Badnam Science? The Spectre of the ‘Bad’ Name and the Politics of Stem Cell Science in India

The range of the implicit meanings of badnam (bad name) stop short of unpacking the complexity underscoring the implied soiling and spoiling of ‘name’: the crucible of reputation, honour, and dignity. What happens when diverse stakeholders working in the burgeoning and high-stakes field of stem cell science in India fear badnami, ignominy (to invoke one possible meaning), in the context of a regulatory flux and fears of rapidly deepening reputation of the field as a maverick site for stem cell research and clinical application? Drawing on longitudinal research mapping the stem cell technology terrain in India and the changing fortunes of a small clinical facility, this article shows how the spectre of ‘spoilt name’ (or badnami) haunts professional narratives and how scientific validation, national honour, economic viability, therapeutic efficacy, and safety come to reside in the ‘name.’ The article conceptualizes ‘name’ as inherently vulnerable and examines its threatened status to highlight the unnameable, unspecified aspect that survives demanding a new name despite the ethics and politics implicit in naming and ‘name-calling.

Cultivated cure, regenerated affliction

In this think piece, I interrogate the notion of cure in order to address the idea of disease. My intention is to show how emerging biotechnological modalities that cultivate an idea of ‘cure as regeneration’ dislocate expert knowledge, descriptions of disease, and its representation into contested new terrains. In approaching disease from the vantage point of the ‘cultivated cure’ I seek to trouble our commonsense view of afflictions. Drawing on ethnographic data from a longitudinal project engaged in mapping stem cell technologies in India, I conceptualize how ‘cure as regeneration’ reanimates the figures of disease and medical knowledge. I take up Veena Das’s challenging query: is it necessary to define terms – illness, disease, diagnosis, health – that defy neat characterization

Flavour Enhanced Food Recommendation

We propose a mechanism to use the features of flavour to enhance the quality of food recommendations. An empirical method to determine the flavour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page

Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle: Evaluating Street Imagery from Moving Vehicles to Identify Visual Pollution

Visual pollution is a growing problem in urban areas. It is important for environmental management to identify, formalize, measure and evaluate visual pollution. This paper presents a study on the development of an automated system for visual pollution classification using street images taken from a moving vehicle. The proposed system uses convolutional neural networks to classify different types of visual pollutants such as graffiti, faded signage, potholes, litter, construction zones, broken signage, poor street lighting, poor billboards, road sand, sidewalk clutter, and unmaintained facades.In this study, we utilized a large dataset of raw sensor camera inputs gathered from a fleet of multiple vehicles in a specific geographical area. Our aim was to develop convolutional neural networks that simulate human learning to classify visual pollutants from these images. The successful implementation of this system would be a significant contribution to the development of urban planning and the strengthening of communities worldwide. Additionally, it could lead to the creation of a "visual pollution score/index" for urban areas, which could serve as a new metric for urban environmental management. Our findings, which we present in this paper, will be a valuable addition to the academic community and the field of computer vision for environmental management applications

Heavy neutrino signatures from leptophilic Higgs portal in the linear seesaw

Lepton collider setups can probe the neutrino sector in the linear seesaw mechanism. Small neutrino masses are sourced by a tiny vacuum expectation value of a leptophilic scalar Higgs doublet and are mediated by Quasi-Dirac heavy neutrinos. These new particles can all be accessible to colliders. We describe novel charged Higgs and heavy neutrino production mechanisms that can be sizeable at $e^{+} e^{-}$ or $e^{-} {\gamma}$ colliders and discuss some of the associated signatures. These may shed light on the Majorana nature of neutrinos and the role of lepton number and lepton flavour symmetries.Comment: 10 pages, 4 figure

Phenomenology of the simplest linear seesaw mechanism

The linear seesaw mechanism provides a simple way to generate neutrino masses. In addition to Standard Model particles, it includes quasi-Dirac leptons as neutrino mass mediators, and a leptophilic scalar doublet seeding small neutrino masses. Here we review its associated physics, including restrictions from theory and phenomenology. The model yields potentially detectable $\mu\to e\gamma$ rates as well as distinctive signatures in the production and decay of heavy neutrinos ($N_i$) and the charged Higgs boson ($H^\pm$) arising from the second scalar doublet. We have found that production processes such as $e^+e^-\to NN$, $e^-\gamma\to NH^-$ and $e^+ e^-\to H^+ H^-$ followed by the decay chain $H^\pm\to\ell_i^\pm N$, $N\to \ell_j^{\pm}W^\mp$ leads to striking lepton number violation signatures at high energies which may probe the Majorana nature of neutrinos.Comment: 52 pages, 33 figures, 2 table

Learning to Answer Semantic Queries over Code

During software development, developers need answers to queries about semantic aspects of code. Even though extractive question-answering using neural approaches has been studied widely in natural languages, the problem of answering semantic queries over code using neural networks has not yet been explored. This is mainly because there is no existing dataset with extractive question and answer pairs over code involving complex concepts and long chains of reasoning. We bridge this gap by building a new, curated dataset called CodeQueries, and proposing a neural question-answering methodology over code. We build upon state-of-the-art pre-trained models of code to predict answer and supporting-fact spans. Given a query and code, only some of the code may be relevant to answer the query. We first experiment under an ideal setting where only the relevant code is given to the model and show that our models do well. We then experiment under three pragmatic considerations: (1) scaling to large-size code, (2) learning from a limited number of examples and (3) robustness to minor syntax errors in code. Our results show that while a neural model can be resilient to minor syntax errors in code, increasing size of code, presence of code that is not relevant to the query, and reduced number of training examples limit the model performance. We are releasing our data and models to facilitate future work on the proposed problem of answering semantic queries over code