Abstract

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high–level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences.

Abstract

Uber drivers in India are witnessing a slow emergence of everyday work formalization predominantly through their engagement with the Uber platform. If a vast segment of the informal employment sector in a country resembles gig work, can companies like Uber bring organisational ability and wage regularity to employment? Despite Uber’s economic model being challenged on several labour fronts in scholarship from the global North, we argue everyday interactions with the Uber platform are ushering organised work practices and improved financial stability for drivers who formerly hail from the informal employment sector in India. The everyday driving for Uber is filtered through a conceptual and practical work model drivers gain with due experience of Uber’s platform features. Our study uncovers the relationship between the controlling demands of the Uber platform and ensuing driver work adaptations. We present findings from a qualitative investigation of Uber ride-hailing services impacting drivers to 1. optimize earnings, 2. link work effort to wages, and 3. converge towards platform compliance. We posit engaged and persistent interactions with the Uber platform bring structure and formality to the profession of driving for Indian Uber drivers

Abstract

The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing datasets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for drug-like molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate drug-like molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, Quantitative Estimate of Drug Likeliness, Topological Polar Surface Area, and Hydration Free Energy along with the binding affinity. For multi-objective optimization, we have devised a novel strategy in which the property being used to calculate the

Abstract

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue’s contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Abstract

Owing to the inherent geometry, the extent of map reconstructed using line-based SfM(structure from motion) is superior to point-based SfM. However, with the existing methods, estimation of structure and motion from observed 2D line segments in images is more complex than that of points. To overcome this, we propose a simple and robust 1-parameter approach for Structure and Motion Estimation from line features in Manhattan Scenes from three views. We leverage the vanishing point directions to estimate the relative rotations as well as to fix the 3D line direction. In consequence we build a constraints matrix, which has the relative translations and 3D line depth as its null space. We then perform 1-parameter line BA using factor graph based cost function. We compare the efficacy of our method with standard line triangulation in synthetic as well as real-world scenes. Keywords-manhattan scenes, line features, Structure and motion estimation, vanishing points, vanishing line, Plucker coordinates, orthonormal representation, factor graph optimization

Abstract

It is critical for aerial robots flying in city scale urban environments to make very quick estimates of a building depth with respect to itself. It should be done in a matter of few views to navigate itself, avoiding collisions with such a towering structure. As such, no one has attacked this problem. We bring together several modules combining deep learning and 3D vision to showcase a quick reconstruction in a few views. We exploit the inherent planar structure in the buildings (facades, windows) for this purpose. We evaluate the efficacy of our pipeline with various constraints and errors from multi-view geometry using ablation studies. We then retrieve the skyline of the buildings in synthetic as well as real-world scenes.

Abstract

Surface normal estimation is an essential component of several computer and robot vision pipelines. While this problem has been extensively studied, most approaches are geared towards indoor scenes and often rely on multiple modalities (depth, multiple views) for accurate estimation of normal maps. Outdoor scenes pose a greater challenge as they exhibit significant lighting variation, often contain occluders, and structures like building facades are often ridden with numerous windows and protrusions. Conventional supervised learning schemes excel in indoor scenes, but do not exhibit competitive performance when trained and deployed in outdoor environments. Furthermore, they involve complex network architectures and require many more trainable parameters. To tackle these challenges, we present an adversarial learning scheme that regularizes the output normal maps from a neural network to appear more realistic, by using a small number of precisely annotated examples. Our method presents a lightweight and simpler architecture, while improving performance by at least 1.5x across most metrics. We evaluate our approaches against the state-of-the-art on normal map estimation, on a synthetic and a real outdoor dataset, and observe significant performance enhancements.

Abstract

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning across several organs and subtypes. However, not much work has gone into analysis of all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs normal patches for 11 subtypes spanning 7 organs (9792 tissue slides) to achieve near-perfect classification performance. We used these models to investigate their performances in the test set of other organs (cross organ inference). We found that every model had a good cross organ inference accuracy when tested on breast, colorectal and liver cancers. Further, a high accuracy is observed between models trained on the cancer subtypes originating from same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high dimensional feature space. We further hypothesized that the high cross organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei geometrical features present within the high attention regions. Keywords: TCGA, histopathology, cancer classification, cross-organ inference, deep learning, tissue morphology, class activation map.

Abstract

Owing to the large dimensions of the histopathology whole slide images (WSI), visually searching for clinically significant regions (patches) is a tedious task for a medical expert. Sequential analysis of several such images further increases the workload resulting in poor diagnosis. A major impediment towards automating this task using deep learning models is that it requires a huge chunk of laboriously annotated data in the form of WSI patches. Our work suggests a novel CNN-based, expert feedback-driven interactive learning technique to mitigate this issue. The proposed method seeks to acquire labels of the most informative patches in small increments with multiple feedback rounds to maximize the throughput. It requires the expert to query a patch of interest from one slide and provide feedback to a set of unlabelled patches chosen using the proposed sampling strategy from a ranked list. The experiments on a large patient cohort of colorectal cancer histological patches (100K images with nine classes of tissues) show a significant reduction (≈ 95%) in the amount of labelled data required to achieve state-of theart results when compared to other existing interactive learning methods (35% − 50%). We also demonstrate the utility of the proposed technique to assist a WSI tumor segmentation annotation task using the ICIAR breast cancer challenge dataset (≈ 12.5K patches per slide). The proposed technique reduces the scanning and searching area to about 2% of the total area of WSI (by seeking labels of ≈ 250 informative patches only) and achieves segmentation outputs with 85% IOU. Thus our work helps avoid the routine procedure of exhaustive scanning and searching during annotation and diagnosis in general.

Abstract

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. Therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual manuscripts before downstream processing. Another challenge is the highly curved and deformed boundaries of manuscripts, causing them to often overlap with each other. To tackle such challenges, we introduce a new document image dataset called IMMI (Indic Multi Manuscript Images). To improve the efficiency of dataset and aid deep network training, we also propose an approach which generates synthetic images to augment sourced non-synthetic images. We conduct experiments using modified versions of existing document instance segmentation frameworks. The results demonstrate the efficacy of the new frameworks for the task. Overall, our contributions enable robust extraction of individual historical manuscript pages. This in turn, could potentially enable better performance on downstream tasks such as region-level instance segmentation within handwritten manuscripts and optical character recognition