CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.Comment: Accepted at CVPR-201

Multi-depth computational periscopy with an ordinary camera

We demonstrate non-line-of-sight imaging of multi-depth scenes using only a single photograph from an ordinary digital camera. The hidden scene, comprising two images at different depths, is partially occluded from a visible wall by an opaque occluding object. The distance from the visible wall to the hidden surfaces, and the images they contain, are recovered.Accepted manuscrip

Inferring object states and articulation modes from egocentric videos

We develop algorithms for understanding objects from the point of view of interacting with them. There are two key aspects to obtaining such an understanding. First, objects can occur in different states and we need features that are sensitive to such states. Second, different objects can be articulated in different ways and we need to understand how to correctly infer their modes of articulation. We propose self and weakly supervised techniques to obtain such an understanding of objects purely through observation of how humans interact with the world around them through their hands. Our experiments on the challenging EPIC- KITCHENS dataset show the merits of using human hands as a probe for understanding objects.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Feature Reinforcement using Autoencoders

Cardiovascular disease (CVD) is the number one cause of death globally, more people die annually from CVDs than from any other cause. People with cardiovascular disease or who are at high cardiovascular risk need early detection and management using counselling and medicines, as appropriate. The early detection of CVDs needs an expert hand and awareness amongst people. Here is where Data analytics can help in predicting the cardiovascular cases before-hand by helping to make informed decisions faster, with great accuracy and at a much earlier date. The dataset used is the Cleveland Heart disease Database taken from UCI learning data set repository. The dataset is being divided into five classes, 0 corresponding to absence of any disease and 1,2,3,4 corresponding to grades of heart disease. The dataset has been bifurcated into absence (0) and presence (1, 2, 3 and 4) of the heart disease. Using medical profiles such as age, sex, blood pressure, cholesterol, sugar level etc. The classifiers can predict the probability of patients getting a heart disease. There is no dearth of classification techniques but feature engineering and data representation is the crux of the model building pre-activity. When done efficiently, this could make the model more robust and accurate. We are introducing an idea of feature reinforcement technique using Artificial Neural Networks (MLP)-Auto encoders. In this technique we would represent the features in an abstracted format using MLPAutoencoders and then reinforce the input features with the abstracted features. This activity would exhaustively capture the latency in input features thus making our feature representation more robust and resilient. We have tested our technique on Cleveland Heart disease dataset. The results obtained by using our technique had higher degree of accuracy than the results obtained with input features alon

Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction

Time series prediction with neural networks has been the focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is important to evaluate their strengths and weaknesses. In this paper, we present an evaluation study that compares the performance of deep learning models for multi-step ahead time series prediction. The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks. We provide a further comparison with simple neural networks that use stochastic gradient descent and adaptive moment estimation (Adam) for training. We focus on univariate time series for multi-step-ahead prediction from benchmark time-series datasets and provide a further comparison of the results with related methods from the literature. The results show that the bidirectional and encoder-decoder LSTM network provides the best performance in accuracy for the given time series problems

Awareness about physiotherapy in patients with diabetes: A population-based study

Background and Purpose: The main purpose of this study is to see the awareness about physiotherapy in people suffering from a diabetic condition in India. Physiotherapy can play a very important role in the life of people with the diabetic condition. Materials & Methods: The participants who visited doctors for diabetic treatment were asked to volunteer in the study. A total of 40 subjects volunteered in the study. All were informed about the study and data collection was made in google form. Results: By result shows very little awareness in a diabetic patient about physiotherapy and its benefit in the condition. Conclusion: Therefore, by this study and the above data, we can conclude that there is very little awareness in the people in diabetic condition for the benefit in their life. They don’t know the advantages of physiotherapy in diabetic conditions and the effects on ROM and their quality of life and pain. Awareness needs to be created in the Indian population about the effects of physiotherapy in diabetic patients and its benefits in their life