Three Flavoured neutrino oscillations and the Leggett Garg Inequality

Three flavoured neutrino oscillations are investigated in the light of the Leggett-Garg inequality. The outline of an experimental proposal is suggested whereby the findings of this investigation may be verified. The results obtained are: (a) The maximum violation of the Leggett Garg Inequality (LGI) is $2.17036$ for neutrino path length $L_{1}=140.15$ Km and $\Delta L=1255.7$ Km.(b) Presence of the mixing angle $\theta_{13}$ enhances the maximum violation of LGI by $4.6\%$.(c) The currently known mass hierarchy parameter $\alpha = 0.0305$ increases the the maximum violation of LGI by $3.7\%$. (d)Presence of CP violating phase parameter enhances the maximum violation of LGI by $0.24\%$, thus providing an \textit{alternative indicator of CP violation} in 3-flavoured neutrino oscillations.Comment: 8 pages, 5 figures, late

Automatic Pill Reminder for Easy Supervision

In this paper we present a working model of an automatic pill reminder and dispenser setup that can alleviate irregularities in taking prescribed dosage of medicines at the right time dictated by the medical practitioner and switch from approaches predominantly dependent on human memory to automation with negligible supervision, thus relieving persons from error-prone tasks of giving wrong medicine at the wrong time in the wrong amount.Comment: 5 pages, 7 figures, ICISS- 2017 (IEEE Conference

Probing LLMs for hate speech detection: strengths and vulnerabilities

Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models. However, none of these works aim to use explanation, additional context and victim community information in the detection process. We utilise different prompt variation, input information and evaluate large language models in zero shot setting (without adding any in-context examples). We select three large language models (GPT-3.5, text-davinci and Flan-T5) and three datasets - HateXplain, implicit hate and ToxicSpans. We find that on average including the target information in the pipeline improves the model performance substantially (~20-30%) over the baseline across the datasets. There is also a considerable effect of adding the rationales/explanations into the pipeline (~10-20%) over the baseline across the datasets. In addition, we further provide a typology of the error cases where these large language models fail to (i) classify and (ii) explain the reason for the decisions they take. Such vulnerable points automatically constitute 'jailbreak' prompts for these models and industry scale safeguard techniques need to be developed to make the models robust against such prompts.Comment: 13 pages, 9 figures, 7 tables, accepted to findings of EMNLP 202

PUO with multiple abscesses due to Burkholderia pseudomallei: a case report

Melioidosis is an infectious disease of humans and animals caused by Burkholderia pseudomallei, previously called as Pseudomonas pseudomallei. We reported a case of a 14 year old female patient presented with fever for last 3 months and multiple swellings of joints, misdiagnosed as M.D.R. T.B. But after proper evaluation diagnosed as multiple joint abscess due to Pseudomonas pseudomallei mimiking tuberculosis. Sporadic cases of melioidosis from various parts of South Indiaand Western costal India have been reported, but remained underdiagnosed due to lack of awareness