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.

ETutor: Online learning for personalized education

Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based education system. Personalizing education is challenging: (i) students need to be provided personalized teaching and training depending on their contexts (e.g. classes already taken, methods of learning preferred, etc.), (ii) for each specific context, the best teaching and training method (e.g type and order of teaching materials to be shown) must be learned, (iii) teaching and training should be adapted online, based on the scores/feedback (e.g. tests, quizzes, final exam, likes/dislikes etc.) of the students. Our personalized online system, e-Tutor, is able to address these challenges by learning how to adapt the teaching methodology (in this case what sequence of teaching material to present to a student) to maximize her performance in the final exam, while minimizing the time spent by the students to learn the course (and possibly dropouts). We illustrate the efficiency of the proposed method on a real-world eTutor platform which is used for remedial training for a Digital Signal Processing (DSP) course. © 2015 IEEE

Adaptive ensemble learning with confidence bounds for personalized diagnosis

With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and high-dimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multimodal medical data. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift

Adaptive Ensemble Learning with Confidence Bounds

Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long-run (asymptotic) and short-run (rate of learning) performance guarantees. Moreover, our approach yields performance guarantees with respect to the optimal local prediction strategy, and is also able to adapt its predictions in a data-driven manner. We illustrate the performance of Hedged Bandits in the context of medical informatics and show that it outperforms numerous online and offline ensemble learning methods. © 2016 IEEE

Residential mobility and local housing-market differences

The authors extend previous literature on variations in mobility rates across local housing markets by examining the linkage of mobility rates at the household level to the structure of local housing markets. The results suggest that residential mobility rates differ widely across local housing markets, substantiating the view that residential relocation is intimately intertwined with conditions at the local level. Local housing-market conditions also have different effects on mobility rates for renters and owner-occupiers. The results suggest that variation in residential mobility rates across housing markets can be in part explained by level of urbanization, the tenure structure, the degree of government intervention, and the size of the housing market. Remarkably, these differences in local housing markets cannot be seen to be related to housing-market features only. The results suggest that these differences can also be attributed to the behavior or attitude of households with respect to housing

Overcoming Psychologism. Twardowski on Actions and Products

This paper is about the topic of psychologism in the work of Kazimierz Twardowski and my aim is to revisit this important issue in light of recent publications from, and on Twardowski’s works. I will first examine the genesis of psychologism in the young Twardowski’s work; secondly, I will examine Twardowski’s picture theory of meaning and Husserl’s criticism in Logical Investigations; the third part is about Twardowski’s recognition and criticism of his psychologism in his lectures on the psychology of thinking; the fourth and fifth parts provide an overview of Twardowski’s paper “Actions and Products” while the sixth part addresses the psychologism issue in the last part of this paper through the delineation of psychology and the humanities. I shall conclude this study with a brief assessment of Twardowski’s solution to psychologism

Hints of (trans-Planckian) asymptotic freedom in semiclassical cosmology

We employ the semiclassical approximation to the Wheeler-DeWitt equation in the spatially flat de Sitter Universe to investigate the dynamics of a minimally coupled scalar field near the Planck scale. We find that, contrary to naive intuition, the effects of quantum gravitational fluctuations become negligible and the scalar field states asymptotically approach plane-waves at very early times. These states can then be used as initial conditions for the quantum states of matter to show that each mode essentially originated in the minimum energy vacuum. Although the full quantum dynamics cannot be solved exactly for the case at hand, our results can be considered as supporting the general idea of asymptotic safety in quantum gravity.Comment: 11 pages, 2 figures; replaced to match content of published versio

Where does Cosmological Perturbation Theory Break Down?

We apply the effective field theory approach to the coupled metric-inflaton system, in order to investigate the impact of higher dimension operators on the spectrum of scalar and tensor perturbations in the short-wavelength regime. In both cases, effective corrections at tree-level become important when the Hubble parameter is of the order of the Planck mass, or when the physical wave number of a cosmological perturbation mode approaches the square of the Planck mass divided by the Hubble constant. Thus, the cut-off length below which conventional cosmological perturbation theory does not apply is likely to be much smaller than the Planck length. This has implications for the observability of "trans-Planckian" effects in the spectrum of primordial perturbations.Comment: 25 pages, uses FeynM

Contextual learning for unit commitment with renewable energy sources

In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule to choose based on the context information such as past load demand and weather condition. CLUC does not require any prior knowledge on the uncertainties such as the load demand and the renewable power outputs, and learns them over time using the context information. We characterize the performance of CLUC analytically, and prove its optimality in terms of the long-term average cost. Through the simulation results, we show the performance of CLUC and the effectiveness of utilizing the context information in the UC problem. © 2016 IEEE