Intersecting Branes in Matrix Theory

We construct BPS states in the matrix description of M-theory. Starting from a set of basic M-theory branes, we study pair intersections which preserve supersymmetry. The fractions of the maximal supersymmetry obtained in this way are 1/2, 1/4, 1/8, 3/16 and 1/16. In explicit examples we establish that the matrix BPS states correspond to (intersecting) brane configurations that are obtained from the d=11 supersymmetry algebra. This correspondence for the 1/2 supersymmetric branes includes the precise relations between the charges.Comment: 11 pages, LaTeX, no figures, minor changes, shortened version to be published in Physics Letters

Extracting New Physics from the CMB

We review how initial state effects generically yield an oscillatory component in the primordial power spectrum of inflationary density perturbations. These oscillatory corrections parametrize unknown new physics at a scale $M$ and are potentially observable if the ratio $H_{infl}/M$ is sufficiently large. We clarify to what extent present and future CMB data analysis can distinguish between the different proposals for initial state corrections.Comment: Invited talk by B. Greene at the XXII Texas Symposium on Relativistic Astrophysics, Stanford University, 13-17 December 2004, (TSRA04-0001), 8 pages, LaTeX, some references added, added paragraph at the end of section 2 and an extra note added after the conclusions regarding modifications to the large k power spectra deduced from galaxy survey

Oscillations in the bispectrum

There exist several models of inflation that produce primordial bispectra that contain a large number of oscillations. In this paper we discuss these models, and aim at finding a method of detecting such bispectra in the data. We explain how the recently proposed method of mode expansion of bispectra might be able to reconstruct these spectra from separable basis functions. Extracting these basis functions from the data might then lead to observational constraints on these models.Comment: 6 pages, 2 figures, submitted to JOP: Conference Series, PASCOS 201

Spacetime-Filling Branes and Strings with Sixteen Supercharges

We discuss branes whose worldvolume dimension equals the target spacetime dimension, i.e. ``spacetime-filling branes''. In addition to the D9-branes, there are 9-branes in the NS-NS sectors of both the IIA and IIB strings. The worldvolume actions of these branes are constructed, via duality, from the known actions of branes with codimension larger than zero. Each of these types of branes is used in the construction of a string theory with sixteen supercharges by modding out a type II string by an appropriate discrete symmetry and adding 32 9-branes. These constructions are related by a web of dualities and each arises as a different limit of the Horava-Witten construction.Comment: 43 pages, LaTeX, 8 figures, uses html.sty, version to appear in Nucl. Phys.

Holographic duals of the <i>N</i> = 1* gauge theory

We use the long-wavelength effective theory of black branes (blackfold approach) to perturbatively construct holographic duals of the vacua of the N = 1* supersymmetric gauge theory. Employing the mechanism of Polchinski and Strassler, we consider wrapped black five-brane probes with D3-brane charge moving in the perturbative supergravity back-grounds corresponding to the high- and low-temperature phases of the gauge theory. Our approach recovers the results for the brane potentials and equilibrium configurations known in the literature in the extremal limit, while away from extremality we find metastable black D3-NS5 configurations with horizon topology ℝ3 × S2 × S3 in certain regimes of parameter space, which cloak potential brane singularities. We uncover novel features of the phase diagram of the N = 1* gauge theory in different ensembles and provide further evidence for the appearance of metastable states in holographic backgrounds dual to confining gauge theories.</p

Multi-Level Visual Alphabets

A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with a hierarchy of representations, each level feeding into the next one, but based on features that are not abstract but directly relevant to the task at hand. Explorative benchmark experiments are carried out on face images to investigate and explain the impact of the key parameters such as pattern size, number of prototypes, and distance measures used. Results show that adding an additional middle layer improves results, by encoding the spatial co-occurrence of lower-level pattern prototypes

Optimising Human-AI Collaboration by Learning Convincing Explanations

Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision support systems. This poses challenges, particularly when models have hard-to-detect failure modes and are able to take actions without oversight. In order to handle this challenge, we propose a method for a collaborative system that remains safe by having a human ultimately making decisions, while giving the model the best opportunity to convince and debate them with interpretable explanations. However, the most helpful explanation varies among individuals and may be inconsistent across stated preferences. To this end we develop an algorithm, Ardent, to efficiently learn a ranking through interaction and best assist humans complete a task. By utilising a collaborative approach, we can ensure safety and improve performance while addressing transparency and accountability concerns. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations, which we validate through extensive simulations alongside a user study involving a challenging image classification task, demonstrating consistent improvement over competing systems