86 research outputs found
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/
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