How ‘Zerodha’ Used Technology to Disrupt the Indian Stock Trading Industry?

In this practitioner-oriented research, we describe how “Zerodha” entered and disrupted the Indian stock trading industry through the use of technology by overcoming the challenges of (1) developing a new business offering that is accessible to all, (2) gaining trust across the community, and (3) fostering and growing their business ecosystem. Our case-based research illustrates how an organization can enter a well-established business area and create value by (1) rethinking the business model, (2) treating technology as a business enabler, (3) empowering the end user, and (4) proactively investing in the business and community. Based on Zerodha’s experiences, we provide guidelines and recommendations for other businesses contemplating to enter and disrupt an established industry by leveraging technology

A UAV Mission Hierarchy

In the following sections, each of the primary missions are decomposed into mission planning, management, and replanning segments in order to identify what the primary functions a human operator will need to perform. The goal is to understand what tasks/functions are common across different UAV missions and platforms in order to map the generalizability of any particular research project.Prepared for Charles River Analytic

Group Evaluations of Individual Faculty Hospitalists

Introduction Faculty evaluations are important tools for improving faculty-to-resident instruction, but residents in our pediatric and internal medicine/pediatric residency programs would seldom evaluate individual pediatric faculty hospitalists. Our objectives were to: (1) increase the percentage of completed evaluations of individual pediatric hospitalists to greater than 85%, (2) improve the quality of pediatric hospitalist feedback as measured by resident and faculty satisfaction surveys, and (3) to reduce the resident concern of lack of anonymity of evaluations. Methods Members of the resident inpatient team (pediatric and internal medicine/pediatric residents) completed group-based evaluations of individual pediatric hospitalists. A survey to evaluate this change in process was distributed to the pediatric hospitalists (n = 6) and another survey was distributed to residents, both based on a 5-point Likert-type scale. Surveys were completed before and four months after implementation of the changes. Pre- and post-survey data of resident and hospitalist responses were compared using the Mann-Whitney test and probability proportion test. Results The percent of completed evaluations increased from 0% to 86% in one month and to 100% in two months. Thereafter, the percent of completed evaluations remained at 100% through the end of the data collection period at seven months. Hospitalists reported (n = 6, 100% participation) their satisfaction regarding the feedback they received from residents significantly increased for all survey questions. Resident satisfaction (n = 24, 89% participation in postintervention surveys) increased significantly with regards to the evaluation process. Conclusions For hospitalists, group-based resident evaluations of individual hospitalists led to an increased percentage of completed evaluations, improved the quality and quantity of feedback to hospitalists, and increased satisfaction with evaluations. For residents, these changes led to increased satisfaction with the evaluation process

The Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems

Recent studies have shown that with appropriate operator decision support and with sufficient automation, inverting the multiple operators to single-unmanned vehicle control paradigm is possible. These studies, however, have generally focused on homogeneous teams of vehicles, and have not completely addressed either the manifestation of heterogeneity in vehicle teams, or the effects of heterogeneity on operator capacity. An important implication of heterogeneity in unmanned vehicle teams is an increase in the diversity of possible team configurations available for each operator, as well as an increase in the diversity of possible attention allocation schemes that can be utilized by operators. To this end, this paper introduces a discrete event simulation (DES) model as a means to model a single operator supervising multiple heterogeneous unmanned vehicles. The DES model can be used to understand the impact of varying both vehicle team design variables (such as team composition) and operator design variables (including attention allocation strategies). The model also highlights the sub-components of operator attention allocation schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. Results from an experimental case study are then used to validate the model, and make predictions about operator performance for various heterogeneous team configurations.The research was supported by Charles River Analytics, the Office of Naval Research (ONR), and MIT Lincoln Laboratory

One Work Analysis, Two Domains: A Display Information Requirements Case Study

Work domain analyses can be time consuming, requiring extensive interviews, documentation review, and observations, among other techniques. Given the time and resources required, we examine how to generalize a work domain analysis technique, namely the hybrid Cognitive Task Analysis (hCTA) method across two domains in order to generate a common set of display information requirements. The two domains of interest are field workers troubleshooting low voltage distribution networks and telecommunication problems. Results show that there is a high degree of similarity between the two domains due to their service call nature, particularly in tasking and decision-making. While the primary differences were due to communication protocols and equipment requirements, the basic overall mission goals, functions, phases of operation, decision processes, and situation requirements were very similar. A final design for both domains is proposed based on the joint requirements

Surreal: Enhancing Surgical simulation Realism using style transfer

Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of cataract surgery using real data labels from an existing public dataset. Our results highlight the feasibility of the approach and also the powerful possibility to extend this technique to incorporate additional temporal constraints and to different applications

Isospin-Breaking quark condensates in Chiral Perturbation Theory

We analyze the isospin-breaking corrections to quark condensates within one-loop SU(2) and SU(3) Chiral Perturbation Theory including $m_u\neq m_d$ as well as electromagnetic (EM) contributions. The explicit expressions are given and several phenomenological aspects are studied. We analyze the sensitivity of recent condensate determinations to the EM low-energy constants (LEC). If the explicit chiral symmetry breaking induced by EM terms generates a ferromagnetic-like response of the vacuum, as in the case of quark masses, the increasing of the order parameter implies constraints for the EM LEC, which we check with different estimates in the literature. In addition, we extend the sum rule relating quark condensate ratios in SU(3) to include EM corrections, which are of the same order as the $m_u\neq m_d$ ones, and we use that sum rule to estimate the vacuum asymmetry within ChPT. We also discuss the matching conditions between the SU(2) and SU(3) LEC involved in the condensates, when both isospin-breaking sources are taken into account.Comment: 16 pages, 1 figure, final version accepted for publication in Journal of Physics

Reconstruction of MIS 5 climate in the central Levant using a stalagmite from Kanaan Cave, Lebanon

Lying at the transition between the temperate Mediterranean domain and subtropical deserts, the Levant is a key area to study the palaeoclimatic response over glacial–interglacial cycles. This paper presents a precisely dated last interglacial (MIS 5) stalagmite (129–84 ka) from the Kanaan Cave, Lebanon. Variations in growth rate and isotopic records indicate a warm humid phase at the onset of the last interglacial at ~ 129 ka that lasted until ~ 125 ka. A gradual shift in speleothem isotopic composition (125–122 ka) is driven mainly by the δ18O source effect of the eastern Mediterranean surface waters during sapropel 5 (S5). The onset of glacial inception began after ~ 122 ka, interrupted by a short wet pulse during the sapropel 4 (S4) event. Low growth rates and enriched oxygen and carbon values until ~ 84 ka indicate a transition to drier conditions during Northern Hemisphere glaciation

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems