POCS-based framework of signal reconstruction from generalized non-uniform samples

We formalize the use of projections onto convex sets (POCS) for the reconstruction of signals from non-uniform samples in their highest generality. This covers signals in any Hilbert space $\mathscr H$, including multi-dimensional and multi-channel signals, and samples that are most generally inner products of the signals with given kernel functions in $\mathscr H$. An attractive feature of the POCS method is the unconditional convergence of its iterates to an estimate that is consistent with the samples of the input, even when these samples are of very heterogeneous nature on top of their non-uniformity, and/or under insufficient sampling. Moreover, the error of the iterates is systematically monotonically decreasing, and their limit retrieves the input signal whenever the samples are uniquely characteristic of this signal. In the second part of the paper, we focus on the case where the sampling kernel functions are orthogonal in $\mathscr H$, while the input may be confined in a smaller closed space $\mathscr A$ (of bandlimitation for example). This covers the increasingly popular application of time encoding by integration, including multi-channel encoding. We push the analysis of the POCS method in this case by giving a special parallelized version of it, showing its connection with the pseudo-inversion of the linear operator defined by the samples, and giving a multiplierless discrete-time implementation of it that paradoxically accelerates the convergence of the iteration.Comment: 12 pages, 4 figures, 1 tabl

Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance

This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted into spiking domain with novel conversion schemes. The quality of the siamese embeddings of input images was evaluated by measuring the classifier performance for different dataset coding types. The models achieved performance similar to existing SNN-based approaches (F1-score of up to 0.9386) while using only about 15% of hidden layer neurons to classify each example. Furthermore, models which did not employ a sparse neural code were about 45% slower than their sparse counterparts. These properties make the model suitable for low energy consumption and low prediction latency applications.Comment: Revised paper accepted for presentation at 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

Recovery of Bandlimited Signal Based on Nonuniform Derivative Sampling

Publication in the conference proceedings of SampTA, Bremen, Germany, 201

The level of dental anxiety and dental status in adult patients

Background: The present study aimed to assess potential correlation between dental anxiety and overall dental status in adult patients, in consideration of the frequency of dental appointments and individual dental hygiene practices. Materials and Methods: Individual dental anxiety levels were assessed with the aid of the Corah’s dental anxiety scale (DAS). The study embraced 112 patients of the University Dental Clinic, Kraków. Following clinical and X-ray exams, respectively, decayed, missing and filled teeth (DMFT) index and dental treatment index (DTI) were computed for each study subject. Results: Mean DAS among the 112 subjects under study was 9.41 standard deviation (SD = 3.36). Mean DMFT value was 15.86 (SD = 7.00), whereas DTI value was 0.76 (SD = 0.27). The number of decayed teeth and an individual dental anxiety level were found to be correlated (r = 0.26). Higher dental anxiety correlated with lower DTI value (r = −0.22) and lesser frequency of dental appointments (r = 0.22). Conclusions: Individual dental anxiety level appears to impact overall dental status, frequency of dental appointments and everyday oral health practices. Every conceivable effort should therefore be undertaken with a view to effectively diminishing dental anxiety levels in the patients. How to cite the article: Dobros K, Hajto-Bryk J, Wnęk A, Zarzecka J, Rzepka D. The level of dental anxiety and dental status in adult patients. J Int Oral Health 2014;6(3):11-4

OME-Zarr : A cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks