Abstract

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.

Abstract

This paper describes a framework for the object-goal navigation (ObjectNav) task, which requires a robot to find and move to an instance of a target object class from a random starting position. The framework uses a history of robot trajectories to learn a Spatial Relational Graph (SRG) and Graph Convolutional Network (GCN)-based embeddings for the likelihood of proximity of different semantically-labeled regions and the occurrence of different object classes in these regions. To locate a target object instance during evaluation, the robot uses Bayesian inference and the SRG to estimate the visible regions, and uses the learned GCN embeddings to rank visible regions and select the region to explore next. This approach is tested using the Matterport3D (MP3D) benchmark dataset of indoor scenes in AI Habitat, a visually realistic simulation environment, to report substantial performance improvement in comparison with state of the art baselines.

Abstract

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal (’where to go’) based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.

Abstract

In the present study, a refined procedure for the seismic evaluation and retrofitting of reinforced concrete (RC) buildings based on the ‘quadrants assessment method’ and ‘material strain limit approach’ is proposed and numerically analysed. The quadrants assessment method involves the performance point, design base shear and threshold damage limit state. Herein, four existing RC buildings (models 1–4) are considered from the Koyna– Warna region, Maharashtra (zone-IV, India). These four buildings were studied using nonlinear static adaptive pushover analysis employing the SeismoStruct software. Based on the quadrants assessment method, the three-storey RC building (model-1) was retrofitted with RC jacketing, while the other three RC buildings did not need to be retrofitted. Also, significant seismic design parameters like ductility, over strength factor, response reduction factor, etc. were evaluated before and after retrofitting. The results depict that the combination of the ‘quadrants assessment method’ and ‘material strain limit approach’ is a rapid, reliable and refined procedure for seismic evaluation and retrofitting of RC buildings.

Abstract

How do language learners avoid the production of verb argument structure overgeneralization errors (*The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to pred

Abstract

Unlike traditional optoelectronic satellite imaging, Synthetic Aperture Radar (SAR) allows remote sensing applications to operate under all weather conditions. This makes it uniquely valuable for detecting ships/vessels involved in illegal, unreported, and unregulated (IUU) fishing. While recent work has shown significant improvement in this domain, detecting small objects using noisy point annotations remains an unexplored area. In order to meet the unique challenges of this problem, we propose a progressive training methodology that utilizes two different spatial sampling strategies. Firstly, we use stochastic sampling of background points to reduce the impact of class imbalance and missing labels, and secondly, during the refinement stage, we use hard negative sampling to improve the model. Experimental results on the challenging xView3 dataset show that our method outperforms conventional small object localization methods in a large, noisy dataset of SAR images.Source code for our method can be found at: https://github.com/manupillai308/DeepSAR

Abstract

Recently, diamond color defect based quantum sensing applications such as nitrogen-vacancy (NV) center magnetometry have emerged in CMOS technology, which use optically detected magnetic resonance (ODMR) for sensing magnetic field strengths (|B~|) from different environmental physical quantities. For ODMR based sensing, CMOS quantum sensors seek an onchip 2.87 GHz microwave (MW) signal generator. Moreover, in order to sense smaller |B~|, these CMOS quantum sensors also require that MW signal should be swept with sufficiently small step-size near 2.87 GHz. In this work, we present a fractional-N synthesizer based 2.87 GHz MW-generator (MWG) with an extremely small programmable sweep step-size for improved sensitivity of |B~| measurements in CMOS NV magnetometry. The proposed MWG is implemented in 180 nm CMOS technology and simulations were done to validate the proposed design. Post-layout simulation results show that the proposed MWG achieves a minimum sweep-step size of 50 kHz, which can be used to sense |B~|<0.9μT and exhibits a phase noise of −114.5 dBc/Hz at an offset of 1 MHz near 2.87 GHz center frequency.

Abstract

In vision-and-language navigation (VLN), an embodied agent is required to navigate in realistic 3D environments following natural language instructions. One major bottleneck for existing VLN approaches is the lack of sufficient training data, resulting in unsatisfactory generalization to unseen environments. While VLN data is typically collected manually, such an approach is expensive and prevents scalability. In this work, we address the data scarcity issue by proposing to automatically create a large-scale VLN dataset from 900 unlabeled 3D buildings from HM3D. We generate a navigation graph for each building and transfer object predictions from 2D to generate pseudo 3D object labels by cross-view consistency. We then fine-tune a pretrained language model using pseudo object labels as prompts to alleviate the cross-modal gap in instruction generation. Our resulting HM3D-AutoVLN dataset is an order of magnitude larger than existing VLN datasets in terms of navigation environments and instructions. We experimentally demonstrate that HM3D-AutoVLN significantly increases the generalization ability of resulting VLN models. On the SPL metric, our approach improves over state of the art by 7.1% and 8.1% on the unseen validation splits of REVERIE and SOON datasets respectively.

Abstract

In this work we outline an approach to mine insights from calls between delivery partners (DP) and customers involved in hyperlocal food delivery in India. Incorrect addresses/ locations or other impediments prompt the DPs to call customers leading to suboptimal experiences like breaches in the promised arrival-time, cancellation, fraud, etc. We demonstrate an end-to-end system that utilizes a multi-modal approach where we combine data across speech, text and geospatial domains to extract the intent behind these calls. To transcribe calls to text, we develop an Automatic Speech Recognition (ASR) engine that works in the Indian context where the calls are typically highly code-mixed (in our case Hindi and English) along with variations in dialects and pronunciations. Additionally in the hyperlocal delivery space, the calls are also corrupted by high levels of background noise due to the nature of the business. Starting with Wav2Vec2.0 as the base we carried out a series of data and model based experiments to progressively reduce the WER from 85.30% to 31.17%. The transcripts from the ASR engine are encoded into embeddings by adapting an IndicBERT based model. Features extracted from the geospatial markers of calls are concatenated with the embeddings and passed through an XGBoost classification head to classify calls into one of three intents. Through ablation studies we show incremental improvements attributable to signals from different modalities. The winning multi-modal model has a macro average precision of 68.33% which is a 29.3pp lift over the baseline not utilizing all the modalities

Abstract

Epilepsy is a common neural abnormality of the nervous system, which is constituted by recurring abnormalities, and humans with their class often suffered from disgrace and inequity. Therefore seizure detection has much importance in the medical domain for the treatment of epilepsy-affected persons. Electroencephalography signals are the most commonly used data modality for epilepsy identification. Yet, it can be challenging to proclaim the understated but severe modulation present in electroencephalogram signals. Here discrete wavelet transform is used for feature extraction, and the extracted features are classified by using an artificial neural network machine learning classifier. The 12 features are classified, and the accuracy among them is compared. To estimate the efficiency of the proposed method, different classifiers and feature extracting techniques are considered in this work. The obtained outcomes have proved that the new system obtained a promising identification accuracy of 97.24%, that is, capable of being an important approach for realistic applications curing epileptics.