On an easy transition from operator dynamics to generating functionals by Clifford algebras

Clifford geometric algebras of multivectors are treated in detail. These algebras are build over a graded space and exhibit a grading or multivector structure. The careful study of the endomorphisms of this space makes it clear, that opposite Clifford algebras have to be used also. Based on this mathematics, we give a fully Clifford algebraic account on generating functionals, which is thereby geometric. The field operators are shown to be Clifford and opposite Clifford maps. This picture relying on geometry does not need positivity in principle. Furthermore, we propose a transition from operator dynamics to corresponding generating functionals, which is based on the algebraic techniques. As a calculational benefit, this transition is considerable short compared to standard ones. The transition is not injective (unique) and depends additionally on the choice of an ordering. We obtain a direct and constructive connection between orderings and the explicit form of the functional Hamiltonian. These orderings depend on the propagator of the theory and thus on the ground state. This is invisible in path integral formulations. The method is demonstrated within two examples, a non-linear spinor field theory and spinor QED. Antisymmetrized and normal-ordered functional equations are derived in both cases.Comment: 23p., 76kB, plain LaTeX, [email protected]

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

Principles of Explanatory Debugging to personalize interactive machine learning

How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system

End-user interactions with intelligent and autonomous systems.

Systems that learn from or personalize themselves to users are quickly becoming mainstream yet interaction with these systems is limited and often uninformative for the end user. This workshop focuses on approaches and challenges to explore making these systems transparent, controllable and ultimately trustworthy to end users. The aims of the workshop are to help establish connections among researchers and industrial practitioners using real-world problems as catalysts to facilitate the exchange of approaches, solutions, and ideas about how to better support end users

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Resolving the L/T transition binary SDSS J2052-1609 AB

Binaries provide empirical key constraints for star formation theories, like the overall binary fraction, mass ratio distribution and the separation distribution. They play a crucial role to calibrate the output of theoretical models, like absolute magnitudes, colors and effective temperature depending on mass, metallicity and age. We present first results of our on-going high-resolution imaging survey of late type brown dwarfs. The survey aims at resolving tight brown dwarf binary systems to better constrain the T dwarf binary fraction. We intent to follow-up the individual binaries to determine orbital parameters. Using NACO at the VLT we performed AO-assisted near-infrared observations of SDSS J2052-1609. High-spatial resolution images of the T1 dwarf were obtained in H and Ks filters. We resolved SDSS J2052-1609 into a binary system with a separation of 0.101" \pm 0.001". Archival data from HST/NICMOS taken one year previous to our observations proves the components to be co-moving. Using the flux ratio between the components we infer J, H and Ks magnitudes for the resolved system. From the near-IR colors we estimate spectral types of T1 +1 -4 and T2.5 \pm 1 for component A and B, respectively. A first estimate of the total system mass yields Mtot > 78 Mjup, assuming a circular orbit.Comment: 5 pages, 3 figures, 3 tables, accepted for publication by A&