Can Share Price be Forecasted

I have recently opened a trading account in stock market of India. Seeing the ever fluctuating prices of share, I was clueless in how to trade shares using my understanding of financial ratios that I have learnt. This study helps to analyse how the market reacts to the fundamentals of operating performance specified as ratios. The concept of correlation factor is used as it closely predicts the relation between variables. I am looking forward to extend this work to establish general behaviour of unpredictable share prices and to use the interpretation of this work to increase my understanding about fundamentals of operating tools like ratios. Keywords: Correlation Analysis, Share Price, Financial ratios, ROCE, RON

Migration and the evolving mediascape: new media, identity and the transnational politics of the Indian diaspora

Internet-based new media—social media platforms in particular—have profoundly altered the boundaries and contours of civic and political life by offering new opportunities for participation and challenge, as well as new perils of communal competition, surveillance, counter-influence and disruption. Additionally, new media technologies have shown unprecedented capabilities for political communication to cross national boundaries. This project considers the complex factors that impact participation by members of a diaspora in the politics of the homeland—in this case Indian immigrants in the United States. A combined approach of historical inquiry and applied survey research attempts to disaggregate the influence of the digital media ecosystem (social networking platforms in particular), as well as core dynamics of personal identity and the dislocation associated with geographic migration. The tested hypotheses examine whether respondents are more or less likely to consider future political participation based on indexed independent variables related to identity, geographic migration and social media platform usage. Additionally, respondents’ sensitivity to exposure to certain types of news information is also considered through an experiment using hypothetical news stories that vary in content, geography and actor identity. These approaches reflect on the existing scholarship, but more importantly, builds new lines of questioning that span across previously disconnected streams of research, offering a more holistic appraisal that more accurately reflects the large, complex, varied mediascape in which migrants see, share and respond to many different forms of online information, communication and interactivity. Online recruitment of resident Indian and Non-Resident Indian (NRI) survey respondents provided two population samples that allows for comparative examination prior and subsequent to the event of migration. The survey questions themselves encompassed of a broad range of questions addressing socioeconomic status, prior civic activity, social media usage, perceptions about political institutions and expectations of future participation in the form of voting. The implications for this research may yield insights into the shape of possible future transnational phenomena, most notably the prospect of absentee voting in the near future. The specific questions and influences on diasporic participation are considered in this context, and recommendations for follow-up research are provided

Strategies to prevent falls in the elderly: effect of a 10-week Taiji training program on proprioception, functional strength and mobility, and postural adaptation

The impact of elderly falls on the Canadian health care system is widespread. Balance and motor coordination are commonly affected during the aging process due to declining proprioception (Ribeiro & Oliveira, 2007). In addition, there is slower walking speed and shorter stride length among fallers (Wolfson, Judge, Whipple, & King, 1995). Robinovitch et al. (2013) reported that 41% of falls in long term care homes were attributed to incorrect weight shifting. Considering the strong relationship between falls in the elderly and declining proprioception (Mion et al. 1989), the purpose of this study was to examine the effects of a 10-week Taiji training program on ankle proprioception, functional lower extremity strength and mobility and postural adaptation of older adults at risk of falls. A sample of 32 older adults (M = 66.5, SD = 4.94) participated in this study. Sixteen participants were conveniently assigned to the Taiji group; practiced Taiji Quan 6-form twice weekly for 60 minutes for 10-weeks, and completed their weekly Taiji logbook. The remaining 16 participants in the control group; continued their regular activities except Taiji and completed their weekly logbook. All the participants completed pre and post assessments of postural control on an AMTI force platform, functional mobility on the Adapted Timed Up and Go Test (ATGUG), ankle joint proprioception i.e., perception of joint movement sensation, on a tilting platform, and functional strength of lower extremities on the Chair Stand test. A two by two mixed factorial ANOVA indicated significant changes with large effect size for proprioception (backward angle), lower extremity strength (repetitions), functional mobility (ATGUG 5 and ATGUG 4) and medium effect size for functional mobility (ATGUG 2). Changes in the proprioception variable suggest that Taiji may be a valuable alternative to traditional exercise programs. As Taiji exercises are beneficial in enhancing ankle joint backward movement perception and it also increases the efficacy of body movement by promoting protective effects against declining physical functions. Future studies should implement randomized controlled design and a larger sample size

PEEKABOO: Interactive Video Generation via Masked-Diffusion

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.Comment: Project webpage - https://jinga-lala.github.io/projects/Peekaboo

Estimating Time to Clear Pendency of Cases in High Courts in India using Linear Regression

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. The High Court National Judicial Data Grid (HC-NJDG) indexes all the cases pending in the high courts and publishes the data publicly. In this paper, we analyze the data that we have collected from the HC-NJDG portal on 229 randomly chosen days between August 31, 2017 to March 22, 2020, including these dates. Thus, the data analyzed in the paper spans a period of more than two and a half years. We show that: 1) the pending cases in most of the high courts is increasing linearly with time. 2) the case load on judges in various high courts is very unevenly distributed, making judges of some high courts hundred times more loaded than others. 3) for some high courts it may take even a hundred years to clear the pendency cases if proper measures are not taken. We also suggest some policy changes that may help clear the pendency within a fixed time of either five or fifteen years. Finally, we find that the rate of institution of cases in high courts can be easily handled by the current sanctioned strength. However, extra judges are needed only to clear earlier backlogs.Comment: 12 pages, 9 figures, JURISIN 2022. arXiv admin note: text overlap with arXiv:2307.1061

End-to-End Neural Network Compression via ℓ1ℓ2\frac{\ell_1}{\ell_2} Regularized Latency Surrogates

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model's Floating Point Operations (FLOPs) or for on-device latency via a novel $\frac{\ell_1}{\ell_2}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization. Crucially, it is fast and runs in almost the same amount of time as single model training; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve $50\%$ reduction in FLOPs with only $1\%$ drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve $15\%$ reduction in FLOPs, and $11\%$ reduction in on-device latency without drop in accuracy, while still requiring $3\times$ less training compute than SOTA compression techniques. Finally, for transfer learning on smaller datasets, our technique identifies $1.2\times$-$1.4\times$ cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy

Platelet transfusion in pregnancy: clinical profile and pregnancy outcome

Background: Thrombocytopenia, being second important hematological disorder of pregnancy can result in maternal and neonatal morbidity and mortality in some women. Some of these disorders are not associated with adverse pregnancy outcomes while in others it is associated with maternal and neonatal morbidity and mortality. So this study was conducted to evaluate the various causes of thrombocytopenia associated with platelet transfusion and its effect on maternal and neonatal outcome.Methods: It is a retrospective data analysis of 70 peripartum women admitted in a tertiary level hospital with thrombocytopenia, requiring platelet transfusion over a period of 9 months (January 2013 to September 2013). Patients were analyzed for the cause of thrombocytopenia, requirement of platelet transfusion, additional treatment, duration of hospital stay and maternal and neonatal morbidity and mortality.Results: In this study, pre-eclampsia and HELLP was present in 37.1% (n=26) of women requiring platelet transfusion while obstetrical hemorrhage (APH, PPH and Rupture uterus), combined iron deficiency anemia and infective causes accounted for 27.1% (n=19), 17.1% (n=12) and 15.7% (n=11) of women respectively. One case each of APLA and idiopathic thrombocytopenia was seen. 70% of women had to stay in hospital for more than 5 days. Four women expired and the incidence of morbidities was 73.1%. Prematurity was present in 41.1% neonates and three expired in nursery. Neonatal morbidity and mortality was not affected by maternal thrombocytopenia.Conclusions: Thrombocytopenia associated with pathological conditions like HELLP, dengue and malaria were associated with profound maternal and neonatal morbidity

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks

Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (SB) of DNNs - bias towards learning only the simplest features - is a key reason for this brittleness, another recent line of work has surprisingly found that diverse/ complex features are indeed learned by the backbone, and their brittleness is due to the linear classification head relying primarily on the simplest features. To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones. Namely, simple features are replicated several times in the learned representations while complex features might not be replicated. This phenomenon, we term Feature Replication Hypothesis, coupled with the Implicit Bias of SGD to converge to maximum margin solutions in the feature space, leads the models to rely mostly on the simple features for classification. To mitigate this bias, we propose Feature Reconstruction Regularizer (FRR) to ensure that the learned features can be reconstructed back from the logits. The use of {\em FRR} in linear layer training (FRR-L) encourages the use of more diverse features for classification. We further propose to finetune the full network by freezing the weights of the linear layer trained using FRR-L, to refine the learned features, making them more suitable for classification. Using this simple solution, we demonstrate up to 15% gains in OOD accuracy on the recently introduced semi-synthetic datasets with extreme distribution shifts. Moreover, we demonstrate noteworthy gains over existing SOTA methods on the standard OOD benchmark DomainBed as well

Decoding the learning curve of non-descent vaginal hysterectomy in the era of laparoscopy- experience at a Zonal Hospital

Background: Despite of the increasing popularity of laparoscopic hysterectomy, vaginal route still stays pertinent. Non descent vaginal hysterectomy (NDVH) involves d steep learning curve and hence, should be a fundamental part of every Gynaecology residency program. Objective of the study was to assess the learning curve of NDVH surgery skill at a Military Zonal Hospital by a single Specialist over a period of two years.Methods: Retrospective study conducted at Military Hospital, Agra between June 2015 to June 2017 on 30 patients who underwent NDVH for benign gynaecological conditions.Results: The average blood loss was noted to reduce from a mean of 285ml (±108.94) in the first 20 cases (Group 1) to 227ml (±110.89) in the next 10 cases (Group 2) despite of the average uterine size increasing from 8.5 (±1.43) weeks in Group 1 to 10.2 (±2.39) weeks in Group 2. The average time taken in minutes was also seen to reduce from 89.75 (±12.62) in Group 1 to 70.5 (±16.50) in Group 2 indicating an improvement of surgical skills. The average 24 hr post-operative haemoglobin fall of 0.8gm% was similar between the two groups.Conclusions: Acquiring NDVH skills is a slow but rewarding process. NDVH involves no incisions, no elaborate set-up, avoids complications of general anaesthesia and pneumo-peritoneum and displays similar results as of laparoscopy. In limited resource countries vaginal route may be the only available minimally invasive option for hysterectomy. Hence, it’s pertinent that Gynecologists are trained in the same.