We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging. Our algorithm is trained with approximately 30,000 images from 250 patients (50 patients with normal pancreas and 200 patients with abnormal pancreas findings) based on public data sources. A test accuracy of 94.6 percents was achieved in the out-of-sample diagnosis classification based on a total of approximately 15,000 images from 113 patients, whereby 26 out of 32 patients with normal pancreas and all 81 patients with abnormal pancreas findings were correctly diagnosed. SML is able to automatically choose fundamental images (on average 5 or 9 images for each patient) in the diagnosis classification and achieve the above mentioned accuracy. The computational time is 75 seconds for diagnosing 113 patients in a laptop with standard CPU running environment. Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest. Our methodology showcases practical utility and improved accuracy of image diagnosis in pancreatic mass screening in the era of AI.

Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we develop a surrogate assisted semi-supervised-learning (SAS) approach to risk modeling with high dimensional predictors, leveraging a large unlabeled data on candidate predictors and surrogates of outcome, as well as a small labeled data with annotated outcomes. The SAS procedure borrows information from surrogates along with candidate predictors to impute the unobserved outcomes via a sparse working imputation model with moment conditions to achieve robustness against mis-specification in the imputation model and a one-step bias correction to enable interval estimation for the predicted risk. We demonstrate that the SAS procedure provides valid inference for the predicted risk derived from a high dimensional working model, even when the underlying risk prediction model is dense and the risk model is mis-specified. We present an extensive simulation study to demonstrate the superiority of our SSL approach compared to existing supervised methods. We apply the method to derive genetic risk prediction of type-2 diabetes mellitus using a EHR biobank cohort.

Interviews are the most challenging part of getting any job especially for Data Scientist and Machine Learning Engineer roles where you are tested on Machine Learning and Deep Learning concepts. So, Given below is a short quiz that consists of 25 Questions consisting of MCQs(One or more correct), True-False, and Integer Type Questions to check your knowledge. Explanation: The derivative of Leaky RELU activation function h(z) is 1 only for z 0, while for z 0, it has a very small value. Explanation: Residuals are vertical offset and the sum of residuals is always zero. Explanation: For deciding class w1, the conditional Risk for w1 is smaller than w2.

Recent studies have demonstrated that deep learning models can discriminate based on protected classes like race and gender. In this work, we evaluate bias present in deepfake datasets and detection models across protected subgroups. Using facial datasets balanced by race and gender, we examine three popular deepfake detectors and find large disparities in predictive performances across races, with up to 10.7% difference in error rate between subgroups. A closer look reveals that the widely used FaceForensics++ dataset is overwhelmingly composed of Caucasian subjects, with the majority being female Caucasians. Our investigation of the racial distribution of deepfakes reveals that the methods used to create deepfakes as positive training signals tend to produce "irregular" faces - when a person's face is swapped onto another person of a different race or gender. This causes detectors to learn spurious correlations between the foreground faces and fakeness. Moreover, when detectors are trained with the Blended Image (BI) dataset from Face X-Rays, we find that those detectors develop systematic discrimination towards certain racial subgroups, primarily female Asians.

Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches.

Objective: Accurate evaluation of the root canal filling result in X-ray image is a significant step for the root canal therapy, which is based on the relative position between the apical area boundary of tooth root and the top of filled gutta-percha in root canal as well as the shape of the tooth root and so on to classify the result as correct-filling, under-filling or over-filling. Methods: We propose a novel anatomy-guided Transformer diagnosis network. For obtaining accurate anatomy-guided features, a polynomial curve fitting segmentation is proposed to segment the fuzzy boundary. And a Parallel Bottleneck Transformer network (PBT-Net) is introduced as the classification network for the final evaluation. Results, and conclusion: Our numerical experiments show that our anatomy-guided PBT-Net improves the accuracy from 40\% to 85\% relative to the baseline classification network. Comparing with the SOTA segmentation network indicates that the ASD is significantly reduced by 30.3\% through our fitting segmentation. Significance: Polynomial curve fitting segmentation has a great segmentation effect for extremely fuzzy boundaries. The prior knowledge guided classification network is suitable for the evaluation of root canal therapy greatly. And the new proposed Parallel Bottleneck Transformer for realizing self-attention is general in design, facilitating a broad use in most backbone networks.

Q2: What is the difference between supervised and unsupervised machine learning? Q3: How is KNN different from k-means clustering? Q5: Define precision and recall. This course will help you to answer some of the questions asked in Interviews related to Machine Learning. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is a method of data analysis that automates analytical model building.

The Semantic Web emerged as an extension to the traditional Web, towards adding meaning to a distributed Web of structured and linked data. At its core, the concept of ontology provides the means to semantically describe and structure information and data and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine learning techniques, able to extract knowledge from information and data sources and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction, from various sources such as text, images, databases and human expertise, with emphasis on the task of feature selection. First, some of the most common classification and feature selection algorithms are briefly presented. Then, selected methodologies, which utilize ontologies to represent features and perform feature selection and classification, are described. The presented examples span diverse application domains, e.g., medicine, tourism, mechanical and civil engineering, and demonstrate the feasibility and applicability of such methods.

In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a small trigger pattern. Model compression is a widely-used approach for reducing the size of deep learning models without much accuracy loss, enabling resource-hungry models to be compressed for use on resource-constrained devices. In this paper, we study the risk that model compression could provide an opportunity for adversaries to inject stealthy backdoors. We design stealthy backdoor attacks such that the full-sized model released by adversaries appears to be free from backdoors (even when tested using state-of-the-art techniques), but when the model is compressed it exhibits highly effective backdoors. We show this can be done for two common model compression techniques -- model pruning and model quantization. Our findings demonstrate how an adversary may be able to hide a backdoor as a compression artifact, and show the importance of performing security tests on the models that will actually be deployed not their precompressed version.

Gaussian processes (GPs) are non-linear probabilistic models popular in many applications. However, na\"ive GP realizations require quadratic memory to store the covariance matrix and cubic computation to perform inference or evaluate the likelihood function. These bottlenecks have driven much investment in the development of approximate GP alternatives that scale to the large data sizes common in modern data-driven applications. We present in this manuscript MuyGPs, a novel efficient GP hyperparameter estimation method. MuyGPs builds upon prior methods that take advantage of the nearest neighbors structure of the data, and uses leave-one-out cross-validation to optimize covariance (kernel) hyperparameters without realizing a possibly expensive likelihood. We describe our model and methods in detail, and compare our implementations against the state-of-the-art competitors in a benchmark spatial statistics problem. We show that our method outperforms all known competitors both in terms of time-to-solution and the root mean squared error of the predictions.