Variational Clustering: Leveraging Variational Autoencoders for Image Clustering

Recent advances in deep learning have shown their ability to learn strong feature representations for images. The task of image clustering naturally requires good feature representations to capture the distribution of the data and subsequently differentiate data points from one another. Often these two aspects are dealt with independently and thus traditional feature learning alone does not suffice in partitioning the data meaningfully. Variational Autoencoders (VAEs) naturally lend themselves to learning data distributions in a latent space. Since we wish to efficiently discriminate between different clusters in the data, we propose a method based on VAEs where we use a Gaussian Mixture prior to help cluster the images accurately. We jointly learn the parameters of both the prior and the posterior distributions. Our method represents a true Gaussian Mixture VAE. This way, our method simultaneously learns a prior that captures the latent distribution of the images and a posterior to help discriminate well between data points. We also propose a novel reparametrization of the latent space consisting of a mixture of discrete and continuous variables. One key takeaway is that our method generalizes better across different datasets without using any pre-training or learnt models, unlike existing methods, allowing it to be trained from scratch in an end-to-end manner. We verify our efficacy and generalizability experimentally by achieving state-of-the-art results among unsupervised methods on a variety of datasets. To the best of our knowledge, we are the first to pursue image clustering using VAEs in a purely unsupervised manner on real image datasets

Human-Robot Handshaking: A Review

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.Comment: Pre-print version. Accepted for publication in the International Journal of Social Robotic

Significance of Diabetes (Siphon) in General And Orthodontic Treatment – A Literature Review

Diabetes Mellitus is a metabolic disorder of carbohydrate, protein and fat resulting from defective synthesis of insulin or its action on body tissue. It is one of the most commonly affected diseases worldwide and India has increasing prevalence in 2018 from 45% to 64%. Some of the deleterious effects of diabetes on oral health include xerostomia, debris accumulation,dental caries, recurrent infections, periodontitis etc. Periodontal destruction is the limiting factor in the orthodontic treatment. Maintaining oral hygiene and prevention of periodontal destruction is important before seeking for orthodontic treatment. Many researches and advancements made for periodontally compromised patients which include low profilebrackets, copper NiTi wires and additional anchorages like mini screws. This article emphasizes on significance of diabetes in general and in orthodontic patients and treatment modalities of such patients

Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2018 More info can be found at the project page at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and the supplementary video can be found at https://www.youtube.com/watch?v=420QmM_Z8v

Detection rates of recurrent prostate cancer : 68Gallium (Ga)-labelled prostate-specific membrane antigen versus choline PET/CT scans. A systematic review

Background: The aim of this work was to assess the use of prostate-specific membrane antigen (PSMA)-labelled radiotracers in detecting the recurrence of prostate cancer. PSMA is thought to have higher detection rates when utilized in positron emission tomography (PET)/computed tomography (CT) scans, particularly at lower prostate-specific antigen (PSA) levels, compared with choline-based scans. Methods: A systematic review was conducted comparing choline and PSMA PET/CT scans in patients with recurrent prostate cancer following an initial curative attempt. The primary outcomes were overall detection rates, detection rates at low PSA thresholds, difference in detection rates and exclusive detection rates on a per-person analysis. Secondary outcome measures were total number of lesions, exclusive detection by each scan on a per-lesion basis and adverse side effects. Results: Overall detection rates were 79.8% for PSMA and 66.7% for choline. There was a statistically significant difference in detection rates favouring PSMA [OR (M–H, random, 95% confidence interval (CI)) 2.27 (1.06, 4.85), p = 0.04]. Direct comparison was limited to PSA < 2 ng/ml in two studies, with no statistically significant difference in detection rates between the scans [OR (M–H, random, 95% CI) 2.37 (0.61, 9.17) p = 0.21]. The difference in detection on the per-patient analysis was significantly higher in the PSMA scans (p < 0.00001). All three studies reported higher lymph node, bone metastasis and locoregional recurrence rates in PSMA. Conclusions: PSMA PET/CT has a better performance compared with choline PET/CT in detecting recurrent disease both on per-patient and per-lesion analysis and should be the imaging modality of choice while deciding on salvage and nonsystematic metastasis-directed therapy strategies.Peer reviewedFinal Published versio

MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction

Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative Human-Robot Interactions (HRI). Learning from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown promising results, especially when coupled with representation learning techniques. However, such methods for learning HRI either do not scale well to high dimensional data or cannot accurately adapt to changing via-poses of the interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD), a method that couples deep representation learning and probabilistic machine learning to address the problem of two-party physical HRIs. We learn the interaction dynamics from demonstrations, using Hidden Semi-Markov Models (HSMMs) to model the joint distribution of the interacting agents in the latent space of a Variational Autoencoder (VAE). Our experimental evaluations for learning HRI from HHI demonstrations show that MILD effectively captures the multimodality in the latent representations of HRI tasks, allowing us to decode the varying dynamics occurring in such tasks. Compared to related work, MILD generates more accurate trajectories for the controlled agent (robot) when conditioned on the observed agent's (human) trajectory. Notably, MILD can learn directly from camera-based pose estimations to generate trajectories, which we then map to a humanoid robot without the need for any additional training.Comment: Accepted at the IEEE-RAS International Conference on Humanoid Robots (Humanoids) 202

Evaluation of the Handshake Turing Test for anthropomorphic Robots

Handshakes are fundamental and common greeting and parting gestures among humans. They are important in shaping first impressions as people tend to associate character traits with a person's handshake. To widen the social acceptability of robots and make a lasting first impression, a good handshaking ability is an important skill for social robots. Therefore, to test the human-likeness of a robot handshake, we propose an initial Turing-like test, primarily for the hardware interface to future AI agents. We evaluate the test on an android robot's hand to determine if it can pass for a human hand. This is an important aspect of Turing tests for motor intelligence where humans have to interact with a physical device rather than a virtual one. We also propose some modifications to the definition of a Turing test for such scenarios taking into account that a human needs to interact with a physical medium.Comment: Accepted as a Late Breaking Report in The 15th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI) 202