose compared to standard-dose diphtheria booster preschool vaccinations in the United Kingdom (UK). Children had previously been randomised to receive one of three combination vaccines: either a combined adsorbed tetanus, low-dose diphtheria, 5-component acellular pertussis and inactivated polio vaccine (IPV) (Tdap-IPV, Repevax(®); Sanofi Pasteur MSD); a combined adsorbed tetanus, low-dose diphtheria and 5-component acellular pertussis vaccine (Tdap, Covaxis(®); Sanofi Pasteur MSD) given concomitantly with oral polio vaccine (OPV); or a combined adsorbed standard-dose diphtheria, tetanus, 2-component acellular pertussis and IPV (DTap-IPV, Tetravac(®); Sanofi Pasteur MSD). Blood samples for the follow-up study were taken at 1, 3 and 5 years after participation in the original trial (median, 5.07 years of age at year 1), and antibody persistence to each vaccine antigen measured against defined serological thresholds of protection. All participants had evidence of immunity to diphtheria with antitoxin concentrations greater than 0.01IU/mL five years after booster vaccination and 75%, 67% and 79% of children who received Tdap-IPV, Tdap+OPV and DTap-IPV, respectively, had protective antitoxin levels greater than 0.1IU/mL. Long lasting protective immune responses to tetanus and polio antigens were also observed in all groups, though polio responses were lower in the sera of those who received OPV. Low-dose diphtheria vaccines provided comparable protection to the standard-dose vaccine and are suitable for use for pre-school booster vaccination

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks

Geographies of the COVID-19 pandemic

The spread of the novel coronavirus (SARS-CoV-2) has resulted in the most devastating global public health crisis in over a century. At present, over 10 million people from around the world have contracted the Coronavirus Disease 2019 (COVID-19), leading to more than 500,000 deaths globally. The global health crisis unleashed by the COVID-19 pandemic has been compounded by political, economic, and social crises that have exacerbated existing inequalities and disproportionately affected the most vulnerable segments of society. The global pandemic has had profoundly geographical consequences, and as the current crisis continues to unfold, there is a pressing need for geographers and other scholars to critically examine its fallout. This introductory article provides an overview of the current special issue on the geographies of the COVID-19 pandemic, which includes 42 commentaries written by contributors from across the globe. Collectively, the contributions in this special issue highlight the diverse theoretical perspectives, methodological approaches, and thematic foci that geographical scholarship can offer to better understand the uneven geographies of the Coronavirus/COVID-19. </jats:p

Forecasting in the light of Big Data

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann