Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors - a person and a machine - that leads to an improved outcome?In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections

Spatial Analysis of Temporal Changes in the Pandemic of Severe Cassava Mosaic Disease in Northwestern Tanzania

Published online: 8 Sept 2017To improve understanding of the dynamics of the cassava mosaic disease (CMD) pandemic front, geospatial approaches were applied to the analysis of 3 years’ data obtained from a 2-by-2° (approximately 222-by-222 km) area of northwestern Tanzania. In total, 80 farmers’ fields were assessed in each of 2009, 2010, and 2011, with 20 evenly distributed fields per 1-by-1° quadrant. CMD-associated variables (CMD incidence, CMD severity, vector-borne CMD infection, and vector abundance) increased in magnitude from 2009 to 2010 but showed little change from 2010 to 2011. Increases occurred primarily in the two westernmost quadrants of the study area. A pandemic “front” was defined by determining the values of CMD incidence and whitefly abundance where predicted disease gradients were greatest. The pandemic-associated virus (East African cassava mosaic virus-Uganda) and vector genotype (Bemisia tabaci sub-Saharan Africa 1–subgroup 1) were both present within the area bounded by the CMD incidence front but both also occurred ahead of the front. The average speed and direction of movement of the CMD incidence front (22.9 km/year; southeast) and whitefly abundance front (46.6 km/year; southeast) were calculated, and production losses due to CMD were estimated to range from US$4.3 million to 12.2 million

Nanoscale structuring of tungsten tip yields most coherent electron point-source

This report demonstrates the most spatially-coherent electron source ever reported. A coherence angle of 14.3 +/- 0.5 degrees was measured, indicating a virtual source size of 1.7 +/-0.6 Angstrom using an extraction voltage of 89.5 V. The nanotips under study were crafted using a spatially-confined, field-assisted nitrogen etch which removes material from the periphery of the tip apex resulting in a sharp, tungsten-nitride stabilized, high-aspect ratio source. The coherence properties are deduced from holographic measurements in a low-energy electron point source microscope with a carbon nanotube bundle as sample. Using the virtual source size and emission current the brightness normalized to 100 kV is found to be 7.9x10^8 A/sr cm^2

Miniaturized photoacoustic trace gas sensing using a raman fiber amplifier

This paper presents the development of a Raman fiber amplifier optical source with a maximum output power of 1.1 W centered around 1651 nm, and its application in miniaturized 3D printed photoacoustic spectroscopy (PAS) trace gas sensing of methane. The Raman amplifier has been constructed using 4.5 km of dispersion shifted fiber, a 1651 nm DFB seed laser and a commercial 4W EDFA pump. The suppression of stimulated Brillouin scattering (SBS) using a high frequency modulation of the seed laser is investigated for a range of frequencies, leading to an increase in optical output power of the amplifier and reduction of its noise content. The amplifier output was used as the source for a miniature PAS sensor by applying a second modulation to the seed laser at the resonant frequency of 15.2 kHz of the miniature 3D printed gas cell. For the targeted methane absorption line at 6057 cm-1 the sensor system performance and influence of the SBS suppression is characterized, leading to a detection limit (1σ) of 17 ppb methane for a signal acquisition time of 130 s, with a normalized noise equivalent absorption coefficient of 4.1•10-9 cm-1 W Hz-1/2 for the system

Algebraic inversion of the Dirac equation for the vector potential in the non-abelian case

We study the Dirac equation for spinor wavefunctions minimally coupled to an external field, from the perspective of an algebraic system of linear equations for the vector potential. By analogy with the method in electromagnetism, which has been well-studied, and leads to classical solutions of the Maxwell-Dirac equations, we set up the formalism for non-abelian gauge symmetry, with the SU(2) group and the case of four-spinor doublets. An extended isospin-charge conjugation operator is defined, enabling the hermiticity constraint on the gauge potential to be imposed in a covariant fashion, and rendering the algebraic system tractable. The outcome is an invertible linear equation for the non-abelian vector potential in terms of bispinor current densities. We show that, via application of suitable extended Fierz identities, the solution of this system for the non-abelian vector potential is a rational expression involving only Pauli scalar and Pauli triplet, Lorentz scalar, vector and axial vector current densities, albeit in the non-closed form of a Neumann series.Comment: 21pp, uses iopar

Anisotropy of the space orientation of radio sources. I: The catalog

A catalog of the extended extragalactic radio sources consisting of 10461 objects is compiled based on the list of radio sources of the FIRST survey. A total of 1801 objects are identified with galaxies and quasars of the SDSS survey and the Veron-Veron catalog. The distribution of the position angles of the axes of radio sources from the catalog is determined, and the probability that this distribution is equiprobable is shown to be less then 10^(-7). This result implies that at Z equal to or smaller then 0.5, spatial orientation of the axes of radio sources is anisotropic at a statistically significant level.Comment: 8 pages, 7 figure

Superconductivity in an Einstein Solid AxV2Al20 (A = Al and Ga)

A cage compound AxV2Al20 (Al10V), that was called an Einstein solid by Caplin and coworkers 40 years ago, is revisited to investigate the low-energy, local vibrations of the A atoms and their influence on the electronic and superconducting properties of the compound. Polycrystalline samples with A = Al, Ga, Y, and La are studied through resistivity and heat capacity measurements. Weak-coupling BCS superconductivity is observed below Tc = 1.49, 1.66, and 0.69 K for Ax = Al0.3, Ga0.2, and Y, respectively, but not above 0.4 K for Ax = La. Low-energy modes are detected only for A = Al and Ga, which are approximately described by the Einstein model with Einstein temperatures of 24 and 8 K, respectively. A weak but significant coupling between the low-energy modes, which are almost identical to those called rattling in recent study, and conduction electrons manifests itself as anomalous enhancement in resistivity at around low temperatures corresponding to the Einstein temperatures.Comment: 12 pages, 5 figures, to be published in J. Phys. Soc. Jp

Predicting novel candidate human obesity genes and their site of action by systematic functional screening in Drosophila.

Funder: NIHR [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation TrustFunder: NHS National Institute for Health Research Clinical Research NetworkFunder: Royal Society Darwin Trust Research ProfessorshipFunder: NIHR Senior Investigator AwardFunder: Health Data Research UKFunder: Higher Education Funding Council for England CatalystFunder: NIHR Cambridge Biomedical Research CentreFunder: Bernard Wolfe Health Neuroscience EndowmentFunder: The Botnar FondationThe discovery of human obesity-associated genes can reveal new mechanisms to target for weight loss therapy. Genetic studies of obese individuals and the analysis of rare genetic variants can identify novel obesity-associated genes. However, establishing a functional relationship between these candidate genes and adiposity remains a significant challenge. We uncovered a large number of rare homozygous gene variants by exome sequencing of severely obese children, including those from consanguineous families. By assessing the function of these genes in vivo in Drosophila, we identified 4 genes, not previously linked to human obesity, that regulate adiposity (itpr, dachsous, calpA, and sdk). Dachsous is a transmembrane protein upstream of the Hippo signalling pathway. We found that 3 further members of the Hippo pathway, fat, four-jointed, and hippo, also regulate adiposity and that they act in neurons, rather than in adipose tissue (fat body). Screening Hippo pathway genes in larger human cohorts revealed rare variants in TAOK2 associated with human obesity. Knockdown of Drosophila tao increased adiposity in vivo demonstrating the strength of our approach in predicting novel human obesity genes and signalling pathways and their site of action