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

Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the FashionMNIST dataset and study the effect of repeating tensor network layers on ansatz’s expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach ≥ 98% test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits. Keywords: Quantum Computing, Quantum Machine Learning, Quantum Many-body Systems, QuantumClassical Algorithms

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

Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19

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

Computational Paninian Grammar (CPG) model helps in decoding a natural language expression as a series of modifier-modified relations and therefore facilitates in identifying dependency relations closer to language/context semantics compared to the usual stanford dependency relations. However, the importance of this CPG dependency scheme has not been studied in the context of multi-modal vision and language applications. At IIIT-H, we plan to perform a novel study to explore the potential advantages and disadvantages of CPG framework in a vision-language navigation task setting, a popular and challenging multimodal grounding task.

Abstract

Depression is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act. Detecting depression is essential as it must be treated early to avoid painful consequences. Nowadays, people are broadcasting how they feel via posts and comments. Using social media, we can extract many comments related to depression and use NLP techniques to train and detect depression. This work presents the submission of the DepressionOne team at LT-EDI-2022 for the shared task, detecting signs of depression from social media text. The depression data is small and unbalanced. Thus, we have used oversampling and undersampling methods such as SMOTE and RandomUnderSampler to represent the data. Later, we used machine learning methods to train and detect the signs of depression.

Abstract

Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion. In this paper, we describe our approach to classify homophobia and transphobia in social media comments. We used an ensemble of transformer based models to build our classifier. Our model ranked 2nd for English, 8th for Tamil and 10th for Tamil-English

Abstract

Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. The transformer models, specifically the masked-language models (MLM), have shown remarkable performance in NER in recent times. With growing data in different online platforms, there is a need for NER in other languages too. NER remains underexplored in Indian languages due to the lack of resources and tools. Our contributions in this paper include (i) Two annotated NER datasets for the Telugu language in multiple domains: Newswire Dataset (ND) and Medical Dataset (MD), and we combined ND and MD to form a Combined Dataset (CD) (ii) Comparison of the finetuned Telugu pretrained transformer models (BERT-Te, RoBERTa-Te, and ELECTRA-Te) with other baseline models (CRF, LSTM-CRF, and BiLSTM-CRF) (iii) Further investigation of the performance of Telugu pretrained transformer models against the multilingual models mBERT (Devlin et al., 2018), XLM-R (Conneau et al., 2020), and IndicBERT (Kakwani et al., 2020). We find that pretrained Telugu language models (BERTTe and RoBERTa) outperform the existing pretrained multilingual and baseline models in NER. On a large dataset (CD) of 38,363 sentences, the BERT-Te achieves a high F1-score of 0.80 (entity-level) and 0.75 (token-level). Further, these pretrained Telugu models have shown state-of-the-art performance on various