Construction of environmental knowledge: experiences from India

This paper explored key issues in how knowledge of the environment is constructed in the Third World. Drawing on which, it showed that there are both explicit and implicit ways in which this knowledge is contested. Particularly, it discussed how implicit forms of contestation are problematic in Third World economies because they are exclusionary and also where such issues become ‘headlines’ only after environmental damage and accompanying social injustices have resulted. It concludes by raising crucial questions for environmental research in the Third World where there is limited role of governments and communities in protecting their environment

Publishing in town and country planning for the upcoming 'Research Excellence Framework' (REF): Lessons from the RAE 2008 for early career academics

Working Paper 2011-0

Poverty alleviation in India and Kerala: An overview

Working paper prepared for the dissemination event at the University of Dundee (07 Sep 2011) as part of British Academy Small Research Grant project, Ref. SG1015

Enhancing stroke generation and expressivity in robotic drummers - A generative physics model approach

The goal of this master's thesis research is to enhance the stroke generation capabilities and musical expressivity in robotic drummers. The approach adopted is to understand the physics of human fingers-drumstick-drumhead interaction and try to replicate the same behavior in a robotic drumming system with the minimum number of degrees of freedom. The model that is developed is agnostic to the exact specifications of the robotic drummer that will attempt to emulate human like drum strokes, and therefore can be used in any robotic drummer that uses actuators with complete control over the motor position angle. Initial approaches based on exploiting the instability of a PID control system to generate multiple bounces and the limitations of this approach are also discussed in depth. In order to assess the success of the model and the implementation in the robotic platform a subjective evaluation was conducted. The evaluation results showed that, the observed data was statistically equivalent to the subjects resorting to a blind guess in order to distinguish between a human playing a multiple bounce stroke and a robot playing a similar kind of stroke.M.S

Leveraging Demonstrations with Latent Space Priors

Demonstrations provide insight into relevant state or action space regions, bearing great potential to boost the efficiency and practicality of reinforcement learning agents. In this work, we propose to leverage demonstration datasets by combining skill learning and sequence modeling. Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy. The sequence model forms a latent space prior over plausible demonstration behaviors to accelerate learning of high-level policies. We show how to acquire such priors from state-only motion capture demonstrations and explore several methods for integrating them into policy learning on transfer tasks. Our experimental results confirm that latent space priors provide significant gains in learning speed and final performance. We benchmark our approach on a set of challenging sparse-reward environments with a complex, simulated humanoid, and on offline RL benchmarks for navigation and object manipulation. Videos, source code and pre-trained models are available at the corresponding project website at https://facebookresearch.github.io/latent-space-priors .Comment: Published in Transactions on Machine Learning Research (03/2023

Bacterial infections in Indian cirrhotic patients: a prospective study

Background: Bacterial infections (BI) are more prevalent in liver cirrhosis (LC), high among hospitalized patients. The aim of this study was to explore the epidemiological pattern of BI in hospitalized patients with LC, and identification the causative agents. Objective of the study was evaluation of therapeutic/empirical approaches for these infections.Methods: Inputs from the body fluid analysis and culture reports were recorded. The Child Pugh score (CPS) was used to assess the severity of liver disease. Antibiotic treatment strategy was analysed, prescribed antibiotics were checked for contraindications using Lexicomp software.Results: Of 60 enrolled patients, four had mixed infection and 55% were culture positive. There was a male preponderance (83.3%). BI was more frequent in those aged 51-60 years (38.3%) and >60 years (35%). Higher proportion of patients (60%) belonged to class C of CPS followed by class B (31.7%). The most common causative organisms identified were E. coli (28.5%), K. pneumonia (14.2%), Enterococcus spp (11.4 %) and less common were K. oxytoca, Coagulase-negative staphylococci, Staphylococcus aureus, gram-positive cocci, gram-negative cocci, P. aeruginosa, S. hemolyticus, ß-hemolytic streptococcus spp. Majority of the subjects had spontaneous bacterial peritonitis (36.7%) followed by urinary tract infection (21%), lower respiratory tract infection (18.3%), sepsis (13.3%), cellulitis (3.3%) and acute gastroenteritis (1.7%). Cephalosporin (61.7%), rifaximin (51.7%), penicillin and β lactamase inhibitors (36.7%) were the common prescribed antimicrobials.Conclusions: There is a positive association between the risk of BI and severity of liver damage

CIRCLE: Capture In Rich Contextual Environments

Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics-based motion capture systems are not suited for simultaneously capturing human movements and complex scenes. The lack of rich contextual 3D human motion datasets presents a roadblock to creating high-quality generative human motion models. We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world while being motion captured in the real world. Our system enables rapid collection of high-quality human motion in highly diverse scenes, without the concern of occlusion or the need for physical scene construction in the real world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information. Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes. To download the data please visit https://stanford-tml.github.io/circle_dataset/