Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation

Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis. In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers. We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.Comment: submitted to ECMLPKDD 201

COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION

Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data

Superfluid vs Ferromagnetic Behaviour in a Bose Gas of Spin-1/2 Atoms

We study the thermodynamic phases of a gas of spin-1/2 atoms in the Hartree-Fock approximation. Our main result is that, for repulsive or weakly-attractive inter-component interaction strength, the superfluid and ferromagnetic phase transitions occur at the same temperature. For strongly-attractive inter-component interaction strength, however, the ferromagnetic phase transition occurs at a higher temperature than the superfluid phase transition. We also find that the presence of a condensate acts as an effective magnetic field that polarizes the normal cloud. We finally comment on the validity of the Hartree-Fock approximation in describing different phenomena in this system.Comment: 10 pages, 2 figure

Sensory Measurements: Coordination and Standardization

Do sensory measurements deserve the label of “measurement”? We argue that they do. They fit with an epistemological view of measurement held in current philosophy of science, and they face the same kinds of epistemological challenges as physical measurements do: the problem of coordination and the problem of standardization. These problems are addressed through the process of “epistemic iteration,” for all measurements. We also argue for distinguishing the problem of standardization from the problem of coordination. To exemplify our claims, we draw on olfactory performance tests, especially studies linking olfactory decline to neurodegenerative disorders

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Gene expression profiling in the lung tissue of cynomolgus monkeys in response to repeated exposure to welding fumes

Many in the welding industry suffer from bronchitis, lung function changes, metal fume fever, and diseases related to respiratory damage. These phenomena are associated with welding fumes; however, the mechanism behind these findings remains to be elucidated. In this study, the lungs of cynomolgus monkeys were exposed to MMA-SS welding fumes for 229 days and allowed to recover for 153 days. After the exposure and recovery period, gene expression profiles were investigated using the Affymetrix GeneChip® Human U133 plus 2.0. In total, it was confirmed that 1,116 genes were up-or down-regulated (over 2-fold changes, P < 0.01) for the T1 (31.4 ± 2.8 mg/m3) and T2 (62.5 ± 2.7 mg/m3) dose groups. Differentially expressed genes in the exposure and recovery groups were analyzed, based on hierarchical clustering, and were imported into Ingenuity Pathways Analysis to analyze the biological and toxicological functions. Functional analysis identified genes involved in immunological disease in both groups. Additionally, differentially expressed genes in common between monkeys and rats following welding fume exposure were compared using microarray data, and the gene expression of selected genes was verified by real-time PCR. Genes such as CHI3L1, RARRES1, and CTSB were up-regulated and genes such as CYP26B1, ID4, and NRGN were down-regulated in both monkeys and rats following welding fume exposure. This is the first comprehensive gene expression profiling conducted for welding fume exposure in monkeys, and these expressed genes are expected to be useful in helping to understand transcriptional changes in monkey lungs after welding fume exposure

Inhibiting LXRα phosphorylation in hematopoietic cells reduces inflammation and attenuates atherosclerosis and obesity in mice

Atherosclerosis and obesity share pathological features including inflammation mediated by innate and adaptive immune cells. LXRα plays a central role in the transcription of inflammatory and metabolic genes. LXRα is modulated by phosphorylation at serine 196 (LXRα pS196), however, the consequences of LXRα pS196 in hematopoietic cell precursors in atherosclerosis and obesity have not been investigated. To assess the importance of LXRα phosphorylation, bone marrow from LXRα WT and S196A mice was transplanted into Ldlr-/- mice, which were fed a western diet prior to evaluation of atherosclerosis and obesity. Plaques from S196A mice showed reduced inflammatory monocyte recruitment, lipid accumulation, and macrophage proliferation. Expression profiling of CD68+ and T cells from S196A mouse plaques revealed downregulation of pro-inflammatory genes and in the case of CD68+ upregulation of mitochondrial genes characteristic of anti-inflammatory macrophages. Furthermore, S196A mice had lower body weight and less visceral adipose tissue; this was associated with transcriptional reprograming of the adipose tissue macrophages and T cells, and resolution of inflammation resulting in less fat accumulation within adipocytes. Thus, reducing LXRα pS196 in hematopoietic cells attenuates atherosclerosis and obesity by reprogramming the transcriptional activity of LXRα in macrophages and T cells to promote an anti-inflammatory phenotype

Spin-photon interface and spin-controlled photon switching in a nanobeam waveguide

Access to the electron spin is at the heart of many protocols for integrated and distributed quantum-information processing [1-4]. For instance, interfacing the spin-state of an electron and a photon can be utilized to perform quantum gates between photons [2,5] or to entangle remote spin states [6-9]. Ultimately, a quantum network of entangled spins constitutes a new paradigm in quantum optics [1]. Towards this goal, an integrated spin-photon interface would be a major leap forward. Here we demonstrate an efficient and optically programmable interface between the spin of an electron in a quantum dot and photons in a nanophotonic waveguide. The spin can be deterministically prepared with a fidelity of 96\%. Subsequently the system is used to implement a "single-spin photonic switch", where the spin state of the electron directs the flow of photons through the waveguide. The spin-photon interface may enable on-chip photon-photon gates [2], single-photon transistors [10], and efficient photonic cluster state generation [11]

Beam Energy and Centrality Dependence of Direct-Photon Emission from Ultrarelativistic Heavy-Ion Collisions.

The PHENIX collaboration presents first measurements of low-momentum (0.41  GeV/c) direct-photon yield dN_{γ}^{dir}/dη is a smooth function of dN_{ch}/dη and can be well described as proportional to (dN_{ch}/dη)^{α} with α≈1.25. This scaling behavior holds for a wide range of beam energies at the Relativistic Heavy Ion Collider and the Large Hadron Collider, for centrality selected samples, as well as for different A+A collision systems. At a given beam energy, the scaling also holds for high p_{T} (&gt;5  GeV/c), but when results from different collision energies are compared, an additional sqrt[s_{NN}]-dependent multiplicative factor is needed to describe the integrated-direct-photon yield

Measurement of the hadronic photon structure function F_{2}^{γ} at LEP2

The hadronic structure function of the photon F_{2}^{γ} (x, Q²) is measured as a function of Bjorken x and of the photon virtuality Q² using deep-inelastic scattering data taken by the OPAL detector at LEP at e⁺e⁻ centre-of-mass energies from 183 to 209 GeV. Previous OPAL measurements of the x dependence of F_{2}^{γ} are extended to an average Q² of 〈Q²〉=780 GeV² using data in the kinematic range 0.15<x<0.98. The Q² evolution of F_{2}^{γ} is studied for 12.1<〈Q²〉<780 GeV² using three ranges of x. As predicted by QCD, the data show positive scaling violations in F_{2}^{γ} with F_{2}^{γ} (Q²)/α = (0.08±0.02⁺⁰·⁰⁵_₀.₀₃) + (0.13±0.01⁺⁰·⁰¹_₀.₀₁) lnQ², where Q² is in GeV², for the central x region 0.10–0.60. Several parameterisations of F_{2}^{γ} are in qualitative agreement with the measurements whereas the quark-parton model prediction fails to describe the data