Abstract

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and dierent neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also aected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject’s age and classified them into dierent age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r 2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how dierent functional regions of the brain are correlated. We also analyzed how functional regions contribute dierently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing

Abstract

During healthy ageing, the brain networks undergo various topological and functional alterations. Previous studies have shown that the dedifferentiation of the functional modules could be one of the hallmarks of large-scale brain networks and alterations through the lifespan. This modular organiza- tion and alterations may be critically linked to a variety of neurodegenerative disorders and cognitive deficits encountered during ageing. In spite of accumulating evidence based on tracking static functional connectivity (FC) and modularity in characterizing dedifferentiation associated with ageing, there is a gap in understanding the brain dynamics of modular segregation and integration through the lifespan. Using the Cam-CAN dataset (young: 18-44, mean 32 years, old: 65-88, mean 75 years), we characterize the modular reorganization using dynamic measures like flexibility, to find characteristic nodes that make up the stable core and flexible periphery in the young and old age groups. In this study, we hypothesize that the nodes that exhibit higher flexibility in the older age groups will be negatively correlated with modularity since these nodes ‘compensate’ for the functional integration while ensuring that the segregation is efficient. Our results demonstrate that the regions from the Default Mode network (DMN) show a negative correlation with modularity in the old age groups. Further, nodes from Limbic, SensoriMotor (SMN) and Salience networks show a positive correlation with modularity. These networks that are responsible for higher-order cognitive functions, e.g., decision making, attentional control, cognitive flexibility are found to make up a stable core as evidenced by their low flexibility scores. We also trained various classifiers using node flexibility scores as features for the binary (young vs old) classification task. Support Vector Machine (SVM) with Gaussian kernel trained on a reduced-dimensional feature set gave the best classification results. The features (nodes) that are found to be important for classification concur with those identified through the data-driven network measures based analysis. In summary, we anticipate that these findings can help identify the regions that are responsible for the reorganization

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder predominantly found in children. The current behavior-based diagnosis of ASD is arduous and requires expertise. Therefore, it is appealing to develop an accurate computer-aided tool for diagnosing ASD. Although resting-state functional magnetic resonance imaging (rsfMRI) has proven to be successful in capturing the neural organization of the brain, automated detection of ASD using rsfMRI scans is a challenging task due to heterogeneity in the dataset and limited sample size. This paper proposes a Multilayer Perceptron (MLP) based classification model with auto encoder pretraining for classifying ASD from Typically Developing (TD) using rsfMRI scans obtained from the ABIDE-1 dataset. Our model achieves new state-of-the-art performance on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 74.82%. Further, we use the Integrated Gradients (IG) and DeepLIFT techniques to identify the correlations between brain regions that contribute most to the classification task. Our analysis identifies the following regions, Left Lingual Gyrus, Right Insula Lobe, Right Cuneus, Right Middle Frontal Gyrus, Left Superior Temporal Gyrus to be associated with ASD. Interestingly, these regions in the brain are primarily responsible for social cognition, language, attention, decision making and visual processing, which are known to be altered in ASD.

Abstract

We discuss the prospects for improving the precision on the hadronic corrections to the anomalous magnetic moment of the muon, and the plans of the Muon g −2 Theory Initiative to update the Standard Model prediction.

Abstract

In this work, we explore the use of Radio frequency (RF) based sensing in uniquely identifying nanoparticles and biosamples by utilizing multi-parameter characterization. Most conventional RF sensors suffer from an inability to perform proper identification due to the dependence of the methodology on a single parameter variation. This paper combats this drawback by proposing a technique that extracts multiple parameters from the frequency response of a narrow band, IDC based RF sensor for performing the analysis. Furthermore, to illustrate the method, the identification is performed for different samples, including Biotin, BSA-conjugated Biotin, Gold Nanorods and Silica-coated Goldnanospheres. The results indicate that multi-parameter characterization yields unique spectral signatures for all samples, and thus identification can efficiently be performed.

Abstract

This paper outlines a theoretical framework using which different automatic metrics can be designed for evaluation of Machine Translation systems. It introduces the concept of cognitive ease which depends on adequacy and lack of fluency. Thus, cognitive ease becomes the main parameter to be measured rather than comprehensibility. The framework allows the components of cognitive ease to be broken up and computed based on different linguistic levels etc. Independence of dimensions and linearly combining them provides for a highly modular approach. The paper places the existing automatic methods in an overall framework, to understand them better and to improve upon them in future. It can also be used to evaluate the newer types of MT systems, such as speech to speech translation and discourse translation..

Abstract

Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances. We propose a way to tailor a pre-trained BERT model for the downstream task via task-specific masking before the standard supervised fine-tuning. For this, a word list is first collected specific to the task. For example, if the task is sentiment classification, we collect a small sample of words representing both positive and negative sentiments. Next, a word’s importance for the task, called the word’s task score, is measured using the word list. Each word is then assigned a probability of masking based on its task score. We experiment with different masking functions that assign the probability of masking based on the word’s task score. The BERT model is further trained on MLM objective, where masking is done using the above strategy. Following this standard supervised fine-tuning is done for different downstream tasks. Results on these tasks show that the selective masking strategy outperforms random masking, indicating its effectiveness.

Abstract

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISUMM, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISUMM uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISUMM. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISUMM on other Indian languages. Our experiments of GAE-ISUMM in seven languages make the following observations: (i) it is competitive or better than state-ofthe-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries. We open-source our dataset and code 1 . Index Terms—component, formatting, style, styling, insert

Abstract

Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challenging when the source and target languages belong to different language families, resulting in differences in generated audio duration. This is further compounded by the original speaker’s rhythm, especially for extempore speech. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically. A prototype is developed for dubbing lectures into 9 Indian languages. A mean-opinion-score (MOS) is obtained for two languages, Hindi and Tamil, on two different courses. The output video is compared with the original video in terms of MOS (1-5) and lip synchronisation with scores of 4.09 and 3.74, respectively. The human effort also reduces by 75%.

Abstract

In computer assisted pronunciation training (CAPT), robust automatic models are critical for pronunciation assessment and mispronunciation detection and diagnosis (MDD). In the modelling, besides the audio data of second language (L2) learners, CAPT requires manually annotated ratings of over- all pronunciation quality, and the MDD uses manually anno- tated phonetic transcriptions. Though the pronunciation qual- ity and the mispronunciation are interdependent, to the best of our knowledge, none of the existing corpora contains both ratings and phonetic transcriptions. This could be due to the cost involved in obtaining phonetic transcriptions. However, a corpus with both kinds of information could benefit the re- searchers to obtain robust models by exploring the interde- pendencies. For addressing this, we develop voisTUTOR 2.0 corpus considering the existing voisTUTOR corpus referred to as voisTUTOR 1.0. We obtain phonetic transcriptions man- ually from a linguist for the entire Indian L2 learners’ En- glish audio data (26529 utterances approximately 14 hours) in voisTUTOR 1.0 for which overall quality ratings and bi- nary scores of factors influencing the pronunciation quality are available. A preliminary analysis of voisTUTOR 2.0 sug- gests that the phonetic errors correlated with the ratings and the binar