Enhancing M30 Grade Concrete: A Comparative Study of Hooked vs. Crimped Steel Fibers in Fly Ash Mixes

The realm of concrete offers vast opportunities for inventive applications, design, and construction methodologies, given its adaptability and cost-effectiveness. Its versatility in meeting diverse requirements has established it as a highly competitive building material. To address escalating structural demands and harsh environmental factors, new cementitious materials and concrete composites are continually being developed, followed by the need for enhanced durability and performance, as well as pressure to utilize industrial waste materials. The research explores the impact of incorporating fly ash and steel fiber into M30-grade concrete, alongside cement, coarse, and fine aggregate, through experimental methods, with the primary aim of determining optimal ingredient proportions for achieving desired strength. The study evaluates compressive strength variations with fly ash ranging from [10%] to [30%] while hooked and crimped steel fibers [ranging from 0% to 1.5%] in concrete, alongside cement, fine and coarse aggregate. Environmental considerations and the imperative to utilize industrial waste have significantly contributed to advancements in concrete technology and sustainability. Through meticulous analysis of results, meaningful inferences are made in relation to the strength attributes of fly ash fiber-reinforced concrete. Two sets of experiments were conducted, one altering fly ash content while maintaining fixed steel fiber content, and the other varying steel fiber content while keeping other parameters constant. The study aims to provide practical insights for engineers seeking cost-effective and sustainable building construction methods, adhering to specified norms (IS Code: 456-2000)

Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval

Image retrieval has garnered a growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MAB-Net) for image retrieval which incorporates both the learning mechanisms. MABNet consists of master and assistant block, both learning independently through supervision and collectively via self-supervision. The master guides the assistant by providing its knowledge base as a reference for self-supervision and the assistant reports its knowledge back to the master by weight transfer. We perform extensive experiments on the public datasets with and without post-processing

On designing light-weight object trackers through network pruning: Use CNNs or transformers?

Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or transformers. Large sizes of such models do not allow their deployment in low-power conditions and designing compressed variants of large tracking models is of great importance. This paper demonstrates how highly compressed light-weight object trackers can be designed using neural architectural pruning of large CNN and transformer based trackers. Further, a comparative study on architectural choices best suited to design light-weight trackers is provided. A comparison between SOTA trackers using CNNs, transformers as well as the combination of the two is presented to study their stability at various compression ratios. Finally results for extreme pruning scenarios going as low as 1% in some cases are shown to study the limits of network pruning in object tracking. This work provides deeper insights into designing highly efficient trackers from existing SOTA methods.Comment: Submitted at IEEE ICASSP 202

Block-based syntax from context-free grammars

Block-based programming systems employ a jigsaw metaphor to write programs. They are popular in the domain of programming education (e.g., Scratch), but also used as a programming interface for end-users in other disciplines, such as arts, robotics, and configuration management. In particular, block-based environments promise a convenient interface for Domain-Specific Languages (DSLs) for domain experts who might lack a traditional programming education. However, building a block-based environment for a DSL from scratch requires significant effort. This paper presents an approach to engineer block-based language interfaces by reusing existing language artifacts. We present Kogi, a tool for deriving block-based environments from context-free grammars. We identify and define the abstract structure for describing block-based environments. Kogi transforms a context-free grammar into this structure, which then generates a block-based environment based on Google Blockly. The approach is illustrated with four case studies, a DSL for state machines, Sonification Blocks (a DSL for sound synthesis), Pico (a simple programming language), and QL (a DSL for questionnaires). The results show that usable block-based environments can be derived from context-free grammars, and with an order of magnitude reduction in effort

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Exploring the Efficacy of Generic Drugs in Treating Cancer

Thousands of scientific publications discuss evidence on the efficacy of non-cancer generic drugs being tested for cancer. However, trying to manually identify and extract such evidence is intractable at scale. We introduce a natural language processing pipeline to automate the identification of relevant studies and facilitate the extraction of therapeutic associations between generic drugs and cancers from PubMed abstracts. We annotate datasets of drug-cancer evidence and use them to train models to identify and characterize such evidence at scale. To make this evidence readily consumable, we incorporate the results of the models in a web application that allows users to browse documents and their extracted evidence. Users can provide feedback on the quality of the evidence extracted by our models. This feedback is used to improve our datasets and the corresponding models in a continuous integration system. We describe the natural language processing pipeline in our application and the steps required to deploy services based on the machine learning models