Chirality, magnetism and light

No abstract available

Biomacromolecular stereostructure mediates mode hybridization in chiral plasmonic nanostructures

The refractive index sensitivity of plasmonic fields has been exploited for over 20 years in analytical technologies. While this sensitivity can be used to achieve attomole detection levels, they are in essence binary measurements that sense the presence/absence of a predetermined analyte. Using plasmonic fields, not to sense effective refractive indices but to provide more “granular” information about the structural characteristics of a medium, provides a more information rich output, which affords opportunities to create new powerful and flexible sensing technologies not limited by the need to synthesize chemical recognition elements. Here we report a new plasmonic phenomenon that is sensitive to the biomacromolecular structure without relying on measuring effective refractive indices. Chiral biomaterials mediate the hybridization of electric and magnetic modes of a chiral solid-inverse plasmonic structure, resulting in a measurable change in both reflectivity and chiroptical properties. The phenomenon originates from the electric-dipole–magnetic-dipole response of the biomaterial and is hence sensitive to biomacromolecular secondary structure providing unique fingerprints of α-helical, β-sheet, and disordered motifs. The phenomenon can be observed for subchiral plasmonic fields (i.e., fields with a lower chiral asymmetry than circularly polarized light) hence lifting constraints to engineer structures that produce fields with enhanced chirality, thus providing greater flexibility in nanostructure design. To demonstrate the efficacy of the phenomenon, we have detected and characterized picogram quantities of simple model helical biopolymers and more complex real proteins

Artificial neural networks and player recruitment in professional soccer

The aim was to objectively identify key performance indicators in professional soccer that influence outfield players’ league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players’ (mean SD; age: 25 ± 4 yr, 1.81 ±) 90-minute performances in the English Football League. ProZone’s MatchViewer system and online databases were used to collect data on 347 indicators assessing the total number, accuracy and consistency of passes, tackles, possessions regained, clearances and shots. Players were assigned to one of three categories based on where they went on to complete most of their match time in the following season: group 0 (n = 209 players) went on to play in a lower soccer league, group 1 (n = 637 players) remained in the Football League Championship, and group 2 (n = 120 players) consisted of players who moved up to the English Premier League. The models created correctly predicted between 61.5% and 78.8% of the players’ league status. The model with the highest average test performance was for group 0 v 2 (U21 international caps, international caps, median tackles, percentage of first time passes unsuccessful upper quartile, maximum dribbles and possessions gained minimum) which correctly predicted 78.8% of the players’ league status with a test error of 8.3%. To date, there has not been a published example of an objective method of predicting career trajectory in soccer. This is a significant development as it highlights the potential for machine learning to be used in the scouting and recruitment process in a professional soccer environment

MDL Convergence Speed for Bernoulli Sequences

The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with probability one, and (b) it additionally specifies the convergence speed. For MDL, in general one can only have loss bounds which are finite but exponentially larger than those for Bayes mixtures. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. We discuss the application to Machine Learning tasks such as classification and hypothesis testing, and generalization to countable classes of i.i.d. models.Comment: 28 page

Sub-Pixel Response Measurement of Near-Infrared Sensors

Wide-field survey instruments are used to efficiently observe large regions of the sky. To achieve the necessary field of view, and to provide a higher signal-to-noise ratio for faint sources, many modern instruments are undersampled. However, precision photometry with undersampled imagers requires a detailed understanding of the sensitivity variations on a scale much smaller than a pixel. To address this, a near-infrared spot projection system has been developed to precisely characterize near-infrared focal plane arrays and to study the effect of sub-pixel non uniformity on precision photometry. Measurements of large format near-infrared detectors demonstrate the power of this system for understanding sub-pixel response.Comment: 9 pages, 13 figures, submitted to PAS

Laser induced breakdown of the magnetic field reversal symmetry in the propagation of unpolarized light

We show how a medium, under the influece of a coherent control field which is resonant or close to resonance to an appropriate atomic transition, can lead to very strong asymmetries in the propagation of unpolarized light when the direction of the magnetic field is reversed. We show how EIT can be used to mimic effects occuring in natural systems and that EIT can produce very large asymmetries as we use electric dipole allowed transitions. Using density matrix calculations we present results for the breakdown of the magnetic field reversal symmetry for two different atomic configurations.Comment: RevTex, 6 pages, 10 figures, Two Column format, submitted to Phys. Rev.

Experimental determination of the degree of quantum polarisation of continuous variable states

We demonstrate excitation-manifold resolved polarisation characterisation of continuous-variable (CV) quantum states. In contrast to traditional characterisation of polarisation that is based on the Stokes parameters, we experimentally determine the Stokes vector of each excitation manifold separately. Only for states with a given photon number does the methods coincide. For states with an indeterminate photon number, for example Gaussian states, the employed method gives a richer and more accurate description. We apply the method both in theory and in experiment to some common states to demonstrate its advantages.Comment: 5 page

A new species in the major malaria vector complex sheds light on reticulated species evolution

Complexes of closely related species provide key insights into the rapid and independent evolution of adaptive traits. Here, we described and studied Anopheles fontenillei sp.n., a new species in the Anopheles gambiae complex that we recently discovered in the forested areas of Gabon, Central Africa. Our analysis placed the new taxon in the phylogenetic tree of the An. gambiae complex, revealing important introgression events with other members of the complex. Particularly, we detected recent introgression, with Anopheles gambiae and Anopheles coluzzii, of genes directly involved in vectorial capacity. Moreover, genome analysis of the new species allowed us to clarify the evolutionary history of the 3La inversion. Overall, An. fontenillei sp.n. analysis improved our understanding of the relationship between species within the An. gambiae complex, and provided insight into the evolution of vectorial capacity traits that are relevant for the successful control of malaria in Africa