Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

The Photoemission from Quantum Wells, Wires and Carbon Nanotubes:Simplified Theory and Relative Comparison

We investigate the photoemission from quantum wells (QWs) in ultrathin films (UFs) and quantum well wires (QWWs) of non-linear optical materials on the basis of a newly formulated electron dispersion law considering the anisotropies of the effective electron masses, the spin-orbit splitting constants and the presence of the crystal field splitting within the framework of k.p formalism. The results of quantum confined Ill-V compounds form the special cases of our generalized analysis. The photoemission has also been studied for quantum confined II-VI, n-GaP, n-Ge, PtSb2, stressed materials and Bismuth on the basis of respective dispersion relations. It has been found taking quantum confined CdGeAS(2), InAs, InSb, CdS, GaP, Ge, PtSb2, stressed n-InSb and B1 that the photoemission exhibits quantized variations with the incident photon energy, changing electron concentration and film thickness, respectively, for all types of quantum confinement. The photoemission from CNs exhibits oscillatory dependence with increasing normalized electron degeneracy and the signature of the entirely different types of quantum systems are evident from the plots. Besides, under certain special conditions, all the results for all the materials gets simplified to the well-known expression of photoemission from non-degenerate semiconductors and parabolic energy bands, leading to the compatibility test.

Phases and transitions in the spin-1 Bose-Hubbard model: Systematics of a mean-field theory

We generalize the mean-field theory for the spinless Bose-Hubbard model to account for the different types of superfluid phases that can arise in the spin-1 case. In particular, our mean-field theory can distinguish polar and ferromagnetic superfluids, Mott insulator, that arise at integer fillings at zero temperature, and normal Bose liquids into which the Mott insulators evolve at finite temperatures. We find, in contrast to the spinless case, that several of the superfluid-Mott insulator transitions are of first order at finite temperatures. Our systematic study yields rich phase diagrams that include first-order and second-order transitions and a variety of tricritical points. We discuss the possibility of realizing such phase diagrams in experimental systems.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Theory of the unusual doping and temperature dependence of photoemission spectra in manganites

A recent, major, puzzle in the core-level photoemission spectra of doped manganites is the observation of a 1–2 eV wide shoulder with intensity varying with temperature T as the square of the magnetization over a T scale of order 200 K, an order of magnitude less than electronic energies. This is addressed and resolved here, by extending a recently proposed two-fluid polaron–mobile electron model for these systems to include core-hole effects. The position of the shoulder is found to be determined by Coulomb and Jahn-Teller energies, while its spectral weight is determined by the mobile electron energetics which is strongly T and doping dependent, due to annealed disorder scattering from the polarons and the t2g core spins. Our theory accounts quantitatively for the observed T dependence of the difference spectra, and furthermore, explains the observed correspondence between spectral changes due to increasing doping and decreasing T.

Simplified theory of optical nonlinearities in spin-polarized bulk GaAs

Apart from their intrinsic physical interest, spin-polarized many-body effects are expected to be important to the working of spintronic devices. A vast literature exists on the effects of a spin-unpolarized electron-hole plasma on the optical properties of a semiconductor. Here, we include the spin degree of freedom to model optical absorption of circularly polarized light by spin-polarized bulk GaAs. Our model is easy to implement and does not require elaborate numerics, since it is based on the closed-form analytical pair-equation formula that is valid in 3d. The efficacy of our approach is demonstrated by a comparison with recent experimental data.

Modified density matrix renormalization group algorithm for the zigzag spin-1/2 chain with frustrated antiferromagnetic exchange: Comparison with field theory at large J(2)/J(1)

A modified density matrix renormalization group (DMRG) algorithm is applied to the zigzag spin-1/2 chain with frustrated antiferromagnetic exchange J(1) and J(2) between first and second neighbors. The modified algorithm yields accurate results up to J(2)/J(1) approximate to 4 for the magnetic gap Delta to the lowest triplet state, the amplitude B of the bond order wave phase, the wavelength lambda of the spiral phase, and the spin correlation length xi. The J(2)/J(1) dependences of Delta, B, lambda, and xi provide multiple comparisons to field theories of the zigzag chain. The twist angle of the spiral phase and the spin structure factor yield additional comparisons between DMRG and field theory. Attention is given to the numerical accuracy required to obtain exponentially small gaps or exponentially long correlations near a quantum phase transition.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Toiminta, valta ja kokemus organisaation muutoksessa : Tutkimus kolmesta suuryrityksestä

Action, Power and Experience in Organizational Change - A Study of Three Major Corporations This study explores change management and resistance to change as social activities and power displays through worker experiences in three major Finnish corporations. Two important sensitizing concepts were applied. Firstly, Richard Sennett's perspective on work in the new form of capitalism, and its shortcomings - the lack of commitment and freedom accompanied by the disruption to lifelong career planning and the feeling of job insecurity - offered a fruitful starting point for a critical study. Secondly, Michel Foucault's classical concept of power, treated as anecdotal, interactive and nonmeasurable, provided tools for analyzing change-enabling and resisting acts. The study bridges the gap between management and social sciences. The former have usually concentrated on leadership issues, best practices and goal attainment, while the latter have covered worker experiences, power relations and political conflicts. The study was motivated by three research questions. Firstly, why people resist or support changes in their work, work environment or organization, and the kind of analyses these behavioural choices are based on. Secondly, the kind of practical forms which support for, and resistance to change take, and how people choose the different ways of acting. Thirdly, how the people involved experience and describe their own subject position and actions in changing environments. The examination focuses on practical interpretations and action descriptions given by the members of three major Finnish business organizations. The empirical data was collected during a two-year period in the Finnish Post Corporation, the Finnish branch of Vattenfal Group, one of the leading European energy companies, and the Mehiläinen Group, the leading private medical service provider in Finland. It includes 154 non-structured thematic interviews and 309 biographies concentrating on personal experiences of change. All positions and organizational levels were represented. The analysis was conducted using the grounded theory method introduced by Straus and Corbin in three sequential phases, including open, axial and selective coding processes. As a result, there is a hierarchical structure of categories, which is summarized in the process model of change behaviour patterns. Key ingredients are past experiences and future expectations which lead to different change relations and behavioural roles. Ultimately, they contribute to strategic and tactical choices realized as both public and hidden forms of action. The same forms of action can be used in both supporting and resisting change, and there are no specific dividing lines either between employer and employee roles or between different hierarchical positions. In general, however, it is possible to conclude that strategic choices lead more often to public forms of action, whereas tactical choices result in hidden forms. The primary goal of the study was to provide knowledge which has practical applications in everyday business life, HR and change management. The results, therefore, are highly applicable to other organizations as well as to less change-dominated situations, whenever power relations and conflicting interests are present. A sociological thesis on classical business management issues can be of considerable value in revealing the crucial social processes behind behavioural patterns. Keywords: change management, organizational development, organizational resistance, resistance to change, change management, labor relations, organization, leadership