Machine Vision Using Cellphone Camera: A Comparison of deep networks for classifying three challenging denominations of Indian Coins

Indian currency coins come in a variety of denominations. Off all the varieties Rs.1, RS.2, and Rs.5 have similar diameters. Majority of the coin styles in market circulation for denominations of Rs.1 and Rs.2 coins are nearly the same except for numerals on its reverse side. If a coin is resting on its obverse side, the correct denomination is not distinguishable by humans. Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model. The digital images were generated by using cheap cell phone cameras. To find the most suitable deep neural network architecture, four were selected based on the preliminary analysis carried out for comparison. The results confirm that two of the four deep neural network models can classify the correct denomination from either side of a coin with an accuracy of 97%.Comment: 6 Pages, 4 Figures, 6 Tables, Conference pape

Diagnostic challenges in ovarian hyperthecosis: Clinical presentation with subdiagnostic testosterone levels

Symptoms of hyperandrogenism and virilization in postmenopausal women warrant workup for ovarian hyperthecosis. In this case series, we discuss two patients who presented with symptoms of hyperandrogenism and metabolic abnormalities including insulin resistance stemming from ovarian hyperthecosis. Imaging revealed normal ovaries in both patients. However, both patients had total serum testosterone levels below the lower diagnostic limit for ovarian hyperthecosis. Due to high clinical suspicion of ovarian hyperthecosis, both patients underwent bilateral oophorectomy without venous sampling for ovarian androgens. The diagnosis of ovarian hyperthecosis was confirmed on histological examination. Both women had improvement in their hyperandrogenic symptoms, testosterone levels, and biochemical features of insulin resistance after surgical intervention. This presentation of ovarian hyperthecosis with subdiagnostic total serum testosterone levels demonstrates the need for continued research into the pathophysiology of the disease, discussion of the diagnostic threshold of total serum testosterone, as well as the inclusion of ovarian hyperthecosis in the differential of postmenopausal women with hyperandrogenism and insulin resistance

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

On-demand ride-pooling (e.g., UberPool) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies. Unlike in Taxi on Demand (ToD) services -- where a vehicle is only assigned one passenger at a time -- in on-demand ride-pooling, each (possibly partially filled) vehicle can be assigned a group of passenger requests with multiple different origin and destination pairs. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering its effect on future assignments. This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem. A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, while the assignment problem in ride-pooling requires an Integer Linear Program (ILP) with bad LP relaxations. To this end, our key technical contribution is in providing a general ADP method that can learn from ILP-based assignments. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach outperforms past approaches on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

Similar prevalence of anxiety and depression symptoms in any ICU survivor patient relative

Background: Iwashyna et al defined a chronic critically ill (CCI) patient as any patient requiring care in ICU for more than or equal to 10 days. Physicians often assume that the prevalence of anxiety and depression symptoms in relatives of CCI patients would be higher than in those patients who are not CCI. We hypothesized that there would be no difference in the prevalence of anxiety and depression symptoms of relatives of a CCI and those whose patients are not CCI. We aimed to establish that the prevalence of anxiety and depression symptoms are similar in relatives of any ICU survivor patient. Methods: The study was a non-interventional, observational, cross-sectional study. Relatives were evaluated as early as possible after day ten following ICU admission for CCI patients and non-CCI patients on or a day before discharge from ICU. During this evaluation, anonymous demographic data of relatives were captured, and PHQ-9 and GAD-7 scales were administered and completed by the relative. Results: A total of 418 relatives consented and were included in the study [104 in CCI patient group and 314 in non-CCI group]. Overall, the prevalence of anxiety and depression symptoms in the entire study cohort was 23.2% (95% CI, 19.4-27.5) and 16.5% (95% CI, 13.2-20.4), respectively. There was no statistical difference between the two groups in the proportion of PHQ-9 total score >9 (p value: 0.577) as well as the GAD-7 total score (p value: 0.816). Conclusions: There was no difference in the prevalence of anxiety and depression symptoms in relatives of a CCI versus those whose patients are not CCI

Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning

The declining participation of beneficiaries over time is a key concern in public health programs. A popular strategy for improving retention is to have health workers `intervene' on beneficiaries at risk of dropping out. However, the availability and time of these health workers are limited resources. As a result, there has been a line of research on optimizing these limited intervention resources using Restless Multi-Armed Bandits (RMABs). The key technical barrier to using this framework in practice lies in the need to estimate the beneficiaries' RMAB parameters from historical data. Recent research has shown that Decision-Focused Learning (DFL), which focuses on maximizing the beneficiaries' adherence rather than predictive accuracy, improves the performance of intervention targeting using RMABs. Unfortunately, these gains come at a high computational cost because of the need to solve and evaluate the RMAB in each DFL training step. In this paper, we provide a principled way to exploit the structure of RMABs to speed up intervention planning by cleverly decoupling the planning for different beneficiaries. We use real-world data from an Indian NGO, ARMMAN, to show that our approach is up to two orders of magnitude faster than the state-of-the-art approach while also yielding superior model performance. This would enable the NGO to scale up deployments using DFL to potentially millions of mothers, ultimately advancing progress toward UNSDG 3.1.Comment: 12 pages, 3 figures, 2 table

SMART MUSIC PLAYER

Nowadays in the era of smart phones world needs a smart applications that are easy to use and very effective in the end. Android is the one of the popular mobile application platform these days which we have used. SMART music player is the android application which can be operated on menu driven commands as well as voice commands. All the basic functionalities of music player can be operated via voice inputs. SMART music player can also be used for the promotions of movies, songs etc. It can be a good advertising media. It also provides the facilities which can synchronize with social media like facebook, twitter etc

Thermal Infrared Imager Development for CubeSat Constellation

Wildfires are becoming a major challenge to our civilization today. Early warning and detection of wildfires not only helps in managing them but also mitigates the loss of lives and property. To meet this challenge, OroraTech is developing a constellation of CubeSats for wildfire detection to alert about wildfire hazards within minutes instead of hours. On board these CubeSats, a thermal infrared imager will scan the surface in multiple spectral bands to identify fires. Radiometric simulations with influences of sun glint, daylight and atmospheric absorption were done. A prototype of the imager based on an uncooled micro-bolometer focal plane array was developed including a shutter system, thermal stabilization, mounting structure and a data processing unit. The imager was tested for absolute temperature accuracy and noise behavior. With image processing the performance of the imager was further improved. The prototype is scheduled to fly on a stratospheric balloon late 2020, and the on-orbit demonstration is planned for early 2021

Prospective randomized subject-masked study of intravitreal bevacizumab monotherapy versus dexamethasone implant monotherapy in the treatment of persistent diabetic macular edema

Purpose: To compare intravitreal bevacizumab monotherapy with intravitreal dexamethasone delayed delivery system monotherapy for persistent diabetic macular edema. Methods: Single-center, randomized, subject-masked study of eyes with persistent diabetic macular edema, defined as central subfield thickness (CST) >340 μm despite ≥3 anti–vascular endothelial growth factors injections within 5 months. The intravitreal bevacizumab monotherapy (n = 23 eyes) and delayed delivery system monotherapy (n = 27 eyes) groups received treatments q1month and q3months, respectively. Results: Baseline best-corrected visual acuity and CST were similar in the two groups. At Month 7, the mean final best-corrected visual acuity (mean ± SD) was 65 ± 16 letters (mean Snellen visual acuity 20/50) and 64 ± 11 letters (20/50) (P = 0.619), the mean change in best-corrected visual acuity was +5.6 ± 6.1 and +5.8 ± 7.6 letters (P = 0.785), the mean final CST was 471 ± 157 and 336 ± 89 μm (P = 0.001), and the mean change in CST was −13 ± 105 and −122 ± 120 μm (P = 0.005) in the intravitreal bevacizumab monotherapy and delayed delivery system monotherapy groups, respectively. The number of injections was 7.0 ± 0.2 and 2.7 ± 0.5 (P < 0.001) in the 2 groups. Conclusion: The two groups had similar best-corrected visual acuity gains. The delayed delivery system monotherapy group achieved a significantly greater reduction of CST compared with the intravitreal bevacizumab monotherapy group, with a q3month interval of treatment, and had no recurrent edema at any visit