An Evaluation of Social Responsibility Practices in selected Corporate Units

Corporate social responsibility has become a favourite topic of discussion for businessmen, academicians, political leaders, social reformers and management students in India. Opinion about the primary responsibility of business is sharply divided between those who believe that ‘business of business is business’ and those who believe that the business community as an affluent and capable group within an Unusually poor society has a moral obligation to consciously help the needy and under-privileged even while going about its business. From the social advertisements, and speeches of top managers of some large private industrial enterprises in India, it appears that increasingly private sector is lending support to the view that as integral part of the larger social system, business has an obligation to serve the society as a whole. Some interesting questions that the prevailing context gives rise to are: Are managers in Indian industry favourable to corporate social responsibility, irrespective of their association with a particular type of corporate ownership and a level in management hierarchy? What areas of social effort do they prefer? What problems do they find in implementation? How can their social performance be improved? Most of the views expressed by different Indian writers on these aspects are subjective in character, as they are not based on empirical studies. The present study is designed to bridge the gap by providing an objective assessment of the attitudes of managers towards these aspects

Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics

Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype

We present a new publicly available dataset that contains simulated data of a novel calorimeter to be installed at the CERN Large Hadron Collider. This detector will have more than six-million channels with each channel capable of position, ionisation and precision time measurement. Reconstructing these events in an efficient way poses an immense challenge which is being addressed with the latest machine learning techniques. As part of this development a large prototype with 12,000 channels was built and a beam of high-energy electrons incident on it. Using machine learning methods we have reconstructed the energy of incident electrons from the energies of three-dimensional hits, which is known to some precision. By releasing this data publicly we hope to encourage experts in the application of machine learning to develop efficient and accurate image reconstruction of these electrons.Comment: 7 pages, 6 figure

Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisions. The optical transparency of the crystals degrades slowly with radiation dose due to the beam-beam collisions. The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products. Predicting the optical transparency of the crystals, both in the short-term and in the long-term, is a critical task for the CMS experiment. We describe here the public data release, following FAIR principles, of the crystal monitoring data collected by the CMS Collaboration between 2016 and 2018. Besides describing the dataset and its access, the problems that can be addressed with it are described, as well as an example solution based on a Long Short-Term Memory neural network developed to predict future behavior of the crystals

Hepatic adenoma-an unusual case report

A 70-year-old female visited to tertiary care hospital with complains of abdominal pain on and off for 2 years. Pain gradually increased and was associated with vomiting. Patient is a known case of hypertension and diabetes mellitus. The patient`s complete blood count was normal with increased coagulation profile. Provisional clinical diagnosis was fibronodular variant of hepatocellular carcinoma. Computed tomography scan suggestive of fibronodular hyperplasia. Specimen received in pathology department, which on gross examination showed well circumscribed, well encapsulated tumour with variegated appearance. Histopathological diagnosis of Hepatic adenoma was made

Explainable Misinformation Detection Across Multiple Social Media Platforms

In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN mode. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets, namely CoAID and MiSoVac, and compared results with and without DANN implementation. DANN significantly improves the accuracy measure F1 classification score and increases the accuracy and AUC performance. The results obtained show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation enabling trustworthy information processing and extraction to combat misinformation effectively.Comment: 28 pages,4 figure

Development of a transdiagnostic, low-intensity, psychological intervention for common adolescent mental health problems in Indian secondary schools

Background: The PRIDE programme aims to establish a suite of transdiagnostic psychological interventions organised around a stepped care system in Indian secondary schools. This paper describes the development of a low-intensity, first-line component of the PRIDE model. Method: Contextual and global evidence informed an intervention ‘blueprint’ with problem solving as the primary practice element. Successive iterations were tested and modified across two pilot cohort studies (N=45; N=39). Participants were aged 13–20 years and presenting with elevated mental health symptoms in New Delhi schools. Results: The first iteration of the intervention, based on a guided self-help modality, showed promising outcomes and user satisfaction when delivered by psychologists. However, delivery was not feasible within the intended 6-week schedule, and participants struggled to use materials outside ‘guidance’ sessions. In Pilot 2, a modified counsellor-led problem-solving intervention was implemented by less experienced counsellors over a 3–4 week schedule. Outcomes were maintained, with indications of enhanced feasibility and acceptability. High demand was observed across both pilots, leading to more stringent eligibility criteria and a modified sensitisation plan. Discussion: Findings have shaped a first-line intervention for common adolescent mental health problems in low-resource settings. A forthcoming randomised controlled trial will test its effectiveness

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe

An embedding technique to determine ττ backgrounds in proton-proton collision data

An embedding technique is presented to estimate standard model tau tau backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed mu mu events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique described in this paper was developed for CMS. Its validation and the inherent uncertainties are also discussed. The demonstration of the performance of the technique is based on a sample of proton-proton collisions collected by CMS in 2017 at root s = 13 TeV corresponding to an integrated luminosity of 41.5 fb(-1).Peer reviewe