Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data

Time Resolution of a Few Nanoseconds in Silicon Strip Detectors Using the APV25 Chip

The APV25 front-end chip for the CMS Silicon Tracker has a peaking time of 50 ns, but confines the signal to a single clock period (=bunch crossing) with its internal “deconvolution” filter. This method requires a beam-synchronous clock and thus cannot be applied to a (quasi-) continuous beam. Nevertheless, using the multi-peak mode of the APV25, where 3 (or 6,9,12,...) consecutive shaper output samples are read out, the peak time can be reconstructed externally with high precision. Thus, offtime hits can be discarded which results in significant occupancy reduction. We will describe this method, results from beam tests and the intended implementation in an upgrade of the BELLE Silicon Vertex Detector

Construction and Performance of a Double-Sided Silicon Detector Module Using the Origami Concept

The APV25 front-end chip with short shaping time will be used in the Belle II Silicon Vertex Detector (SVD) in order to achive low occupancy. Since fast amplifiers are more susceptible to noise caused by their capacitive input load, they have to be placed as close to the sensor as possible. On the other hand, material budget inside the active volume has to be kept low in order to constrain multiple scattering. We built a low mass sensor module with double-sided readout, where thinned APV25 chips are placed on a single flexible circuit glued onto one side of the sensor. The interconnection to the other side is done by Kapton fanouts, which are wrapped around the edge of the sensor, hence the name Origami. Since all front-end chips are aligned in a row on the top side of the module, cooling can be done by a single aluminum pipe. The performance of the Origami module was evaluated in a beam test at CERN in August 2009, of which first results are presented here

Readout and Data Processing Electronics for the Belle-II Silicon Vertex Detector

A prototype readout system has been developed for the future Belle-II Silicon Vertex Detector at the Super-KEK-B factory in Tsukuba, Japan. It will receive raw data from double-sided sensors with a total of approximately 240,000 strips read out by APV25 chips at a trigger rate of up to 30kHz and perform strip reordering, pedestal subtraction, a two-pass common mode correction and zero suppression in FPGA firmware. Moreover, the APV25 will be operated in multi-peak mode, where (typically) six samples along the shaped waveform are used for precise hit-time reconstruction which will also be implemented in FPGAs using look-up tables

The Optimal Single Copy Measurement for the Hidden Subgroup Problem

The optimization of measurements for the state distinction problem has recently been applied to the theory of quantum algorithms with considerable successes, including efficient new quantum algorithms for the non-abelian hidden subgroup problem. Previous work has identified the optimal single copy measurement for the hidden subgroup problem over abelian groups as well as for the non-abelian problem in the setting where the subgroups are restricted to be all conjugate to each other. Here we describe the optimal single copy measurement for the hidden subgroup problem when all of the subgroups of the group are given with equal a priori probability. The optimal measurement is seen to be a hybrid of the two previously discovered single copy optimal measurements for the hidden subgroup problem.Comment: 8 pages. Error in main proof fixe

An efficient quantum algorithm for the hidden subgroup problem in extraspecial groups

Extraspecial groups form a remarkable subclass of p-groups. They are also present in quantum information theory, in particular in quantum error correction. We give here a polynomial time quantum algorithm for finding hidden subgroups in extraspecial groups. Our approach is quite different from the recent algorithms presented in [17] and [2] for the Heisenberg group, the extraspecial p-group of size p3 and exponent p. Exploiting certain nice automorphisms of the extraspecial groups we define specific group actions which are used to reduce the problem to hidden subgroup instances in abelian groups that can be dealt with directly.Comment: 10 page

Release of Mast Cell Tryptase into Saliva: A Tool to Diagnose Food Allergy by a Mucosal Challenge Test?

Background: Our aim was to examine whether measurement of the saliva mast cell tryptase (MCT) concentrations before and after a mucosal challenge test with the offending food would be helpful in diagnosing food allergy. Methods: We performed a retrospective analysis of 44 food challenge tests performed in 38 patients between 2006 and 2009. Patients with a suspected history of food allergy chewed the food until they developed symptoms or until the amount of time known from the patients' history to usually be required for the provocation of symptoms had passed. In 5 patients, saliva samples for the measurement of MCT were collected at minutes 0, 1, 4, 8, 11, and 16 after the first onset of symptoms. The remainder of the patients only had samples taken before chewing and 4 min after the end of the test period. Results: During repeated measurements, MCT peaked about 4 min after the onset of symptoms (p = 0.028). During 33 of the 44 tests (75.0%), we observed oral symptoms during testing; after 25 of the 33 (75.8%) tests evoking symptoms, the saliva MCT concentration increased. The MCT increase was negative in all other tests where no oral symptoms could be provoked. Conclusions: The measurement of saliva MCT 4 min after the onset of symptoms may be helpful to diagnose food allergy. Because of numerous confounding variables, however, a negative saliva MCT increase does not exclude food allergy. Copyright (C) 2011 S. Karger AG, Base

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Optimal phase estimation in quantum networks

We address the problem of estimating the phase phi given N copies of the phase rotation u(phi) within an array of quantum operations in finite dimensions. We first consider the special case where the array consists of an arbitrary input state followed by any arrangement of the N phase rotations, and ending with a POVM. We optimise the POVM for a given input state and fixed arrangement. Then we also optimise the input state for some specific cost functions. In all cases, the optimal POVM is equivalent to a quantum Fourier transform in an appropriate basis. Examples and applications are given.Comment: 9 pages, 2 figures; this is an extended version of arXiv:quant-ph/0609160. v2: minor corrections in reference