New photonic conservation laws in parametric nonlinear optics

Conservation laws are one of the most generic and useful concepts in physics. In nonlinear optical parametric processes, conservation of photonic energy, momenta and parity often lead to selection rules, restricting the allowed polarization and frequencies of the emitted radiation. Here we present a new scheme to derive conservation laws in optical parametric processes in which many photons are annihilated and a single new photon is emitted. We then utilize it to derive two new such conservation laws. Conservation of reflection-parity (RP) arises from a generalized reflection symmetry of the polarization in a superspace, analogous to the superspace employed in the study of quasicrystals. Conservation of space-time-parity (STP) similarly arises from space-time reversal symmetry in superspace. We explore these new conservation laws numerically in the context of high harmonic generation and outline experimental set-ups where they can be tested

The effective string spectrum in the orthogonal gauge

The low-energy effective action on long string-like objects in quantum field theory, such as confining strings, includes the Nambu-Goto action and then higher-derivative corrections. This action is diffeomorphism-invariant, and can be analyzed in various gauges. Polchinski and Strominger suggested a specific way to analyze this effective action in the orthogonal gauge, in which the induced metric on the worldsheet is conformally equivalent to a flat metric. Their suggestion leads to a specific term at the next order beyond the Nambu-Goto action. We compute the leading correction to the Nambu-Goto spectrum using the action that includes this term, and we show that it agrees with the leading correction previously computed in the static gauge. This gives a consistency check for the framework of Polchinski and Strominger, and helps to understand its relation to the theory in the static gauge.Comment: 21 page

Reading handwritten digits: a ZIP code recognition system

A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail

Active Learning During the Performance Task

terns filtered through remains large. Since patterns that are not queried are exactly those which the learner "knows", it makes sense to use query filtering during the performance task. This results in the following on-site model (Matan 1995) that considers the loss associated with classifying or querying and must enforce a policy to minimize the total loss. The system receives a pattern. It responds with one of the following actions: 1. Classify The payoff is +G if correct and \GammaM if incorrect. 2. Query A teacher will classify the pattern and the classifier may or may not adjust accordingly. Payoff is \GammaQ for the query and +G for correct classification (assuming the teacher is reliable). This model is related to previously studied models: ffl The classify/reject model: many classification applications reject low confidence patterns to ensure a low

On Voting Ensembles of Classifiers (Extended Abstract)

We study the classification ability of majority ensembles of classifiers. A majority ensemble classifies a pattern by letting each member of the ensemble cast a single vote for the correct class and deciding according to a simple majority or special majority vote. We give upper and lower bounds on the classification performance of a majority ensemble as a function of the classification performances of its individual members

Staff Scheduling for Inbound Call Centers and Customer Contact Centers

The staff scheduling problem is a critical problem in the call center (or more generally, customer contact center) industry. This paper describes Director, a staff scheduling system for contact centers. Director is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service quality metrics. Director has been successfully deployed at over 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this paper