Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed

Liquid-Solid Phase Transition of the System with Two particles in a Rectangular Box

We study the statistical properties of two hard spheres in a two dimensional rectangular box. In this system, the relation like Van der Waals equation loop is obtained between the width of the box and the pressure working on side walls. The auto-correlation function of each particle's position is calculated numerically. By this calculation near the critical width, the time at which the correlation become zero gets longer according to the increase of the height of the box. Moreover, fast and slow relaxation processes like $\alpha$ and $\beta$ relaxations observed in supper cooled liquid are observed when the height of the box is sufficiently large. These relaxation processes are discussed with the probability distribution of relative position of two particles.Comment: 6 figure

Default policies for global optimisation of noisy functions with severe noise

Global optimisation of unknown noisy functions is a daunting task that appears in domains ranging from games to control problems to meta-parameter optimisation for machine learning. We show how to incorporate heuristics to Stochastic Simultaneous Optimistic Optimization (STOSOO), a global optimisation algorithm that has very weak requirements from the function. In our case, heuristics come in the form of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The new algorithm, termed Guided STOSOO (STOSOO-G), combines the ability of CMA-ES for fast local convergence (due to the algorithm following the “natural” gradient) and the global optimisation abilities of STOSOO. We compare all three algorithms in the “harder” parts of the Comparing Continuous Optimisers on Black-Box Optimization Benchmarking benchmark suite, which provides a default set of functions for testing. We show that our approach keeps the best of both worlds, i.e. the almost optimal exploration/exploitation of STOSOO with the local optimisation strength of CMA-ES

Composite Fermion Metals from Dyon Black Holes and S-Duality

We propose that string theory in the background of dyon black holes in four-dimensional anti-de Sitter spacetime is holographic dual to conformally invariant composite Dirac fermion metal. By utilizing S-duality map, we show that thermodynamic and transport properties of the black hole match with those of composite fermion metal, exhibiting Fermi liquid-like. Built upon Dirac-Schwinger-Zwanziger quantization condition, we argue that turning on magnetic charges to electric black hole along the orbit of Gamma(2) subgroup of SL(2,Z) is equivalent to attaching even unit of statistical flux quanta to constituent fermions. Being at metallic point, the statistical magnetic flux is interlocked to the background magnetic field. We find supporting evidences for proposed holographic duality from study of internal energy of black hole and probe bulk fermion motion in black hole background. They show good agreement with ground-state energy of composite fermion metal in Thomas-Fermi approximation and cyclotron motion of a constituent or composite fermion excitation near Fermi-point.Comment: 30 pages, v2. 1 figure added, minor typos corrected; v3. revised version to be published in JHE

Developing Pulmonary Vasculopathy in Systemic Sclerosis, Detected with Non-Invasive Cardiopulmonary Exercise Testing

BACKGROUND: Patients with systemic sclerosis (SSc) may develop exercise intolerance due to musculoskeletal involvement, restrictive lung disease, left ventricular dysfunction, or pulmonary vasculopathy (PV). The latter is particularly important since it may lead to lethal pulmonary arterial hypertension (PAH). We hypothesized that abnormalities during cardiopulmonary exercise testing (CPET) in patients with SSc can identify PV leading to overt PAH. METHODS: Thirty SSc patients from the Harbor-UCLA Rheumatology clinic, not clinically suspected of having significant pulmonary vascular disease, were referred for this prospective study. Resting pulmonary function and exercise gas exchange were assessed, including peakVO2, anaerobic threshold (AT), heart rate-VO2 relationship (O2-pulse), exercise breathing reserve and parameters of ventilation-perfusion mismatching, as evidenced by elevated ventilatory equivalent for CO2 (VE/VCO2) and reduced end-tidal pCO2 (PETCO2) at the AT. RESULTS: Gas exchange patterns were abnormal in 16 pts with specific cardiopulmonary disease physiology: Eleven patients had findings consistent with PV, while five had findings consistent with left-ventricular dysfunction (LVD). Although both groups had low peak VO2 and AT, a higher VE/VCO2 at AT and decreasing PETCO2 during early exercise distinguished PV from LVD. CONCLUSIONS: Previously undiagnosed exercise impairments due to LVD or PV were common in our SSc patients. Cardiopulmonary exercise testing may help to differentiate and detect these disorders early in patients with SSc

Identification of a Danish breast/ovarian cancer family double heterozygote for BRCA1 and BRCA2 mutations

Mutations in the two breast cancer susceptibility genes BRCA1 and BRCA2 are associated with increased risk of breast and ovarian cancer. Patients with mutations in both genes are rarely reported and often involve Ashkenazi founder mutations. Here we report the first identification of a Danish breast and ovarian cancer family heterozygote for mutations in the BRCA1 and BRCA2 genes. The BRCA1 nucleotide 5215G > A/c.5096G > A mutation results in the missense mutation Arg1699Gln, while the BRCA2 nucleotide 859 + 4A > G/c.631 + 4A > G is novel. Exon trapping experiments and reverse transcriptase (RT)–PCR analysis revealed that the BRCA2 mutation results in skipping of exon 7, thereby introducing a frameshift and a premature stop codon. We therefore classify the mutation as disease causing. Since the BRCA1 Arg1699Gln mutation is also suggested to be disease-causing, we consider this family double heterozygote for BRCA1 and BRCA2 mutations

Entangled Quantum States of Magnetic Dipoles

Free magnetic moments usually manifest themselves in Curie Laws, where weak external magnetic fields produce magnetizations diverging as the reciprocal 1/T of the temperature. for a variety of materials that do not disply static magnetism, including doped semiconductors and certain rare earth intermetallics, the 1/T law is changed to a power law T^-a with a<1. We report here that a considerably simpler material, namely an insulating magneticsalt can also display such a power law, and show via comparison to specific heat data and numerical simulations that quantum mechanics is crucial for its formation. Two quantum mechanical phenomena are needed, namely level splitting - which affects the spectrum of excited states - and entanglement - where the wavefunction of a system with several degrees of freedom cannot be written as a product of wavefunctions for each degree of freedom. Entanglement effects become visible for remarkably small tunnelling terms, and are turned on well before tunnelling has visible effects on the spectrum. Our work is significant because it illustrates that entanglement is at the very heart of a very simple experimental observation for an insulating quantum spin system.Comment: 17 pages, 4 figure

Characterization of digital medical images utilizing support vector machines

BACKGROUND: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. METHODS: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. RESULTS: The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. CONCLUSION: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis