GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF). GANDALF relies on a new tabular processing unit with a gating mechanism and in-built feature selection called Gated Feature Learning Unit (GFLU) as a feature representation learning unit. We demonstrate that GANDALF outperforms or stays at-par with SOTA approaches like XGBoost, SAINT, FT-Transformers, etc. by experiments on multiple established public benchmarks. We have made available the code at github.com/manujosephv/pytorch_tabular under MIT License.Comment: 7 pages + Reference & Appendi

Chief Minister’s Jan Awas Yojna-2015 – An Emerging Prospect Of Affordable Housing In Rajasthan

 After independence, the country's urban population has exploded. The migration of people from rural and semi-urban areas to urban areas is a common occurrence. They face a significant shelter challenge in urban areas, which is out of reach due to scarce resources, expensive property, construction materials, and labour, as well as a general lack of infrastructure in urban areas. As a result, policymakers must take a constructive position in encouraging the development of EWS and LIG housing stock in urban areas. According to TG-12 reports, the poorer parts of India accounted for 96 percent of the housing shortage.3 As a result policy proposals were needed to close the gap between urban housing demand and supply. The Pradhan Mantri Awas Yojna-2015 (Urban) and the Chief Minister's Jan Awas Yojna-2015 (Rajasthan) will serve as a watershed moment in this regard. Central and state governments strategically manage the cost of affordable housing units for the EWS and LIG segments by subsidizing affordable houses and extending various incentives, exemptions, rebates, relaxations, interest subsidy, and facilities to various stakeholders with the aim of maximising the use of valuable urban lands to provide EWS and LIG houses at affordable rates to targeted groups. This research paper aims to evaluate the integration of both schemes in order to meet affordable housing goals in the state of Rajasthan, as well as summarise the perspectives of various stakeholders on both schemes

Semantic Consistency for Assuring Reliability of Large Language Models

Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks. However, recent research has highlighted their sensitivity to variations in input prompts. To deploy LLMs in a safe and reliable manner, it is crucial for their outputs to be consistent when prompted with expressions that carry the same meaning or intent. While some existing work has explored how state-of-the-art LLMs address this issue, their evaluations have been confined to assessing lexical equality of single- or multi-word answers, overlooking the consistency of generative text sequences. For a more comprehensive understanding of the consistency of LLMs in open-ended text generation scenarios, we introduce a general measure of semantic consistency, and formulate multiple versions of this metric to evaluate the performance of various LLMs. Our proposal demonstrates significantly higher consistency and stronger correlation with human evaluations of output consistency than traditional metrics based on lexical consistency. Finally, we propose a novel prompting strategy, called Ask-to-Choose (A2C), to enhance semantic consistency. When evaluated for closed-book question answering based on answer variations from the TruthfulQA benchmark, A2C increases accuracy metrics for pretrained and finetuned LLMs by up to 47%, and semantic consistency metrics for instruction-tuned models by up to 7-fold

On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks

As large-scale training regimes have gained popularity, the use of pretrained models for downstream tasks has become common practice in machine learning. While pretraining has been shown to enhance the performance of models in practice, the transfer of robustness properties from pretraining to downstream tasks remains poorly understood. In this study, we demonstrate that the robustness of a linear predictor on downstream tasks can be constrained by the robustness of its underlying representation, regardless of the protocol used for pretraining. We prove (i) a bound on the loss that holds independent of any downstream task, as well as (ii) a criterion for robust classification in particular. We validate our theoretical results in practical applications, show how our results can be used for calibrating expectations of downstream robustness, and when our results are useful for optimal transfer learning. Taken together, our results offer an initial step towards characterizing the requirements of the representation function for reliable post-adaptation performance

Accelerated Neural Network Training with Rooted Logistic Objectives

Many neural networks deployed in the real world scenarios are trained using cross entropy based loss functions. From the optimization perspective, it is known that the behavior of first order methods such as gradient descent crucially depend on the separability of datasets. In fact, even in the most simplest case of binary classification, the rate of convergence depends on two factors: (1) condition number of data matrix, and (2) separability of the dataset. With no further pre-processing techniques such as over-parametrization, data augmentation etc., separability is an intrinsic quantity of the data distribution under consideration. We focus on the landscape design of the logistic function and derive a novel sequence of {\em strictly} convex functions that are at least as strict as logistic loss. The minimizers of these functions coincide with those of the minimum norm solution wherever possible. The strict convexity of the derived function can be extended to finetune state-of-the-art models and applications. In empirical experimental analysis, we apply our proposed rooted logistic objective to multiple deep models, e.g., fully-connected neural networks and transformers, on various of classification benchmarks. Our results illustrate that training with rooted loss function is converged faster and gains performance improvements. Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss. The code implementing our losses and models can be found here for open source software development purposes: https://anonymous.4open.science/r/rooted_loss

Promoting Reproductive Health Using Telemedicine: A Prospective Study Among Rural Appalachian High School Teens

Introduction: Telemedicine allows rural underserved populations access to medical resources that may not be available in their communities. Following promising results with a telemedicine project aimed at educating female students in McDowell County, West Virginia on reproductive health (RH) in 2015, we conducted a follow-up study including both male and female high school students during the 2016 school year. Materials and Methods: Telemedicine sessions on RH were incorporated into existing afterschool programs at two rural high schools. Students’ knowledge on RH was assessed via pre-test, immediate post-test, and 6-month post-test evaluations. RH was taught by medical students and faculty at a distal university. Results: 77 students participated and 37 students completed a 6-month follow-up survey. Statistically significant increases in knowledge scores regarding both birth control and sexually transmitted disease prevention were noted at 6-month follow-up. A larger proportion of students reported “always” utilizing birth control (35.3% vs. 64.7%) as well as condom usage (46.2% vs. 58.8%) at six months compared to before the intervention, but was not statistically significant. The intervention was rated as “effective” or “very effective” by 91.9% of participants, and 75.6% stated they were “very likely” to participate the future. Discussion: Telemedicine is a promising and sustainable tool in teaching RH to rural underserved areas

Welfare policy and labour market outcomes among persons with disabilities (PwDs) in India: Evidence from the NSS 76th Round

India is the host of one of the largest population of persons with disabilities (PwDs) in the world. However, a large section of this population has been unable to access opportunities for economic and social progress, despite the enactment of the RPWD Act, 2016, the country’s most ambitious disability rights legislation to date. This study attempts to analyse the impact of the Indian government’s two primary welfare policies – the disability certificate and a cash transfer (the pension) - on employment outcomes for PwDs. Logistic regression results show that having a certificate with percentage of disability less than 60%, raises the probability of employment by 17%. As the extent of disability rises above 60%, the probability of being employed declines. However, the largest effect size is seen for a narrow set of registered salaried jobs. The effect of the pension is analyzed through a propensity score matching technique for each state, keeping out of pocket (OOP) disability expenses as the outcome, and pension as the treatment. The results reveal negative and significant treatment effects (ATT) of the pension on OOP disability expenses. Interpreting OOP expenses as an indicator of the ‘conversion handicap’ faced by PwDs (Sen, 2004), we argue that cash transfers can also indirectly reduce the barriers to employment

Scleral abscess following posterior subtenon triamcinolone acetonide injection for diabetic macular edema

AbstractA 65-year-old male with uncontrolled diabetes, received posterior subtenon triamcinolone (PST) injection in the right eye for diabetic macular edema. Two days following PST, he developed scleral abscess at the injection site. The Gram stain showed Gram positive cocci in clusters. He responded favorably with systemic control of diabetes, topical concentrated cefazolin, concentrated tobramycin, and intravenous antibiotics. Possibility of infective complications should be considered when using periocular steroids, especially in diabetics. Strict control of diabetes and aggressive systemic antibiotics favor rapid healing in such cases

Promoting Reproductive Health Using Telemedicine: A Prospective Study Among Rural Appalachian High School Teens

Introduction: Telemedicine allows rural underserved populations access to medical resources that may not be available in their communities. Following promising results with a telemedicine project aimed at educating female students in McDowell County, West Virginia on reproductive health (RH) in 2015, we conducted a follow-up study including both male and female high school students during the 2016 school year. Materials and Methods: Telemedicine sessions on RH were incorporated into existing afterschool programs at two rural high schools. Students’ knowledge on RH was assessed via pre-test, immediate post-test, and 6-month post-test evaluations. RH was taught by medical students and faculty at a distal university. Results: 77 students participated and 37 students completed a 6-month follow-up survey. Statistically significant increases in knowledge scores regarding both birth control and sexually transmitted disease prevention were noted at 6-month follow-up. A larger proportion of students reported “always” utilizing birth control (35.3% vs. 64.7%) as well as condom usage (46.2% vs. 58.8%) at six months compared to before the intervention, but was not statistically significant. The intervention was rated as “effective” or “very effective” by 91.9% of participants, and 75.6% stated they were “very likely” to participate the future. Discussion: Telemedicine is a promising and sustainable tool in teaching RH to rural underserved areas