Replicator-mutator dynamics of Rock-Paper-Scissors game: Learning through mistakes

We generalize the Bush--Mosteller learning, the Roth--Erev learning, and the social learning to include mistakes such that the nonlinear replicator-mutator equation with either additive or multiplicative mutation is generated in an asymptotic limit. Subsequently, we exhaustively investigate the ubiquitous Rock-Paper-Scissors game for some analytically tractable motifs of mutation pattern. We consider both symmetric and asymmetric game interactions, and reveal that mistakes can some-times help the players learn. While the replicator-mutator flow exhibits rich dynamics that include limit cycles and chaotic orbits, it can control chaos as well to lead to rational Nash equilibrium outcome. Moreover, we also report an instance of hitherto unknown Hamiltonian structure of the replicator-mutator equation.Comment: 16 pages, 14 figure

A Graph-Based Context-Aware Model to Understand Online Conversations

Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. But in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations' surrounding contexts should improve the model's performance for the inference task at hand. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples "nearby" comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment's embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and found that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro-F1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively.Comment: 25 pages, 9 figures. arXiv admin note: text overlap with arXiv:2202.0817

Characterising User Content on a Multi-lingual Social Network

Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multi-cultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.Comment: Accepted at ICWSM 2020, please cite the ICWSM versio

The study of clinico-pathological correlation and treatment outcome in acute allograft rejection in the immediate post renal transplant period

Background: The kidney Tx is the treatment of choice for patients with ESRD. However, episodes of AR have a negative impact on short- and long-term graft survival. In spite of immunosuppressive medications, CNI, MMF and steroid, the AR remains a crucial problem for Tx. This analysis was performed to evaluate the changing profile of early AR (during first week of transplant) and its repercussions on graft survival.Methods: This study was an observational cohort study and included 50 renal transplant patients irrespective of age, sex and race who developed bx proven AR within first week of transplant. Three groups were made according to histopathology: ACR, AMR and mixed rejection group. The patients were followed for 6 months thereafter.Results: AR within a week of renal Tx were less symptomatic except decrease in UO. ACR was more common (72%) than AMR and mixed rejections. AMR and Mixed group required more therapeutic modalities than ACR. More patients required HD during AR in AMR and mixed rejection group than ACR. The mean s.cr at 6 months was 1.3,1.5 and 1.6 in ACR, AMR and mixed group respectively. There were more incidences of BK viremia, CMV infection UTI and rejection fronts follow up in AMR and mixed group than ACR group.Conclusions: Acute rejections within a week are less symptomatic and ACR occurred more frequently than AMR and mixed rejection There were more incidences of BKV, CMV and UTI for 6 months follow up in AMR and Mixed rejection group

Cytological spectrum of granulomatous mastitis: diagnostic and treatment challenges

Background: Granulomatous mastitis (GM) is an inflammatory disease of the breast which clinico- radiologically mimics both inflammatory and malignant lesions. This leads to diagnostic dilemmas and delay in treatment. The aim of the present study was to review the cases diagnosed as granulomatous mastitis on Fine Needle Aspiration Cytology (FNAC) with an objective to co-relate their clinico-radiological findings, histology review where available and follow up treatment received to establish etiology and study the treatment outcome.Methods: Cytologically diagnosed cases of granulomatous mastitis were retrieved and reviewed from August 2015 - July 2017 records. Clinico-radiological co-relation, histology review where available and follow up treatment records were sought for.Results: Around 31.7% (530/1670) cases were reported as malignant, 60.3% (1009/1670) as benign proliferative and 7.9% (131/1670) as inflammatory lesions by breast FNA. 3.1% (51/1670) cases were reported as GM of all breast FNAC and 38% (51/131) of all inflammatory lesions. Follow up was available for 47 cases. Of which 26 (55.3%) cases were diagnosed as Tubercular Granulomatous mastitis (TGM) and 21(44.7%) were idiopathic granulomatous mastitis (IGM).Conclusions: Countries where tuberculosis is endemic, high degree of clinical suspicion and detailed work-up to rule out TGM is essential for all cases of granulomatous mastitis. Authors recommend a multidisciplinary workup with microbiological culture and molecular based tests on FNA material. This retrospective study illustrates that the cause of GM needs to be determined accurately for timely treatment, to avoid unnecessary delays and treatment dilemma in these patients

A Comparative Study of Supervised Machine Learning Algorithms for Fruit Prediction

In this paper, machine learning techniques have been applied for the fruit image classification and prediction over a large dataset. In the implemented work, five models have been developed and their performances are compared in predicting the fruit names. These models are based on five supervised learning techniques i.e., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Softmax. The experimental results show that Support Vector Machine algorithm performs the best for large datasets and also Support Vector Machine is the best for small datasets. The results also reveal that reduction in the number of fruits reduces the accuracy’s of each algorithm

Intra-operative point-of-procedure delineation of oral cancer margins using optical coherence tomography.

ObjectivesSurgical margin status is a significant determinant of treatment outcome in oral cancer. Negative surgical margins can decrease the loco-regional recurrence by five-fold. The current standard of care of intraoperative clinical examination supplemented by histological frozen section, can result in a risk of positive margins from 5 to 17 percent. In this study, we attempted to assess the utility of intraoperative optical coherence tomography (OCT) imaging with automated diagnostic algorithm to improve on the current method of clinical evaluation of surgical margin in oral cancer.Materials and methodsWe have used a modified handheld OCT device with automated algorithm based diagnostic platform for imaging. Intraoperatively, images of 125 sites were captured from multiple zones around the tumor of oral cancer patients (n = 14) and compared with the clinical and pathologic diagnosis.ResultsOCT showed sensitivity and specificity of 100%, equivalent to histological diagnosis (kappa, ĸ = 0.922), in detection of malignancy within tumor and tumor margin areas. In comparison, for dysplastic lesions, OCT-based detection showed a sensitivity of 92.5% and specificity of 68.8% and a moderate concordance with histopathology diagnosis (ĸ = 0.59). Additionally, the OCT scores could significantly differentiate squamous cell carcinoma (SCC) from dysplastic lesions (mild/moderate/severe; p ≤ 0.005) as well as the latter from the non-dysplastic lesions (p ≤ 0.05).ConclusionThe current challenges associated with clinical examination-based margin assessment could be improved with intra-operative OCT imaging. OCT is capable of identifying microscopic tumor at the surgical margins and demonstrated the feasibility of mapping of field cancerization around the tumor

Clinical Presentation and Outcome of Sinonasal Mucormycosis in Pre COVID-19 Era from a Tertiary Care Centre in Uttarakhand: A Cross-sectional Study

Introduction: Sinonasal mucormycosis is an invasive fungal rhinosinusitis which rapidly involves orbits and brain either by direct extension or angioinvasion. Uncontrolled diabetics and immunocompromised patients are prone for this invasive fungal infection. The rapidity of severity of symptoms and morbidity of this invasive fungal infection warrant earliest diagnosis and appropriate management. This research work will be helpful in comparing sinonasal mucormycosis in Coronavirus Disease2019 (COVID-19) patients as all cases in present study were not associated with COVID-19 infection. Aim: To observe presenting features and estimate morbidity of mucormycosis patients in tertiary care hospital. Materials and Methods: This cross-sectional retroprospective study was conducted in Department of Otorhinolaryngology of a tertiary teaching hospital from July 2018 to March 2020. Total 25 sinonasal mucormycosis patients who underwent endoscopic debridement along with amphotericin B were included in this study. Patient was analysed regarding age, gender, chief complains, accompanying co-morbidity, extension of disease, medical treatment, surgical intervention and final outcome. Statistical analysis was done in the form of mean, mode, median and percentage wherever required. Results: Total 14 (56%) patients were male and 11 (44%) patients were female with median age of 48 years. Total 13 (52 %) patients had facial pain or headache while 13 (52%) had facial or orbital swelling followed by nasal symptoms in 5 (20%), vision loss in 4 (16%) and ptosis in 3 (12%) cases. Twenty four (96%) of cases were having uncontrolled diabetes mellitus. Only 3 (12%) had limited sinonasal disease while 22 (88%) had fungal invasion in orbit. Total 7 (28%) patients had intracranial extension. Out of 25 patients, 4 (16%) expired, 7 (28%) had permanent vision loss and 12 (48%) recovered completely and 2 (8%) left hospital against medical advice. Conclusion: Present study concluded that mucormycosis is strongly associated with uncontrolled diabetes mellitus. Most common presenting features were facial pain, headache and facial orbital swelling. Only half of the patients recovered with minimal morbidity. Mortality is associated with intracranial extension of mucormycosis. Early diagnosis, extensive and timely endoscopic debridement and appropriate use of amphotericin B is key for treatment of black fungus