Families of spectral sets for Bernoulli convolutions

In this paper, we study the harmonic analysis of Bernoulli measures. We show a variety of orthonormal Fourier bases for the L^2 Hilbert spaces corresponding to certain Bernoulli measures, making use of contractive transfer operators. For other cases, we exhibit maximal Fourier families that are not orthonormal bases.Comment: 25 pages, same result

Additive spectra of the 1/4 Cantor measure

In this paper, we add to the characterization of the Fourier spectra for Bernoulli convolution measures. These measures are supported on Cantor subsets of the line. We prove that performing an odd additive translation to half the canonical spectrum for the 1/4 Cantor measure always yields an alternate spectrum. We call this set an additive spectrum. The proof works by connecting the additive set to a spectrum formed by odd multiplicative scaling.Comment: 9 pages, 1 figur

Scaling by 5 on a 1/4-Cantor Measure

Each Cantor measure (\mu) with scaling factor 1/(2n) has at least one associated orthonormal basis of exponential functions (ONB) for L^2(\mu). In the particular case where the scaling constant for the Cantor measure is 1/4 and two specific ONBs are selected for L^2(\mu), there is a unitary operator U defined by mapping one ONB to the other. This paper focuses on the case in which one ONB (\Gamma) is the original Jorgensen-Pedersen ONB for the Cantor measure (\mu) and the other ONB is is 5\Gamma. The main theorem of the paper states that the corresponding operator U is ergodic in the sense that only the constant functions are fixed by U.Comment: 34 page

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Stark deceleration of CaF molecules in strong- and weak-field seeking states

We report the Stark deceleration of CaF molecules in the strong-field seeking ground state and in a weak-field seeking component of a rotationally-excited state. We use two types of decelerator, a conventional Stark decelerator for the weak-field seekers, and an alternating gradient decelerator for the strong-field seekers, and we compare their relative merits. We also consider the application of laser cooling to increase the phase-space density of decelerated molecules.Comment: 10 pages, 8 figure

Mars Orbiter Study. Volume 2: Mission Design, Science Instrument Accommodation, Spacecraft Design

Spacecraft system and subsystem designs were developed at the conceptual level to perform either of two Mars Orbiter Missions, a Climatology Mission and an Aeronomy Mission. The objectives of these missions are to obtain and return data to increase knowledge of Mars

Augmented reality–assisted microsurgical resection of brain arteriovenous malformations: illustrative case

Background: Arteriovenous malformations (AVMs) of the brain are vessel conglomerates of feeding arteries and draining veins that carry a risk of spontaneous and intraoperative rupture. Augmented reality (AR)-assisted neuronavigation permits continuous, real-time, updated visualization of navigation information through a heads-up display, thereby potentially improving the safety of surgical resection of AVMs. Observations: The authors report a case of a 37-year-old female presenting with a 2-year history of recurrent falls due to intermittent right-sided weakness and increasing clumsiness in the right upper extremity. Magnetic resonance imaging, magnetic resonance angiography, and cerebral angiography of the brain revealed a left parietal Spetzler-Martin grade III AVM. After endovascular embolization of the AVM, microsurgical resection using an AR-assisted neuronavigation system was performed. Postoperative angiography confirmed complete obliteration of arteriovenous shunting. The postsurgical course was unremarkable, and the patient remains in excellent health. Lessons: Our case describes the operative setup and intraoperative employment of AR-assisted neuronavigation for AVM resection. Application of this technology may improve workflow and enhance patient safety

Harmonic analysis of iterated function systems with overlap

In this paper we extend previous work on IFSs without overlap. Our method involves systems of operators generalizing the more familiar Cuntz relations from operator algebra theory, and from subband filter operators in signal processing.Comment: 37 page