Ce Ju · 2024
Neuroimaging tasks present significant challenges in signal processing and analysis due to factors such as low signal-to-noise ratios, high non-stationarity, and limited dataset sizes. Furthermore, understanding brain dynamics is complicated by the coupling mechanisms across various neuroimaging modalities. To address these challenges, my study introduces an alternative approach by formulating covariance-based neuroimaging data on symmetric positive definite manifolds. I integrate various geometric methods to model this data and develop geometric deep learning frameworks for multiple neuroimaging tasks, including EEG-based motor imagery classification and the multimodal fusion of simultaneous EEG-fMRI data.
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Betsy K. Murdock, oldham,Edina C. · Defense Technical Information Center (DTIC) · 1964
One experiment was conducted to compare three different methods of decoding binary digits. The message consisted of the eight letters A - H encoded in a Huffmantype instantaneous compact code; the redundancy of the message was approximately 8%. The binary digits were either presented sequentially (self-paced) and decoded with telegraph keys or the method of whole presentation was employed with either verbal or written readout. Decoding with the keys was both slower and less accurate than the other two methods; verbal decoding was faster (through no less accurate) than written decoding. It was suggested that decoding time can be subdivided into three components: processing time, reaction time, and recovery time. None of these three components seemed to vary consistently with the length of the code word (or the uncertainty of the symbol transmitted); instead, the relevant factors seemed to be stage of practice and distinctiveness of the stimuli (or responses). Finally, it appears that college students can achieve a rather high rate of decoding in a short period of time with a small message ensemble, whole presentation, and verbal report. (Author)
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Hideki Imai, Yasuji Nagasaka · Electronics and Communications in Japan (Part I Communications) · 1982
Abstract Double encoding is a method by which a new code is constructed by twofold encoding with two codes. Using this method, a code with excellent error‐correcting capabilities can be constructed from relatively simple codes. The decoding can also be made simpler compared with a single code with the same code length and the same error‐correcting performance. Both random and burst errors can be corrected. This paper describes a general, practical decoding method for use in a double‐encoding system. Algebraic decoding is used in both the first and the second stages, but the detailed information obtained in the first decoding is fully utilized in the second stage: the usual error‐correcting method is applied in the first decoding and a Chase algorithm is applied in the second decoding. A decoding system which is a practical simplification of the above method is also proposed. The proposed methods are compared with widely‐used conventional systems in terms of the decoding error rate; it is shown that the decoding error rate can drastically be improved.
Cited by 5
Julio A. Peraza, Taylor Salo, Michael C. Riedel et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2023
Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.
Cited by 1Open access
So‐Yeon Choi, Youngjoo Lee, Hoyoung Yoo · 2020
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Richard Taylor, Stephen M. Reilly · Journal of Experimental Psychology · 1970
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Lindy Comstock, Claudia Lainscsek, Vinícius Rezende Carvalho et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2021
State-of-the-art technologies in neural speech decoding utilize data collected from microwires or microarrays implanted directly into the cerebral cortex. Yet as a tool accessible only to individuals with implanted electrodes, speech decoding from devices of this nature is severely limited in its implementation, and cannot be considered a viable solution for widespread application. Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%), but only from very small classification tasks, with more complex tasks typically yielding a limited (20-50%) classification accuracy. We propose a novel combination of technologies in which transcranial magnetic stimulation (TMS) is first applied to augment the neural signals of interest, producing a greater signal-to-noise ratio in the EEG data. Next, delay differential analysis (DDA) – a cutting-edge computational method based on nonlinear dynamics – is implemented to capture the widest range of information available in the neural signal, by incorporating both linear and nonlinear dynamics.
Cited by 5Open access
Kimberly L Ray, Jessica Flannery, Jennifer L. Robinson, Ph.D. et al. · OSF Preprints (OSF Preprints) · 2024
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Matthias J. Becker, Matthew L. Bolton · Journal of Contemporary Antisemitism · 2022
Abstract This article introduces the pilot project “Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online.” The aim of the project is to analyse the frequency, content and linguistic structure of online antisemitism, with the eventual aim of developing AI machine learning that is capable of recognizing explicit and implicit forms of antisemitic hate speech. The initial focus is on comments found on the websites and social media platforms of major media outlets in the United Kingdom, Germany, and France. The article outlines the project’s multi-step methodological design, which seeks to capture the complexity, diversity and continual development of antisemitism online. The first step is qualitative content analysis. Rather than relying on surveys, here a pre-existing “real-world” data set-namely, threads of online comments responding to media stories judged to be potential triggers for antisemitic speech-is collected and analysed for antisemitic content and linguistic structure by expert coders. The second step is supervised machine learning. Here, models are trained to mimic the decisions of human coders and learn how antisemitic stereotypes are currently reproduced in different web milieus-including implicit forms. The third step is large-scale quantitative analyses in which frequencies and combinations of words and phrases are measured, allowing the exploration of trends from millions of pieces of data.
Cited by 6
Marianne Régis, Paola Pulcina, Dmitry A. Kretov · RNA Biology · 2026
RNA-binding proteins (RBPs) constitute a diverse class of proteins essential for every stage of the gene expression process. Many RBPs are also linked to human diseases and pathologies. Understanding the molecular grammar of RNA-protein interactions is critical for deciphering the regulatory RNA code. This review provides a comprehensive overview of Massively Parallel Binding Assays (MPBAs), high-throughput techniques that use large libraries of RNA or protein variants to systematically investigate RNA-protein interactions. We describe the underlying principles of both <i>in vitro</i> and <i>in vivo</i> approaches, their applications, as well as their strengths and weaknesses. We conclude by outlining future directions and challenges in the field that will help drive the development of novel methods to better understand the RBP recognition code.
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British Broadcasting Corp., London. Research Dept., Clarke, C.K.P. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1982
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Diego Mauricio Murillo Gómez, Filippo Maria Fazi, Mincheol Shin · DepositOnce · 2014
Ambisonics is a sound reproduction technique based on the decomposition of the sound field using spherical harmonics. The truncation in the number of coefficients used to recreate the sound field leads to reproduction artifacts which depend on the frequency and the listener spatial location. In this work, the performance of three different decoding methods (Basic, Max-rE and In-Phase) has been studied and evaluated in the light of the results of experimental measurements. The latter were performed using a spherical array composed of 40 uniformly distributed loudspeakers and a translating 29-channel linear microphone array. An error analysis is presented based on the difference between the desired and synthesized sound pressure and acoustic intensity field. The results indicate that, as expected, the size of the region of accurate sound field reconstruction reduces as frequency increases, but with different trends depending on the type of decoder implemented.
Cited by 9Open access
Murillo Gomez, Diego M., Fazi, Filippo Maria, Shin, Mincheol · ePrints Soton (University of Southampton) · 2014
Ambisonics is a sound reproduction technique based on the decomposition of the sound field using spherical harmonics. The truncation in the number of coefficients used to recreate the sound field leads to reproduction artifacts which depend on the frequency and the listener spatial location. In this work, the performance of three different decoding methods (Basic, Max-rE and In-Phase) has been studied and evaluated in the light of the results of experimental measurements. The latter were performed using a spherical array composed of 40 uniformly distributed loudspeakers and a translating 29-channel linear microphone array. An error analysis is presented based on the difference between the desired and synthesized sound pressure and acoustic intensity field. The results indicate that, as expected, the size of the region of accurate sound field reconstruction reduces as frequency increases, but with different trends depending on the type of decoder implemented
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Kai Li, Jinying Peng, Chengqi Yi · Fundamental Research · 2023
More than 160 types of post-transcriptional RNA modifications have been reported; there is substantial variation in modification type, abundance, site, and function across species, tissues, and RNA type. The recent development of high-throughput detection technology has enabled identification of diverse dynamic and reversible RNA modifications, including N6,2'-O-dimethyladenosine (m6Am), N1-methyladenosine (m1A), 5-methylcytosine (m5C), N6-methyladenosine (m6A), pseudouridine (Ψ), and inosine (I). In this review, we focus on eukaryotic mRNA modifications. We summarize their biogenesis, regulatory mechanisms, and biological functions, as well as high-throughput methods for detection of mRNA modifications. We also discuss challenges that must be addressed in mRNA modification research.
Cited by 22Open access
Ghaya Rekaya-Ben Othman · HAL (Le Centre pour la Communication Scientifique Directe) · 2018
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U.Yu. Komarova, V.I. Zatserkovniy, Vasyl Martsenyuk · Proceedings · 2018
Summary One of the most important functions of remote sensing data is providing valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format. This paper concerns methods of pictures fast classification on the basis of information included in them.
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K.P. Lim, Sullivan Gj, Thomas Wiegand · 2004
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Ghaya Rekaya-Ben Othman, Aymen Askri · HAL (Le Centre pour la Communication Scientifique Directe) · 2019
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Yifan Sun, Lara Dolecek · 2011
Linear programming (LP) decoders can outperform currently used message-passing decoders in channel coding applications, but require prohibitively large complexity on even moderately sized codes. Previous works have proposed complexity-reducing algorithms that either relax the problem or modify the number of constraints; however, little work is done in optimizing solver implementation. We show that popular LP solvers like LIPSOL may not be efficient for LP decoding (LPD), and that an equivalent dual LP problem can be solved with equal accuracy but much more quickly. We propose an improved primal-dual method (iPD-MPC) whose overall runtime for both problem formulations outpreform LIPSOL. Additionally, as an alternative for memory-limited systems, we propose an improved hybrid gradient descent and Newton's method (iGD-NM) that further decreases overall runtime. In this way, we make LPD more feasible for channel codes of practical lengths.
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Douglas O’Shaughnessy, T. Nagarajan Li · The Journal of the Acoustical Society of America · 2006
Different ways to improve the accuracy and performance of automatic speech recognition (ASR) systems are examined. Earlier research in the INRS group concentrated on improvements in adaptation techniques, to try to reduce the mismatch between system training and operating conditions. More recently, a concentration was made on improvements to the modeling and decoding methodology of the ASR systems. A two-level syllable model-based decoding approach is proposed here. At the first level, a syllable model-based decoding is performed to segment the utterances into syllable segments, and to identify which syllable group this segment belongs to. Then, at the second level, each segment is rescored using the phoneme model, where the possible phoneme sequences are constrained by the syllables in that syllable group. A new heuristic score is proposed to be added in hidden Markov model (HMM) decoding that indicates the degree of competition among different HMM states. Speech features are obtained from the posterior of this HMM approach and these features are used to further improve recognition results. The state evolution tracks are incorporated as features in an HMM-based recognizer. Experiments were carried out on a phoneme classification task (TIMIT) and these methods were found to show improvements compared to a baseline performance. [Work supported by NSERC-Canada and Prompt-Quebec.]
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