An equivalency condition of nonsingularity in nonlinear semidefinite programming

<正> In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,is established under certain conditions of convexity.     

Creating Action Research Quality in Organization Development: Rigorous,Reflective and Relevant

The purpose of the paper is to present a framework that enables action researchers to create quality action research projects within the organization development (OD) domain using the broad criteria of being rigorous, reflective and relevant and so contribute to the realm of practical knowing. What constitutes good quality action research within OD is a difficult question, given the broad range of approaches that operates in a wide variety of settings and with great diversity. It advances specific dimensions by which action researchers can create, review and assess quality in action research work. This integrative framework and criteria are practical tools to enable action researchers to create quality action research in OD.     

MULTIPLE CRITERIA DECISION MAKING： DISCORDANT PREFERENCES AND PROBLEM DESCRIPTION

There are many practical decision problems where decision makers＇ preferences may be inconsistent and contradictory. In this paper, new methods for ordering and classifying multi-attribute objects by discordant collective preferences are suggested. These methods are based on the theory of multiset metric spaces. The proposed techniques are applied to ranking companies and a competitive selection of projects, which are estimated by several experts upon multiple qualitative criteria.     

Termination of asymmetric cell division and differentiation of stomata

Stomata consist of a pair of guard cells that mediate gas and water-vapour exchange between plants and the atmosphere. Stomatal precursor cells-meristemoids-possess a transient stem-cell-like property and undergo several rounds of asymmetric divisions before further differentiation. Here we report that the Arabidopsis thaliana basic helix-loop-helix (bHLH) protein MUTE is a key switch for meristemoid fate transition. In the absence of MUTE, meristemoids abort after excessive asymmetric divisions and fail to differentiate stomata. Constitutive overexpression of MUTE directs the entire epidermis to adopt guard cell identity. MUTE has two paralogues: FAMA, a regulator of guard cell morphogenesis, and SPEECHLESS (SPCH). We show that SPCH directs the first asymmetric division that initiates stomatal lineage. Together, SPCH, MUTE and FAMA bHLH proteins control stomatal development at three consecutive steps: initiation, meristemoid differentiation and guard cell morphogenesis. Our findings highlight the roles of closely related bHLHs in cell type differentiation in plants and animals.     

Classification and Categorical Inputs with Treed Gaussian Process Models

Recognizing the successes of treed Gaussian process (TGP) models as an interpretable and thrifty model for nonparametric regression, we seek to extend the model to classification. Both treed models and Gaussian processes (GPs) have, separately, enjoyed great success in application to classification problems. An example of the former is Bayesian CART. In the latter, real-valued GP output may be utilized for classification via latent variables, which provide classification rules by means of a softmax function. We formulate a Bayesian model averaging scheme to combine these two models and describe a Monte Carlo method for sampling from the full posterior distribution with joint proposals for the tree topology and the GP parameters corresponding to latent variables at the leaves. We concentrate on efficient sampling of the latent variables, which is important to obtain good mixing in the expanded parameter space. The tree structure is particularly helpful for this task and also for developing an efficient scheme for handling categorical predictors, which commonly arise in classification problems. Our proposed classification TGP (CTGP) methodology is illustrated on a collection of synthetic and real data sets. We assess performance relative to existing methods and thereby show how CTGP is highly flexible, offers tractable inference, produces rules that are easy to interpret, and performs well out of sample.     

Functional specificity of local synaptic connections in neocortical networks

Neuronal connectivity is fundamental to information processing in the brain. Therefore, understanding the mechanisms of sensory processing requires uncovering how connection patterns between neurons relate to their function. On a coarse scale, long-range projections can preferentially link cortical regions with similar responses to sensory stimuli. But on the local scale, where dendrites and axons overlap substantially, the functional specificity of connections remains unknown. Here we determine synaptic connectivity between nearby layer 2/3 pyramidal neurons in vitro, the response properties of which were first characterized in mouse visual cortex in vivo. We found that connection probability was related to the similarity of visually driven neuronal activity. Neurons with the same preference for oriented stimuli connected at twice the rate of neurons with orthogonal orientation preferences. Neurons responding similarly to naturalistic stimuli formed connections at much higher rates than those with uncorrelated responses. Bidirectional synaptic connections were found more frequently between neuronal pairs with strongly correlated visual responses. Our results reveal the degree of functional specificity of local synaptic connections in the visual cortex, and point to the existence of fine-scale subnetworks dedicated to processing related sensory information.