The Attentional Control of Reading: Insights from Behavior, Imaging and Development

The process by which the initially attention-requiring task of transforming scribbles into meaningful concepts eventually becomes facile remains a central riddle of cognitive neuroscience. This body of work represents an effort to provide forward movement in answering the question of how attentional control mediates the process of reading, both by considering different stages of reading competence (development) and by seeking convergence between types of evidence (behavior and imaging). Inspired by a study published by Balota and colleagues in 2000, the paradigm used throughout this work involves comparing a simple speeded reading task vs. a regularize ( sound out ) task (Balota et al. 2000). In the first data chapter, I replicate the essential findings of the Balota et al. study in 2 young adult cohorts, confirming that stimulus characteristics, including lexicality and frequency, influence reading task performance in a manner that is modulated by top-down attentional control. I furthermore argue that the reaction time (RT) patterns are consistent with 2 distinct mechanisms by which top-down attentional control interacts with reading processes, pathway control and response checking. I then present evidence, motivated by the 2-mechanism hypothesis, that 2 sets of brain regions, including members of previously defined attentional control networks, show separable activity patterns that map nicely onto roles reflecting pathway control and response checking. In the second data chapter, I show that 8-10 year old children, like young adults, can perform the regularize task. Unexpectedly, the early readers are faster than the experienced readers to regularize, and this speed advantage for children holds for both words and pseudowords. Because children are slower than adults across a range of cognitive tasks (e.g., Kail 1991) - with children showing particular immaturity with regard to inhibiting prepotent responses (e.g., Davidson et al. 2006) - the developmental observation is remarkable in and of itself. Complemented by a cadre of post hoc analyses, the age groups differences can also be interpreted as additional support for the 2-mechanism interaction of attention and reading. Together, these results suggest that dissociable subcomponents of attentional control interact with subcomponents of reading processing, and that these interactions are dynamic across skill development and across task demands

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Graph Neural Networks for Molecules

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.Comment: A chapter for the book "Machine Learning in Molecular Sciences". 31 pages, 4 figure

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Non-Intrusive Subscriber Authentication for Next Generation Mobile Communication Systems

Merged with duplicate record 10026.1/753 on 14.03.2017 by CS (TIS)The last decade has witnessed massive growth in both the technological development, and the consumer adoption of mobile devices such as mobile handsets and PDAs. The recent introduction of wideband mobile networks has enabled the deployment of new services with access to traditionally well protected personal data, such as banking details or medical records. Secure user access to this data has however remained a function of the mobile device's authentication system, which is only protected from masquerade abuse by the traditional PIN, originally designed to protect against telephony abuse. This thesis presents novel research in relation to advanced subscriber authentication for mobile devices. The research began by assessing the threat of masquerade attacks on such devices by way of a survey of end users. This revealed that the current methods of mobile authentication remain extensively unused, leaving terminals highly vulnerable to masquerade attack. Further investigation revealed that, in the context of the more advanced wideband enabled services, users are receptive to many advanced authentication techniques and principles, including the discipline of biometrics which naturally lends itself to the area of advanced subscriber based authentication. To address the requirement for a more personal authentication capable of being applied in a continuous context, a novel non-intrusive biometric authentication technique was conceived, drawn from the discrete disciplines of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric where variations in the behavioural stimulus of the human voice (due to the propagation effects of acoustic waves within the human head), are used to verify the identity o f a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. Firstly, the generic authentication procedures of registration and verification are automated within a prototype implementation. Secondly, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototypes performance realised early system error rates of; FNMR = 6% and FMR = 0.025%. The results clearly demonstrate the authentication capabilities of this novel biometric approach and the contribution this new work can make to the protection of subscriber data in next generation mobile networks.Orange Personal Communication Services Lt

Robust Machine Learning by Integrating Context

Intelligent software has the potential to transform our society. It is becoming the building block for many systems in the real world. However, despite the excellent performance of machine learning models on benchmarks, state-of-the-art methods like neural networks often fail once they encounter realistic settings. Since neural networks often learn correlations without reasoning with the right signals and knowledge, they fail when facing shifting distributions, unforeseen corruptions, and worst-case scenarios. Since neural networks are black-box models, they are not interpretable or trusted by the user. We need to build robust models for machine learning to be confidently and responsibly deployed in the most critical applications and systems. In this dissertation, I introduce our robust machine learning systems advancements by tightly integrating context into algorithms. The context has two aspects: the intrinsic structure of natural data, and the extrinsic structure from domain knowledge. Both are crucial: By capitalizing on the intrinsic structure in natural data, my work has shown that we can create robust machine learning systems, even in the worst case, an analytical result that also enjoys strong empirical gains. Through integrating external knowledge, such as the association between tasks and causal structure, my framework can instruct models to use the right signals for inference, enabling new opportunities for controllable and interpretable models. This thesis consists of three parts. In the first part, I aim to cover three works that use the intrinsic structure as a constraint to achieve robust inference. I present our framework that performs test-time optimization to respect the natural constraint, which is captured by self-supervised tasks. I illustrate that test-time optimization improves out-of-distribution generalization and adversarial robustness. Besides the inference algorithm, I show that intrinsic structure through discrete representations also improves out-of-distribution robustness. In the second part of the thesis, I then detail my work using external domain knowledge. I first introduce using causal structure from external domain knowledge to improve domain generalization robustness. I then show how the association of multiple tasks and regularization objectives helps robustness. In the final part of this dissertation, I show three works on trustworthy and reliable foundation models, a general-purpose model that will be the foundation for many AI applications. I show a framework that uses context to secure, interpret, and control foundation models

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics