Abstract

The nationwide lockdown was imposed over In- dia from 25 March to 31 May 2020 with varied relax- ations from phase I to phase IV to contain the spread of COVID-19. Thus, emissions from industrial and transport sectors were halted during lockdown (LD), which has re-sulted in a significant reduction of anthropogenic pollutants. The first two lockdown phases were strictly implemented (phase I and phase II) and hence were considered to be total lockdown (TLD) in this study. Satellite-based tropospheric columnar nitrogen dioxide (TCN) from the years 2015 to 2020, tropospheric columnar carbon monoxide (TCC) dur-ing 2019/20, and aerosol optical depth (AOD550) from the years 2014 to 2020 during phase I and phase II LD and pre- LD periods were investigated with observations from Aura OMI, Sentinel-5P TROPOMI, and Aqua and Terra MODIS. To quantify lockdown-induced changes in TCN, TCC, and AOD550, detailed statistical analysis was performed on de-trended data using the Student paired statistical t test. Re-sults indicate that mean TCN levels over India showed a dipof 18 % compared to the previous year and also against the 5-year mean TCN levels during the phase I lockdown, which was found to be statistically significant (p value < 0.05) against the respective period. Furthermore, drastic changes in TCN levels were observed over hotspots, namely eastern region and urban cities. For example, there was a sharp de-crease of 62 % and 54 % in TCN levels compared to 2019 and against 5-year mean TCN levels over New Delhi with a p value of 0.0002 (which is statistically significant) dur-ing total LD. The TCC levels were high in the northeast (NE) region during the phase I LD period, which is mainly attributed to the active fire counts in this region. However, lower TCC levels are observed in the same region due to the diminished fire counts during phase II. Further, AOD550 is reduced over the country by ∼ 16 % (Aqua and Terra) from the 6-year (2014–2019) mean AOD550 levels, with a signifi-cant reduction (Aqua MODIS 28 %) observed over the Indo- Gangetic Plain (IGP) region with a p value of  0.05. How-ever, an increase in AOD550 levels (25 % for Terra MODIS, 15 % for Aqua MODIS) was also observed over central India during LD compared to the preceding year and found signif-icant with a p value of 0.03. This study also reports the rate of change of TCN levels and AOD550 along with statistical metrics during the LD period.

Abstract

How bad is my wound? How fast will the wound heal? Do I need to get hospitalized? Questions like these are critical for wound assessment, but challenging to answer. Given a wound image and patient attributes, our goal is to build models for two wound assessment tasks: (1) predicting if the patient needs hospitalization for the wound to heal, and (2) estimating wound progression, i.e., weeks to heal. The problem is challenging because wound progression and hospitalization risk depend on multiple factors that need to be inferred automatically from the given wound image. There exists no work which performs a rigorous study of wound assessment tasks considering multiple wound attributes inferred using a large dataset of wound images. We present HealTech, a two-stage wound assessment solution. The first stage predicts various wound attributes (like ulcer type, location, stage, etc.) from wound images, using deep neural networks. The second stage predicts (1) whether the wound would heal (using conventional in-house treatment) or not (needs hospitalization), and (2) the number of weeks to heal, using an evolutionary algorithm based stacked Light Gradient Boosted Machines (LGBM) model. On a large dataset of 125711 wound images, HealTech achieves a recall of 83 and a precision of 92 for wounds with the risk of hospitalization. For wounds that can be healed without hospitalization, precision and recall are as high as 99. Our wound progression model provides a mean absolute error of 3.3 weeks.

Abstract

Performing sound source separation and visual object seg- mentation jointly in naturally occurring videos is a notori- ously difficult task, especially in the absence of annotated data. In this study, we leverage the concurrency between audio and visual modalities in an attempt to solve the joint audio-visual segmentation problem in a self-supervised man- ner. Human beings interact with the physical world through a few sensory systems such as vision, auditory, movement, etc. The usefulness of the interplay of such systems lies in the con- cept of degeneracy [1]. It tells us that the cross-modal signals can educate each other without the presence of an external supervisor. In this work, we efficiently exploit this fact that learning from one modality inherently helps to find patterns in others by introducing a novel audio-visual fusion technique. Also, to the best of our knowledge, we are the first to ad- dress the partially occluded sound source segmentation task. Our study shows that the proposed model significantly out- performs existing state-of-the-art methods in both visual and audio source separation tasks

Abstract

Estimation of time delay from the received broadband signals like speech, collected at two or more spatially distributed microphones, has many applications. Methods like the cross-correlation of the signals directly and generalized crosscorrelation based methods (GCC and GCC-PHAT) have been used for several years to estimate the time delay. Performance of these methods degrades due to noise, multi-path reflections, and reverberation in a practical environment, like a live room. The estimated time delay is usually robust due to the averaging effect of the delay obtained over several frames in an utterance of a few seconds. The robustness is affected if the varying time delay of a moving speaker is desired. A smaller duration for averaging results in errors in the estimation of the time delay, and a longer duration for averaging results in loss of accuracy. Since the single frequency filtering (SFF) based analysis provides an estimation of the instantaneous time delay, it is possible to study the trade off between accuracy and robustness. This paper examines this trade-off in determining the number of stationary speakers from mixed signals and in tracking a speaker moving along a straight line path and along a circular path. The results are illustrated for actual data collected in a live room.

Abstract

Humans overcome the degradation effect of background noise on the speech by changing their vocal characteristics dynamically, which text-to-speech systems trained on clean speech cannot, re- sulting in degraded intelligibility. To improve the intelligibility of such system requires a large amount of speech samples, which is difficult to collect for various conditions like noise backgrounds and signal-to-noise ratios. This paper presents a noise dependent enhancement to text-to-speech, based on generative adversarial network training framework to generate intelligible speech in noise. The learning mechanism for the synthesizer network is inspired from the acoustic feedback humans use to nullify the effect of vari- ous background noises. The system thus trained is evaluated under cafeteria noise condition with two objective measures, which indi- cated improvement in intelligibility compared to models trained on clean speech across 3 SNRs. The proposed modification does not require any additional training data and can be applied to a variety of Deep Neura

Abstract

We propose an all-CMOS, supply independent, second-order temperature compensated, process invariant, 10.09 μA current reference circuit working on a 1.8 V power supply. The current reference is built using an adaptive regulated cascode configuration integrated with a Widlar current source. It is implemented in 180 nm UMC MPW RF process and consumes an area of 0.014 mm 2 . It has a very low line sensitivity of 0.52 %/V and a temperature coefficient of 9.94 ppm/°C over the automotive temperature range of −40°C to 125°C. The proposed current reference is used to build a high quality, power efficient stable ring oscillator circuit working at 377.9 MHz frequency to be used in IoT SoCs. The ring oscillator shows a variation of less than 1.1 % in frequency, with supply voltage over the range 1.5 V to 2 V. The invariance of frequency with supply voltage can be exploited to reduce the power consumption up to 36.93 % in the oscillator circuit.

Abstract

The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have be- come one of the major concerns because of its socio-political con- sequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal de- tection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contex- tual references, and handling multiple aspects of antisemitism. Un- fortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab re- spectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific anti- semitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the ef- ficacy of the proposed system. Finally, we also present a qualitative analysis of our study.

Abstract

Social media has grown exponentially in a short period, coming to the forefront of communications and online interactions. Despite their rapid growth, social media platforms have been unable to scale to different languages globally and remain inaccessible to many. In this paper, we characterize Koo, a multilingual micro-blogging site that rose in popularity in 2021, as an Indian alternative to Twitter. We collected a dataset of 4.07 million users, 163.12 million follower-following relationships, and their content and activity across 12 languages. We study the user demographic along the lines of language, location, gender, and profession. The prominent presence of Indian languages in the discourse on Koo indicates the platform's success in promoting regional languages. We observe Koo's follower-following network to be much denser than Twitter's, comprising of closely-knit linguistic communities. An N-gram analysis of posts on Koo shows a #KooVsTwitter rhetoric, revealing the debate comparing the two platforms. Our characterization highlights the dynamics of the multilingual social network and its diverse Indian user base.

Abstract

Exploring shared autonomy in assistive and service robots makes human-aware navigation of a person following robot (PFR) a required behaviour. In this paper, we propose a control framework for a non-holonomic wheeled robot that not only tracks a target person in sight but also anticipates the human movement patterns to predict the future sequence of its path when the person is out of sight. Human beings form a crowd and can exhibit complex random movement compared to vehicles while indoor environments with intersections can pose serious challenges for long-distance path-following without the breakdown of tracking. Thus, a nonlinear model predictive controller is designed with long-term prediction and socially compliant rules for natural person-following behaviour. It can generate the collision-free path and optimized control inputs using a single optimization framework Finally, the integrated navigation-control stack is evaluated using simulations for real-time operation. We also present a hardware configuration for its real-world implementation.

Abstract

Recent data-driven approaches to visual servoing have shown improved performances over classical methods due to precise feature matching and depth estimation. Some recent servoing approaches use a model predictive control (MPC) framework which generalise well to novel environments and are capable of incorporating dynamic constraints, but are computationally intractable in real-time, making it difficult to deploy in real-world scenarios. On the contrary, single-step methods optimise greedily and achieve high servoing rates, but lack the benefits of the MPC multi-step ahead formulation. In this paper, we make the best of both worlds and propose a lightweight visual servoing MPC framework which generates optimal control near real-time at a frequency of 10.52 Hz. This work utilises the differential cross-entropy sampling method for quick and effective control generation along with a lightweight neural network, significantly improving the servoing frequency. We also propose a flow depth normalisation layer which amelio- rates the issue of inferior predictions of two view depth from the flow network. We conduct extensive experimentation on the Habitat simulator and show a notable decrease in servoing time in comparison with other approaches that optimise over a time horizon. We achieve the right balance between time and performance for visual servoing in six degrees of freedom (6DoF), while retaining the advantageous MPC formulation. Our code and dataset are publicly available