Abstract

A system and method of automatically reconstructing a three - dimensional ( 3D ) model of an object using a machine learning model is provided . The method includes ( i ) obtain ing , using an image capturing device , a color image of an object , ( ii ) generating , using an encoder , a feature map by converting the color image that is represented in the 3D array to n - dimensional array , ( iii ) generating , using the machine learning model , a set of peeled depth maps and a set of RGB maps from the feature map , ( iv ) determining one or more 3D surface points of the object by back projecting the set of peeled depth maps and the set of RGB maps to 3D space , ( v ) reconstructing , using the machine learning model , a 3D model of the object by performing surface reconstruc tion using the one or more 3D surface points of the object

Abstract

An active area of research in cognitive science is characterizing the relationship between brain structure and the observed functional activations. Recent graph diffusion models have had great success in mapping whole-brain, resting-state dy- namics measured using functional Magnetic Resonance Imaging (fMRI) to the brain structure derived using diffusion and T1 brain imaging. Here we test the application of one such graph diffusion method called the Multiple Kernel Learning (MKL) model. MKL model, formulated as a reaction-diffusion system using Wilson-Cowan equations, combines multiple diffusion ker- nels at different scales to predict functional connectome (FC) arising from a fixed structural connectome (SC). Our simulation results demonstrate that the MKL model successfully mapped the relationship between SC and FC from five different Electroen- cephalogram (EEG) bands (delta, theta, alpha, beta, and gamma). We used simultaneously acquired EEG-fMRI and NODDI dataset of 17 participants. The correlation between predicted FC and ground truth FC was higher for EEG bands than for fMRI data. The prediction accuracy peaked for the alpha band, and the highest frequency band, gamma had the lowest prediction accuracy. To the best of our knowledge, this is the first such end- to-end application of multiple kernel graph diffusion framework for modeling EEG data. One of the important features of MKL model is its ability to incorporate structural connectivity features into the generative model that predicts the EEG functional connectivity.

Abstract

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous brain activity fluctuations during the resting-state as captured by func- tional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and mul- tiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose multiple GraphHeat networks (M-GHN), a novel approach for mapping SC-FC. M- GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On the HCP dataset of 100 participants, the M-GHN achieves a high Pearson correlation

Abstract

We present a novel self-supervised framework for learning the discretization-agnostic surface parameterization of arbitrary 3D objects with both bounded and unbounded surfaces. Our framework leverages diffusion enabled global-to-local shape context for each vertex to first partition the unbounded surface into multiple patches using proposed self-supervised PatchNet and subsequently perform independent UV parameterization of these patches by learning forward and backward UV mapping for individual patches. Thus, our framework enables learning a discretization-agnostic parameterization at a lower resolution and then directly infer the parameterization for a higher resolution mesh without retraining. We evaluate our framework on multiple 3D objects from publicly available SHREC [Lian et al. 2011] dataset and report superior/faster UV parameterization over conventional methods.

Abstract

Kathakali is one of the major forms of Classical Indian Dance. The dance form is distinguished by the elaborately colourful makeup, costumes and face masks. In this work, we present (a) a framework to analyze the facial expressions of the actors and (b) novel visualization techniques for the same. Due to extensive makeup, costumes and masks, the general face analysis techniques fail on Kathakali videos. We present a dataset with manually annotated Kathakali sequences for four downstream tasks, i.e. face detection, background subtraction, landmark detection and face segmentation. We rely on transfer learning and fine-tune deep learning models and present qualitative and quantitative results for these tasks. Finally, we present a novel application of style-transfer of Kathakali video onto a cartoonized face. The comprehensive framework presented in the paper paves the way for better understanding, analysis, pedagogy and visualization of Kathakali videos.

Abstract

The simulation of appropriate ground motion is the main objective in earthquake-resistant designing to mitigate the impact of landslides and structural damages. The seismic hazard studies serve as the basis for quantifying the hazard associated within the particular area over a given time period. In this study, the detailed site specific hazard analysis of Tindharia site as well as Darjeeling Sikkim Himalaya (DSH) region has been carried out to assure the safety of human and infrastructure against seismic risk. The study area is mountainous region on north western side of India, falls in seismic zone IV and V, and is one of the most seismically active regions with vulnerable earthquakes along with fatal landslides. The future large earthquakes are likely to occur in the region due to constant movement of Indian plate towards Eurasian plate.Therefore, the heightened seismic risk increases the demand for seismic studies near the Himalayan belt. Thus, the assessment of associated seismic hazard in the study area has been carried out by deterministic and probabilistic seismic hazard approaches. From the results, the seismo-tectonic map of a 300km radial distance of the study area is developed. The deterministic hazard distribution in terms of peak ground acceleration (PGA) and the PGA and peak spectral acceleration (PSA) hazard maps for 2% and 10% probability of exceedance in 50 years at different time periods have been generated. The updated hazard maps developed in this study play pivotal roles in mitigation measures with better pre-disaster prevention by earthquake resistant design and in preparedness and post disaster rescue in the study area

Abstract

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to frst perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. The depth of the transformed quantum circuits is minimized by utilizing the Monte Carlo tree search (MCTS) to perform qubit routing by making it both construct each action and search over the space of all actions. It is aided in performing these tasks by a Graph neural network that evaluates the value function and action probabilities for each state. Along with this, we propose a new method of adding mutex-lock like variables in our state representation which helps factor in the parallelization of the scheduled operations, thereby pruning the depth of the output circuit. Overall, our procedure (referred to as QRoute) performs qubit routing in a hardware agnostic manner, and it outperforms other available qubit routing implementations on various circuit benchmarks.

Abstract

Recent advancements in deep learning have enabled 3D human body reconstruction from a monocular image, which has broad applications in multiple domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People in loose clothing), a novel end-to-end trainable network that accurately recovers the 3D geometry and appearance of humans in loose clothing from a monocular image. SHARP uses a sparse and efficient fusion strategy to combine parametric body prior with a non-parametric 2D representation of clothed humans. The parametric body prior enforces geometrical consistency on the body shape and pose, while the non-parametric representation models loose clothing and handles self-occlusions as well. We also leverage the sparseness of the non-parametric representation for faster training of our network while using losses on 2D maps. Another key contribution is 3DHumans, our new life-like dataset of 3D human body scans with rich geometrical and textural details. We evaluate SHARP on 3DHumans and other publicly available datasets, and show superior qualitative and quantitative performance than existing state-of-the-art methods.

Abstract

The present study compared various empirical and data-driven algorithms to predict Actual Evapotranspiration (AET) using various hydro climatic variables. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India, and Waipara (New Zealand) was estimated using different empirical methods-based PET using Budyko and Turc models. Modelled PET from five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor, Random Forest, and Support Vector Regression were trained to predict AET using meteorological variables. The results show simple empirical-based AET models, Budyko and Turc, can estimate AET very well. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models based on PET have been noted as the most robust models for estimating AET with minimal climate data. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed, and precipitation for estimating AET for semi-arid climatology.

Abstract

The task of multilingual news article similarity en- tails determining the degree of similarity of a given pair of news articles in a language-agnostic setting. This task aims to determine the extent to which the articles deal with the entities and events in ques- tion without much consideration of the subjective aspects of the discourse. Considering the supe- rior representations being given by these models as validated on other tasks in NLP across an array of high and low-resource languages and this task not having any restricted set of languages to focus on, we adopted using the encoder representations from these models as our choice throughout our experiments. For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture. In experimentation, we investigated on several fronts including features passed to the encoder model, data augmentation and ensembling among our major experiments. We found data aug- mentation to be the most effective working strategy among our experiments.