Dario Dematties

Dario Dematties
Northwestern University | NU · Northwestern-Argonne Institute of Science and Engineering

PhD

About

10
Publications
927
Reads
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15
Citations
Introduction
Dario Dematties currently works at Northwestern-Argonne Institute of Science and Engineering, Northwestern University. Dario does research in Machine Learning at the Edge by means of Federated Learning, High Performance Computing (HPC) and Self-Supervised Learning.
Additional affiliations
May 2020 - November 2021
National Scientific and Technical Research Council
Position
  • PostDoc Position
May 2013 - April 2020
Universidad de Buenos Aires
Position
  • PhD Student
Education
May 2013 - April 2020
Universidad de Buenos Aires
Field of study
  • Engineering

Publications

Publications (10)
Article
Full-text available
Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recogniti...
Preprint
Phase Correlation (PC) is a well-known method for estimating cloud motion vectors (CMV) from infrared and visible spectrum images. Commonly phase-shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the block-wise PC method by changing the block size...
Article
Full-text available
It is assumed that semantic transparency in compounds depends on integration of constituents and head transparency. Spanish verb-noun compounds are semantically and morphologically exocentric, lacking a head. We studied if verb argument structure determines differences in semantic integration and morphological decomposition. During a lexical decisi...
Article
Full-text available
Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions b...
Article
Full-text available
Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background....
Preprint
Full-text available
Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background....
Article
Full-text available
A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data con...
Conference Paper
Full-text available
The interdisciplinary field of neuroscience has made significant progress in recent decades, providing the scientific community in general with a new level of understanding on how the brain works beyond the store-and-fire model found in traditional neural networks. Meanwhile, Machine Learning (ML) based on established models has seen a surge of int...
Article
Full-text available
Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units-such as phonemes-are extr...

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Projects

Project (1)
Project
Many computational theories have been developed in order to improve artificial phonetic classification performance from linguistic auditory streams. However, little attention has been paid to neurophysiological features recently found in cortical tissue in a context in which basic linguistic units–such as phonemes–are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We hypothesize that such features may be relevant for perception invariance in the brain. In this project we introduce a biologically inspired and completely unsupervised neurocomputational approach which incorporates key neurophysiological and anatomical cortical properties. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. We propose a model that leverages the phonetic classification performance of the supervised Support Vector Machine (SVM) technique for monosyllabic, disyllabic and trisyllabic word classification tasks against environmental disturbances such as white noise, reverberation, and pitch variations. Our implementation outperforms sophisticated multiresolution spectro-temporal auditory feature representations of phonetic linguistic streams of data. This research has significant potential to open up new -and more biologically accurate- alternatives to overcome limitations in current deep feature extraction methods.