Did Samuel Clarke really disavow action at a distance in his correspondence with Leibniz?: Newton,Clarke, and Bentley on gravitation and action at a distance

In this paper I ague against John Henry's claim that Newton embraced unmediated action at a distance as an explanation of gravity (Henry, 1994, 1999, 2011, 2014). In particular, I take issue with his apparent suggestion that the fact, as he sees it, that two of Newton's prominent followers, namely, Richard Bentley and Samuel Clarke, embraced unmediated action at a distance as an explanation of gravity provides significant supporting evidence that Newton did as well (see Henry, 1994 and 1999). Instead, I argue that while Bentley did ultimately defend the notion of unmediated action at a distance as an explanation of gravity, Newton himself accepted that notion neither in his correspondence with Bentley, as Henry has maintained, nor in any of his later works. I also provide evidence that suggests that Newton did, in fact, accept both the principle of local causation and the passivity of matter. Finally, I argue that whatever the case may be with respect to Newton on the matter, it is clear from his correspondence with Leibniz, as well as from his Boyle lectures, that contrary to what Henry has maintained, Clarke was a stalwart opponent of unmediated action at a distance due to his strong commitment to both the principle of local causation and the passivity of matter.     

Entanglement in a solid-state spin ensemble

Entanglement is the quintessential quantum phenomenon. It is a necessary ingredient in most emerging quantum technologies, including quantum repeaters, quantum information processing and the strongest forms of quantum cryptography. Spin ensembles, such as those used in liquid-state nuclear magnetic resonance, have been important for the development of quantum control methods. However, these demonstrations contain no entanglement and ultimately constitute classical simulations of quantum algorithms. Here we report the on-demand generation of entanglement between an ensemble of electron and nuclear spins in isotopically engineered, phosphorus-doped silicon. We combined high-field (3.4?T), low-temperature (2.9?K) electron spin resonance with hyperpolarization of the (31)P nuclear spin to obtain an initial state of sufficient purity to create a non-classical, inseparable state. The state was verified using density matrix tomography based on geometric phase gates, and had a fidelity of 98% relative to the ideal state at this field and temperature. The entanglement operation was performed simultaneously, with high fidelity, on 10(10) spin pairs; this fulfils one of the essential requirements for a silicon-based quantum information processor.     

Inclusive fitness theory and eusociality

Arising from M. A. Nowak, C. E. Tarnita & E. O. Wilson 466, 1057-1062 (2010); Nowak et al. reply. Nowak et al. argue that inclusive fitness theory has been of little value in explaining the natural world, and that it has led to negligible progress in explaining the evolution of eusociality. However, we believe that their arguments are based upon a misunderstanding of evolutionary theory and a misrepresentation of the empirical literature. We will focus our comments on three general issues.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

The identification of liquid ethane in Titan's Ontario Lacus

Titan was once thought to have global oceans of light hydrocarbons on its surface, but after 40 close flybys of Titan by the Cassini spacecraft, it has become clear that no such oceans exist. There are, however, features similar to terrestrial lakes and seas, and widespread evidence for fluvial erosion, presumably driven by precipitation of liquid methane from Titan's dense, nitrogen-dominated atmosphere. Here we report infrared spectroscopic data, obtained by the Visual and Infrared Mapping Spectrometer (VIMS) on board the Cassini spacecraft, that strongly indicate that ethane, probably in liquid solution with methane, nitrogen and other low-molecular-mass hydrocarbons, is contained within Titan's Ontario Lacus.     

Human metabolic phenotype diversity and its association with diet and blood pressure

Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.