Domestic Violence against Women during the Covid-19: A Case Study of Bihar (India)

The Covid-19 pandemic revealed that the socioeconomic challenges in developing countries intersect within and beyond the dynamics of caste, class, space, and most importantly, gender. The recent outbreak of the Covid-19 pandemic compelled the entire human population to survive on the brink of uncertainty. The subsequent lockdown witnessed an upsurge in domestic abuse cases across the globe, making us realize how the four walls of the familial space turned into a hotbed of the \u27shadow pandemic\u27 resulting from the socioeconomic disparities and individual frustration during difficult times. India also witnessed a sudden surge in domestic violence cases, often called a shadow pandemic. While some got reported, many went without being documented in any forum. In this regard, this research is a case study of Bihar (India), which encountered a higher rate of domestic violence during the pandemic than other states such as Delhi, Haryana, Uttar Pradesh, and Himanchal Pradesh. This empirical study examines the economic, psychological, and social factors responsible for the surge in domestic abuse in Bihar during the Covid-19 pandemic

Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201

Operationalizing Machine Learning: An Interview Study

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees' pain points and anti-patterns, with implications for tool design.Comment: 20 pages, 4 figure

Revisiting Prompt Engineering via Declarative Crowdsourcing

Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called prompt engineering-the process of asking an LLM to do something via a series of prompts. However, for LLM-powered data processing workflows, in particular, optimizing for quality, while keeping cost bounded, is a tedious, manual process. We put forth a vision for declarative prompt engineering. We view LLMs like crowd workers and leverage ideas from the declarative crowdsourcing literature-including leveraging multiple prompting strategies, ensuring internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make prompt engineering a more principled process. Preliminary case studies on sorting, entity resolution, and imputation demonstrate the promise of our approac

DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing

Existing deep learning based visual servoing approaches regress the relative camera pose between a pair of images. Therefore, they require a huge amount of training data and sometimes fine-tuning for adaptation to a novel scene. Furthermore, current approaches do not consider underlying geometry of the scene and rely on direct estimation of camera pose. Thus, inaccuracies in prediction of the camera pose, especially for distant goals, lead to a degradation in the servoing performance. In this paper, we propose a two-fold solution: (i) We consider optical flow as our visual features, which are predicted using a deep neural network. (ii) These flow features are then systematically integrated with depth estimates provided by another neural network using interaction matrix. We further present an extensive benchmark in a photo-realistic 3D simulation across diverse scenes to study the convergence and generalisation of visual servoing approaches. We show convergence for over 3m and 40 degrees while maintaining precise positioning of under 2cm and 1 degree on our challenging benchmark where the existing approaches that are unable to converge for majority of scenarios for over 1.5m and 20 degrees. Furthermore, we also evaluate our approach for a real scenario on an aerial robot. Our approach generalizes to novel scenarios producing precise and robust servoing performance for 6 degrees of freedom positioning tasks with even large camera transformations without any retraining or fine-tuning.Comment: Accepted in International Conference on Robotics and Automation (ICRA) 2020, IEE

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding

Knowledge, attitude and practices of antibiotic usage among students from Mumbai University

Background: Discovery of antibiotics have helped to manage the devastating diseases. Presently, the antibiotic era is threatened by the emergence of high level of antibiotic resistance of important pathogens. Misuse of antibiotics poses a serious risk to infectious disease control. It is necessary to improve public awareness to bring a change in the behavior of consumers. Therefore, present study was undertaken to assess the existing knowledge, attitude and practices related to antibiotic usage among university students.Methods: A cross-sectional study was carried out among students from Mumbai University, India during May-June 2017. 300 students were approached to participate in the study of which 250 agreed to participate (males: 117; females: 133). Pretested questionnaire was distributed and collected data was analyzed using IBM SPSS version 23.Results: Substantial number (33% and 40%) participants were unaware about the differences in antibiotic-anti-inflammatory drugs and antibiotic-antipyretics respectively. 28% of the participants thought it is right to stop antibiotics only based on symptoms improvement. Sixty eight percent and seventy nine percent participants believed that antibiotics should always be prescribed to treat flu like symptoms and pneumonia respectively.Conclusions: Participants demonstrated poor knowledge about antibiotics. Similarly, their attitude and practice toward antibiotic use was associated with misconceptions. An educational intervention can be introduced to make them aware about rational antibiotic practices