AN INTRODUCTION OF PROBLEM BASED LEARNING IN IMS, BHU

Aim: To find out the interest generated among medical students for problem based learning (PBL). Methods: Questionnaires were distributed to a batch of medical students (final year). Problem based learning was explained to them by power point presentation by the first author before the questionnaires were distributed. Then the students were asked to fill the questionnaires which were collected within ten minutes. Results: Thirty two students answered that PBL has ‘generated interest' in them as student while one student answered ‘definitely yes'.Students were asked ‘do they think it is a better way of teaching/learning'? Twenty nine students answered yes, three answered definitely yes and one answered may be.Students gave variety of replies to the question ‘ why they thought it was better'? Majority of students wrote that active participation brings responsibility, enhances learning and retention. PBL will be a realistic way of teaching. Students also felt that PBL is active form of learning and it is deep learning, it will boost student confidence and strengthens students' teacher relationship. They also felt that it will be interesting and practical.Conclusions: The questionnaire survey among the final year MBBS professional students revealed their interest in PBL. The reason we would prefer PBL to be introduced in the IMS, BHU is because it is a self-learning method which is the deepest form of learning. It is well known that we will need more resources and also more staffing to continue doing PBL but it will be worth the effort for our students

Acceptance Dependence of Fluctuation in Particle Multiplicity

The effect of limiting the acceptance in rapidity on event-by-event multiplicity fluctuations in nucleus-nucleus collisions has been investigated. Our analysis shows that the multiplicity fluctuations decrease when the rapidity acceptance is decreased. We explain this trend by assuming that the probability distribution of the particles in the smaller acceptance window follows binomial distribution. Following a simple statistical analysis we conclude that the event-by-event multiplicity fluctuations for full acceptance are likely to be larger than those observed in the experiments, since the experiments usually have detectors with limited acceptance. We discuss the application of our model to simulated data generated using VENUS, a widely used event generator in heavy-ion collisions. We also discuss the results from our calculations in presence of dynamical fluctuations and possible observation of these in the actual data.Comment: To appear in Int. J. Mod. Phys.

Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods that have made a large impact on the vision field increasingly more difficult and less efficient. In this paper, we propose a new probabilistic latent variable model for disease classification in chest X-ray images. Specifically we consider chest X-ray datasets that contain global disease labels, and for a smaller subset contain object level expert annotations in the form of eye gaze patterns and disease bounding boxes. We propose a two-stage optimization algorithm which is able to handle these different label granularities through a single training pipeline in a two-stage manner. In our pipeline global dataset features are learned in the lower level layers of the model. The specific details and nuances in the fine-grained expert object-level annotations are learned in the final layers of the model using a knowledge distillation method inspired by conditional variational inference. Subsequently, model weights are frozen to guide this learning process and prevent overfitting on the smaller richly annotated data subsets. The proposed method yields consistent classification improvement across different back-bones on the common benchmark datasets Chest X-ray14 and MIMIC-CXR. This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.</p

Theoretical study of isolated dangling bonds, dangling bond wires and dangling bond clusters on H:Si(100)-(2×\times1) surface

We theoretically study the electronic band structure of isolated unpaired and paired dangling bonds (DB), DB wires and DB clusters on H:Si(100)-(2$\times$1) surface using Extended H\"uckel Theory (EHT) and report their effect on the Si band gap. An isolated unpaired DB introduces a near-midgap state, whereas a paired DB leads to $\pi$ and $\pi^*$ states, similar to those introduced by an unpassivated asymmetric dimer (AD) Si(100)-(2$\times$1) surface. Such induced states have very small dispersion due to their isolation from the other states, which reside in conduction and valence band. On the other hand, the surface state induced due to an unpaired DB wire in the direction along the dimer row (referred to as $[\bar{1}10]$), has large dispersion due to the strong coupling between the adjacent DBs, being 3.84$\AA$ apart. However, in the direction perpendicular to the dimer row (referred to as [110]), due to the reduced coupling between the DBs being 7.68$\AA$ apart, the dispersion in the surface state is similar to that of an isolated unpaired DB. Apart from this, a paired DB wire in $[\bar{1}10]$ direction introduces $\pi$ and $\pi^*$ states similar to those of an AD surface and a paired DB wire in [110] direction exhibits surface states similar to those of an isolated paired DB, as expected. Besides this, we report the electronic structure of different DB clusters, which exhibit states inside the band gap that can be interpreted as superpositions of states due to unpaired and paired DBs.Comment: 7 pages, 10 figure, 1 tabl

Oxidation mechanism in metal nanoclusters: Zn nanoclusters to ZnO hollow nanoclusters

Zn nanoclusters (NCs) are deposited by Low-energy cluster beam deposition technique. The mechanism of oxidation is studied by analysing their compositional and morphological evolution over a long span of time (three years) due to exposure to ambient atmosphere. It is concluded that the mechanism proceeds in two steps. In the first step, the shell of ZnO forms over Zn NCs rapidly up to certain limiting thickness: with in few days -- depending upon the size -- Zn NCs are converted to Zn-ZnO (core-shell), Zn-void-ZnO, or hollow ZnO type NCs. Bigger than ~15 nm become Zn-ZnO (core-shell) type: among them, NCs above ~25 nm could able to retain their initial geometrical shapes (namely triangular, hexagonal, rectangular and rhombohedral), but ~25 to 15 nm size NCs become irregular or distorted geometrical shapes. NCs between ~15 to 5 nm become Zn-void-ZnO type, and smaller than ~5 nm become ZnO hollow sphere type i.e. ZnO hollow NCs. In the second step, all Zn-void-ZnO and Zn-ZnO (core-shell) structures are converted to hollow ZnO NCs in a slow and gradual process, and the mechanism of conversion proceeds through expansion in size by incorporating ZnO monomers inside the shell. The observed oxidation behaviour of NCs is compared with theory of Cabrera - Mott on low-temperature oxidation of metal.Comment: 9 pages, 8 figure

Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

© 2020, Springer Nature Switzerland AG. Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance

Variations in visceral leishmaniasis burden, mortality and the pathway to care within Bihar, India

BACKGROUND: Visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem (< 1 case/10,000 people/year) in the Indian sub-continent (ISC) by 2020. Bihar State in India, which accounts for the majority of cases in the ISC, remains a major target for this elimination effort. However, there is considerable spatial, temporal and sub-population variation in occurrence of the disease and the pathway to care, which is largely unexplored and a threat to achieving the target. METHODS: Data from 6081 suspected VL patients who reported being clinically diagnosed during 2012-2013 across eight districts in Bihar were analysed. Graphical comparisons and Chi-square tests were used to determine differences in the burden of identified cases by season, district, age and sex. Log-linear regression models were fitted to onset (of symptoms)-to-diagnosis and onset-to-treatment waiting times to estimate their associations with age, sex, district and various socio-economic factors (SEFs). Logistic regression models were used to identify factors associated with mortality. RESULTS: Comparisons of VL caseloads suggested an annual cycle peaking in January-March. A 17-fold variation in the burden of identified cases across districts and under-representation of young children (0-5 years) relative to age-specific populations in Bihar were observed. Women accounted for a significantly lower proportion of the reported cases than men (41 vs 59%, P < 0.0001). Age, district of residence, house wall materials, caste, treatment cost, travelling for diagnosis and the number of treatments for symptoms before diagnosis were identified as correlates of waiting times. Mortality was associated with age, district of residence, onset-to-treatment waiting time, treatment duration, cattle ownership and cost of diagnosis. CONCLUSIONS: The distribution of VL in Bihar is highly heterogeneous, and reported caseloads and associated mortality vary significantly across different districts, posing different challenges to the elimination campaign. Socio-economic factors are important correlates of these differences, suggesting that elimination will require tailoring to population and sub-population circumstances

LifeLonger: A Benchmark for Continual Disease Classification

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark will be made publicly available

Snowmass CF1 Summary: WIMP Dark Matter Direct Detection

As part of the Snowmass process, the Cosmic Frontier WIMP Direct Detection subgroup (CF1) has drawn on input from the Cosmic Frontier and the broader Particle Physics community to produce this document. The charge to CF1 was (a) to summarize the current status and projected sensitivity of WIMP direct detection experiments worldwide, (b) motivate WIMP dark matter searches over a broad parameter space by examining a spectrum of WIMP models, (c) establish a community consensus on the type of experimental program required to explore that parameter space, and (d) identify the common infrastructure required to practically meet those goals.Comment: Snowmass CF1 Final Summary Report: 47 pages and 28 figures with a 5 page appendix on instrumentation R&

Shape Transition in the Epitaxial Growth of Gold Silicide in Au Thin Films on Si(111)

Growth of epitaxial gold silicide islands on bromine-passivated Si(111) substrates has been studied by optical and electron microscopy, electron probe micro analysis and helium ion backscattering. The islands grow in the shape of equilateral triangles up to a critical size beyond which the symmetry of the structure is broken, resulting in a shape transition from triangle to trapezoid. The island edges are aligned along $Si[110]$ directions. We have observed elongated islands with aspect ratios as large as 8:1. These islands, instead of growing along three equivalent [110] directions on the Si(111) substrate, grow only along one preferential direction. This has been attributed to the vicinality of the substrate surface.Comment: revtex version 3.0, 11 pages 4 figures available on request from [email protected] - IP/BBSR/93-6