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

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

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

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

Improving Workflow Efficiency for Mammography Using Machine Learning.

OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. METHODS: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient's nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. RESULTS: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. CONCLUSION: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies

Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e.g. to alert them to potential biases or oversights on their part. To do so, it is necessary to develop interpretable representations of how agents make decisions and how this process changes over time as the agent learns online in reaction to the accrued experience. To then understand the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. By interpreting actions within a potential outcomes framework, we introduce a meaningful mapping based on agents choosing an action they believe to have the greatest treatment effect. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them, using a novel architecture built upon an expressive family of deep state-space models. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time