Impact of regularization on Spectral Clustering

The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et. al (2012). Here, we provide an attempt at quantifying this improvement through theoretical analysis. Under the stochastic block model (SBM), and its extensions, previous results on spectral clustering relied on the minimum degree of the graph being sufficiently large for its good performance. By examining the scenario where the regularization parameter $\tau$ is large we show that the minimum degree assumption can potentially be removed. As a special case, for an SBM with two blocks, the results require the maximum degree to be large (grow faster than $\log n$) as opposed to the minimum degree. More importantly, we show the usefulness of regularization in situations where not all nodes belong to well-defined clusters. Our results rely on a `bias-variance'-like trade-off that arises from understanding the concentration of the sample Laplacian and the eigen gap as a function of the regularization parameter. As a byproduct of our bounds, we propose a data-driven technique \textit{DKest} (standing for estimated Davis-Kahan bounds) for choosing the regularization parameter. This technique is shown to work well through simulations and on a real data set.Comment: 37 page

A Framework for Developing and Integrating Effective Routing Strategies Within the Emergency Management Decision-Support System, Research Report 11-12

This report describes the modeling, calibration, and validation of a VISSIM traffic-flow simulation of the San José, California, downtown network and examines various evacuation scenarios and first-responder routings to assess strategies that would be effective in the event of a no-notice disaster. The modeled network required a large amount of data on network geometry, signal timings, signal coordination schemes, and turning-movement volumes. Turning-movement counts at intersections were used to validate the network with the empirical formula-based measure known as the GEH statistic. Once the base network was tested and validated, various scenarios were modeled to estimate evacuation and emergency vehicle arrival times. Based on these scenarios, a variety of emergency plans for San José’s downtown traffic circulation were tested and validated. The model could be used to evaluate scenarios in other communities by entering their community-specific data

Taking a “Deep Dive”: What Only a Top Leader Can Do

Unlike most historical accounts of strategic change inside large firms, empirical research on strategic management rarely uses the day-to-day behaviors of top executives as the unit of analysis. By examining the resource allocation process closely, we introduce the concept of a deep dive, an intervention when top management seizes hold of the substantive content of a strategic initiative and its operational implementation at the project level, as a way to drive new behaviors that enable an organization to shift its performance trajectory into new dimensions unreachable with any of the previously described forms of intervention. We illustrate the power of this previously underexplored change mechanism with a case study, in which a well-established firm overcame barriers to change that were manifest in a wide range of organizational routines and behavioral norms that had been fostered by the pre-existing structural context of the firm.Strategic Change, Resource Allocation Process, Top-down Intervention

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

Single Top Production as a Probe of B-prime Quarks

We show how single top production at the LHC can be used to discover (and characterize the couplings of) B' quarks, which are an essential part of many natural models of new physics beyond the Standard Model. We present the B' effective model and concentrate on resonant production via a colored anomalous magnetic moment. Generally, B's preferentially decay into a single top quark produced in association with a W boson; thus, this production process makes associated single top production essential to B' searches at the LHC. We demonstrate the background processes are manageable and the signal cross section is sufficient to yield a large signal significance even during the 7 TeV LHC run. Specifically, we show that B' masses of 700 GeV or more can be probed. Moreover, if a B' is found, then the chirality of its coupling can be determined. Finally, we present signal cross sections for several different LHC energies.Comment: 19 pages, 7 figures, 1 tabl