Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but poses significant challenges. A large body of work uses 2D affine transformations or homography for the global motion. However, in this work, we introduce a more general representation scheme, which adapts any existing optical flow network to ignore the moving objects and obtain a spatially smooth approximation of the global motion between video frames. We achieve this by a knowledge distillation approach, where we first introduce a low pass filter module into the optical flow network to constrain the predicted optical flow to be spatially smooth. This becomes our student network, named as \textsc{GlobalFlowNet}. Then, using the original optical flow network as the teacher network, we train the student network using a robust loss function. Given a trained \textsc{GlobalFlowNet}, we stabilize videos using a two stage process. In the first stage, we correct the instability in affine parameters using a quadratic programming approach constrained by a user-specified cropping limit to control loss of field of view. In the second stage, we stabilize the video further by smoothing global motion parameters, expressed using a small number of discrete cosine transform coefficients. In extensive experiments on a variety of different videos, our technique outperforms state of the art techniques in terms of subjective quality and different quantitative measures of video stability. The source code is publicly available at \href{https://github.com/GlobalFlowNet/GlobalFlowNet}{https://github.com/GlobalFlowNet/GlobalFlowNet}

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

In molecular research, simulation \& design of molecules are key areas with significant implications for drug development, material science, and other fields. Current classical computational power falls inadequate to simulate any more than small molecules, let alone protein chains on hundreds of peptide. Therefore these experiment are done physically in wet-lab, but it takes a lot of time \& not possible to examine every molecule due to the size of the search area, tens of billions of dollars are spent every year in these research experiments. Molecule simulation \& design has lately advanced significantly by machine learning models, A fresh perspective on the issue of chemical synthesis is provided by deep generative models for graph-structured data. By optimising differentiable models that produce molecular graphs directly, it is feasible to avoid costly search techniques in the discrete and huge space of chemical structures. But these models also suffer from computational limitations when dimensions become huge and consume huge amount of resources. Quantum Generative machine learning in recent years have shown some empirical results promising significant advantages over classical counterparts.

Developing and least developed countries face the dire challenge of ensuring that each child in their country receives required doses of vaccination, adequate nutrition and proper medication. International agencies such as UNICEF, WHO and WFP, among other organizations, strive to find innovative solutions to determine which child has received the benefits and which have not. Biometric recognition systems have been sought out to help solve this problem. To that end, this report establishes a baseline accuracy of a commercial contactless palmprint recognition system that may be deployed for recognizing children in the age group of one to five years old. On a database of contactless palmprint images of one thousand unique palms from 500 children, we establish SOTA authentication accuracy of 90.85% @ FAR of 0.01%, rank-1 identification accuracy of 99.0% (closed set), and FPIR=0.01 @ FNIR=0.3 for open-set identification using PalmMobile SDK from Armatura.

Pedestrian safety is one primary concern in autonomous driving. The under-representation of vulnerable groups in today's pedestrian datasets points to an urgent need for a dataset of vulnerable road users. In this paper, we first introduce a new vulnerable pedestrian detection dataset, BG Vulnerable Pedestrian (BGVP) dataset to help train well-rounded models and thus induce research to increase the efficacy of vulnerable pedestrian detection. The dataset includes four classes, i.e., Children Without Disability, Elderly without Disability, With Disability, and Non-Vulnerable. This dataset consists of images collected from the public domain and manually-annotated bounding boxes. In addition, on the proposed dataset, we have trained and tested five state-of-the-art object detection models, i.e., YOLOv4, YOLOv5, YOLOX, Faster R-CNN, and EfficientDet. Our results indicate that YOLOX and YOLOv4 perform the best on our dataset, YOLOv4 scoring 0.7999 and YOLOX scoring 0.7779 on the mAP 0.5 metric, while YOLOX outperforms YOLOv4 by 3.8 percent on the mAP 0.5:0.95 metric. Generally speaking, all five detectors do well predicting the With Disability class and perform poorly in the Elderly Without Disability class. YOLOX consistently outperforms all other detectors on the mAP (0.5:0.95) per class metric, obtaining 0.5644, 0.5242, 0.4781, and 0.6796 for Children Without Disability, Elderly Without Disability, Non-vulnerable, and With Disability, respectively. Our dataset and codes are available at https://github.com/devvansh1997/BGVP.

Selective classification involves identifying the subset of test samples that a model can classify with high accuracy, and is important for applications such as automated medical diagnosis. We argue that this capability of identifying uncertain samples is valuable for training classifiers as well, with the aim of building more accurate classifiers. We unify these dual roles by training a single auxiliary meta-network to output an importance weight as a function of the instance. This measure is used at train time to reweight training data, and at test-time to rank test instances for selective classification. A second, key component of our proposal is the meta-objective of minimizing dropout variance (the variance of classifier output when subjected to random weight dropout) for training the metanetwork. We train the classifier together with its metanetwork using a nested objective of minimizing classifier loss on training data and meta-loss on a separate meta-training dataset. We outperform current state-of-the-art on selective classification by substantial margins--for instance, upto 1.9% AUC and 2% accuracy on a real-world diabetic retinopathy dataset. Finally, our meta-learning framework extends naturally to unsupervised domain adaptation, given our unsupervised variance minimization meta-objective. We show cumulative absolute gains of 3.4% / 3.3% accuracy and AUC over the other baselines in domain shift settings on the Retinopathy dataset using unsupervised domain adaptation.

Many real-world learning scenarios face the challenge of slow concept drift, where data distributions change gradually over time. In this setting, we pose the problem of learning temporally sensitive importance weights for training data, in order to optimize predictive accuracy. We propose a class of temporal reweighting functions that can capture multiple timescales of change in the data, as well as instance-specific characteristics. We formulate a bi-level optimization criterion, and an associated meta-learning algorithm, by which these weights can be learned. In particular, our formulation trains an auxiliary network to output weights as a function of training instances, thereby compactly representing the instance weights. We validate our temporal reweighting scheme on a large real-world dataset of 39M images spread over a 9 year period. Our extensive experiments demonstrate the necessity of instance-based temporal reweighting in the dataset, and achieve significant improvements to classical batch-learning approaches. Further, our proposal easily generalizes to a streaming setting and shows significant gains compared to recent continual learning methods.

Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language processing. Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text. The approach leverages a pre-trained large language model (LLM), GPT-3, that is fine-tuned on approximately 500 pairs of prompts (inputs) and completions (outputs). Information is extracted either from single sentences or across sentences in abstracts/passages, and the output can be returned as simple English sentences or a more structured format, such as a list of JSON objects. We demonstrate that LLMs trained in this way are capable of accurately extracting useful records of complex scientific knowledge for three representative tasks in materials chemistry: linking dopants with their host materials, cataloging metal-organic frameworks, and general chemistry/phase/morphology/application information extraction. This approach represents a simple, accessible, and highly-flexible route to obtaining large databases of structured knowledge extracted from unstructured text. An online demo is available at http://www.matscholar.com/info-extraction.

Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.