Automating a robotic task, e.g., robotic suturing can be very complex and time-consuming. Learning a task model to autonomously perform the task is invaluable making the technology, robotic surgery, accessible for a wider community. The data of robotic surgery can be easily logged where the collected data can be used to learn task models. This will result in reduced time and cost of robotic surgery in which a surgeon can supervise the robot operation or give high-level commands instead of low-level control of the tools. We present a data-set of needle insertion in soft tissue with two arms where Arm 1 inserts the needle into the tissue and Arm 2 actively manipulate the soft tissue to ensure the desired and actual exit points are the same. This is important in real-surgery because suturing without active manipulation of tissue may yield failure of the suturing as the stitch may not grip enough tissue to resist the force applied for the suturing. We present a needle insertion dataset including 60 successful trials recorded by 3 pair of stereo cameras. Moreover, we present Deep-robot Learning from Demonstrations that predicts the desired state of the robot at the time step after t (which the optimal action taken at t yields) by looking at the video of the past time steps, i.e. n step time history where N is the memory time window, of the task execution. The experimental results illustrate our proposed deep model architecture is outperforming the existing methods. Although the solution is not yet ready to be deployed on a real robot, the results indicate the possibility of future development for real robot deployment.

In real-world situations, data scientists often start an analysis with a simple and easy to implement model such as linear or logistic regression. There are various advantages of this approach such as getting a sense of the data with a minimum cost and giving food for thoughts on how to solve a business problem. In this blog post, I decided to start from the opposite side by applying a multilayer perceptron model (neural network) to predict customer churn. I think it is quite fun and exciting to try different algorithms or at least to know how you can solve a problem in a more sophisticated way. Customer churn is when a customer decides to stop using services, content, or products from a company.

In this article, we will cover the custom-based descriptive statistical analysis with python. Although in python we can do this by just a one-word method i.e. describe(). But still what if we make our own function to generate a statistical report. The statistics we get from the described method are very useful but give minimum information. For example, the picture below shows the result from describe() method.

Time series have attracted widespread attention in many fields today. Based on the analysis of complex networks and visibility graph theory, a new time series forecasting method is proposed. In time series analysis, visibility graph theory transforms time series data into a network model. In the network model, the node similarity index is an important factor. On the basis of directly using the node prediction method with the largest similarity, the node similarity index is used as the weight coefficient to optimize the prediction algorithm. Compared with the single-point sampling node prediction algorithm, the multi-point sampling prediction algorithm can provide more accurate prediction values when the data set is sufficient. According to results of experiments on four real-world representative datasets, the method has more accurate forecasting ability and can provide more accurate forecasts in the field of time series and actual scenes.

Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In this work, we propose a new way to estimate the ITE using the domain generalization framework of invariant risk minimization (IRM). IRM uses data from multiple domains, learns predictors that do not exploit spurious domain-dependent factors, and generalizes better to unseen domains. We propose an IRM-based ITE estimator aimed at tackling treatment assignment bias when there is little support overlap between the control group and the treatment group. We accomplish this by creating diversity: given a single dataset, we split the data into multiple domains artificially. These diverse domains are then exploited by IRM to more effectively generalize regression-based models to data regions that lack support overlap. We show gains over classical regression approaches to ITE estimation in settings when support mismatch is more pronounced.

The manifold Helmholtzian (1-Laplacian) operator $\Delta_1$ elegantly generalizes the Laplace-Beltrami operator to vector fields on a manifold $\mathcal M$. In this work, we propose the estimation of the manifold Helmholtzian from point cloud data by a weighted 1-Laplacian $\mathbf{\mathcal L}_1$. While higher order Laplacians ave been introduced and studied, this work is the first to present a graph Helmholtzian constructed from a simplicial complex as an estimator for the continuous operator in a non-parametric setting. Equipped with the geometric and topological information about $\mathcal M$, the Helmholtzian is a useful tool for the analysis of flows and vector fields on $\mathcal M$ via the Helmholtz-Hodge theorem. In addition, the $\mathbf{\mathcal L}_1$ allows the smoothing, prediction, and feature extraction of the flows. We demonstrate these possibilities on substantial sets of synthetic and real point cloud datasets with non-trivial topological structures; and provide theoretical results on the limit of $\mathbf{\mathcal L}_1$ to $\Delta_1$.

ICD coding is the international standard for capturing and reporting health conditions and diagnosis for revenue cycle management in healthcare. Manually assigning ICD codes is prone to human error due to the large code vocabulary and the similarities between codes. Since machine learning based approaches require ground truth training data, the inconsistency among human coders is manifested as noise in labeling, which makes the training and evaluation of ICD classifiers difficult in presence of such noise. This paper investigates the characteristics of such noise in manually-assigned ICD-10 codes and furthermore, proposes a method to train robust ICD-10 classifiers in the presence of labeling noise. Our research concluded that the nature of such noise is systematic. Most of the existing methods for handling label noise assume that the noise is completely random and independent of features or labels, which is not the case for ICD data. Therefore, we develop a new method for training robust classifiers in the presence of systematic noise. We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise. We compared our method with the baseline that does not handle label noise and the baseline methods that assume random noise, and demonstrated that our proposed method outperforms all baselines when evaluated on expert validated labels.

Abstract--Machine learning (ML) models are constructed by expert ML practitioners using various coding languages, in which they tune and select models hyperparameters and learning algorithms for a given problem domain. They also carefully design an objective function or loss function (often with multiple objectives) that captures the desired output for a given ML task such as classification, regression, etc. In multi-objective optimization, conflicting objectives and constraints is a major area of concern. In such problems, several competing objectives are seen for which no single optimal solution is found that satisfies all desired objectives simultaneously. In the past VA systems have allowed users to interactively construct objective functions for a classifier. In this paper, we extend this line of work by prototyping a technique to visualize multi-objective objective functions either defined in a Jupyter notebook or defined using an interactive visual interface to help users to: (1) perceive and interpret complex mathematical terms in it and (2) detect and resolve conflicting objectives. Visualization of the objective function enlightens potentially conflicting objectives that obstructs selecting correct solution(s) for the desired ML task or goal. We also present an enumeration of potential conflicts in objective specification in multi-objective objective functions for classifier selection. Furthermore, we demonstrate our approach in a VA system that helps users in specifying meaningful objective functions to a classifier by detecting and resolving conflicting objectives and constraints. Through a within-subject quantitative and qualitative user study, we present results showing that our technique helps users interactively specify meaningful objective functions by resolving potential conflicts for a classification task. In the past, researchers in visual analytics (VA) have investigated making ML model construction interactive, which means developing visual interfaces that allow users to construct ML models by interacting with graphical widgets or data marks [1], [2]. For example, the system XClusim helps biologists to interactively cluster a specified dataset [3], Hypermoval [4] and BEAMES [5] allows interactive construction of regression models, Axissketcher allows dimension reduction using simple drag-drop interactions [6]. Workflow adopted in the system CACTUS. Recently, Das et al. have demonstrated that may result into incorrectly predicting many relevant data a VA system, QUESTO [7] that facilitated interactive creation of instances, though improving the generalizability of the model. Here objective functions to solve a classification task utilising an Auto-the objective to train a model with high accuracy on a set of ML system.

This book chapter describes a novel approach to training machine learning systems by means of a hybrid computer setup i.e. a digital computer tightly coupled with an analog computer. As an example a reinforcement learning system is trained to balance an inverted pendulum which is simulated on an analog computer, thus demonstrating a solution to the major challenge of adequately simulating the environment for reinforcement learning.

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based framework, called SMART, is proposed to model and keep track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I) communication latency across a large geographical area. SMART first formulates the spatio-temporal performance of a vehicular network as a graph in which each vertex corresponds to a subregion consisting of a set of neighboring location points with similar statistical features of V2I latency and each edge represents the spatio-correlation between latency statistics of two connected vertices. Motivated by the observation that the complete temporal and spatial latency performance of a vehicular network can be reconstructed from a limited number of vertices and edge relations, we develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm in order to capture the spatial and temporal statistic of feature map pf latency performance for a large-scale vehicular network. Extensive simulations have been conducted based on a five-month latency measurement study on a commercial LTE network. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and reconstructing the latency performance of large vehicular networks.