William James and the Evolution of Consciousness

Despite having been relegated to the realm of superstition during the dominant years of behaviourism, the investigation and discussion of consciousness has again become scientifically defensible. However, attempts at describing animal consciousness continue to be criticised for lacking independent criteria that identify the presence or absence of the phenomenon. Over one hundred years ago William James recognised that mental traits are subject to the same evolutionary processes as are physical characteristics and must therefore be represented in differing levels of complexity throughout the animal kingdom. James's proposals with regard to animal consciousness are outlined and followed by a discussion of three classes of animal consciousness derived from empirical research. These classes are presented to defend both James's proposals and the position that a theory of animal consciousness can be scientifically supported. It is argued that by using particular behavioural expressions to index consciousness and by providing empirical tests by which to elicit these behavioural expressions a scientifically defensible theory of animal consciousness can be developed

Correlations in Nuclear Matter

We analyze the nuclear matter correlation properties in terms of the pair correlation function. To this aim we systematically compare the results for the variational method in the Lowest Order Constrained Variational (LOCV) approximation and for the Bruekner-Hartree-Fock (BHF) scheme. A formal link between the Jastrow correlation factor of LOCV and the Defect Function (DF) of BHF is established and it is shown under which conditions and approximations the two approaches are equivalent. From the numerical comparison it turns out that the two correlation functions are quite close, which indicates in particular that the DF is approximately local and momentum independent. The Equations of State (EOS) of Nuclear Matter in the two approaches are also compared. It is found that once the three-body forces (TBF) are introduced the two EOS are fairly close, while the agreement between the correlation functions holds with or without TBF.Comment: 11 figure

Benchmark ultra-cool dwarfs in widely separated binary systems

Ultra-cool dwarfs as wide companions to subgiants, giants, white dwarfs and main sequence stars can be very good benchmark objects, for which we can infer physical properties with minimal reference to theoretical models, through association with the primary stars. We have searched for benchmark ultra-cool dwarfs in widely separated binary systems using SDSS, UKIDSS, and 2MASS. We then estimate spectral types using SDSS spectroscopy and multi-band colors, place constraints on distance, and perform proper motions calculations for all candidates which have sufficient epoch baseline coverage. Analysis of the proper motion and distance constraints show that eight of our ultra-cool dwarfs are members of widely separated binary systems. Another L3.5 dwarf, SDSS 0832, is shown to be a companion to the bright K3 giant Eta Cancri. Such primaries can provide age and metallicity constraints for any companion objects, yielding excellent benchmark objects. This is the first wide ultra-cool dwarf + giant binary system identified.Comment: 4 pages, 3 figures, conference, "New Technologies for Probing the Diversity of Brown Dwarfs and Exoplanets", oral tal

Effectiveness Of Alternative Heuristic Algorithms For Identifying Indicative Minimum Requirements For Conservation Reserves

We compared the results of 30 heuristic reserve selection algorithms on the same large data set. Twelve of the algorithms were for presence-absence representation goals, designed to find a set of sites to represent all the land types in the study region at least once. Eighteen algorithms were intended to represent a minimum percentage of the total area of each land type. We varied the rules of the algorithms systematically to find the influence of individual rules or sequences of rules on efficiency of representation. Rankings of the algorithms according to relative numbers or areas of selected sites needed to achieve a specified representation target varied between the full data set and a subset and so appear to be data-dependent. We also ran optimizing algorithms to indicate the degree of suboptimality of the heuristics. For the presence-absence problems, the optimizing algorithms had the advantage of guaranteeing an optimal solution but had much longer running times than the heuristics. They showed that the solutions from good heuristics were 5-10% larger than optimal. The optimizing algorithms failed to solve the proportional area problems, although heuristics solved them quickly. Both heuristics and optimizing algorithms have important roles to play in conservation planning. The choice of method will depend on the size of data sets, the representation goal, the required time for analysis, and the importance of a guaranteed optimal solution

Position and energy-resolved particle detection using phonon-mediated microwave kinetic inductance detectors

We demonstrate position and energy-resolved phonon-mediated detection of particle interactions in a silicon substrate instrumented with an array of microwave kinetic inductance detectors (MKIDs). The relative magnitude and delay of the signal received in each sensor allow the location of the interaction to be determined with ≲ 1mm resolution at 30 keV. Using this position information, variations in the detector response with position can be removed, and an energy resolution of σ_E = 0.55 keV at 30 keV was measured. Since MKIDs can be fabricated from a single deposited film and are naturally multiplexed in the frequency domain, this technology can be extended to provide highly pixelized athermal phonon sensors for ∼1 kg scale detector elements. Such high-resolution, massive particle detectors would be applicable to rare-event searches such as the direct detection of dark matter, neutrinoless double-beta decay, or coherent neutrino-nucleus scattering

Is the Yb2Ti2O7 pyrochlore a quantum spin ice?

We use numerical linked cluster (NLC) expansions to compute the specific heat, C(T), and entropy, S(T), of a quantum spin ice model of Yb2Ti2O7 using anisotropic exchange interactions recently determined from inelastic neutron scattering measurements and find good agreement with experimental calorimetric data. In the perturbative weak quantum regime, this model has a ferrimagnetic ordered ground state, with two peaks in C(T): a Schottky anomaly signalling the paramagnetic to spin ice crossover followed at lower temperature by a sharp peak accompanying a first order phase transition to the ferrimagnetic state. We suggest that the two C(T) features observed in Yb2Ti2O7 are associated with the same physics. Spin excitations in this regime consist of weakly confined spinon-antispinon pairs. We suggest that conventional ground state with exotic quantum dynamics will prove a prevalent characteristic of many real quantum spin ice materials.Comment: 8 pages (two-column), 9 figure

A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity

In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R 2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, and especially achieved excellent performance in predicting outputs that have a very large range of value differences