Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, deep learning models have drawn increasing attention in healthcare. However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes. In this paper we introduce BERTSurv, a deep learning survival framework which applies Bidirectional Encoder Representations from Transformers (BERT) as a language representation model on unstructured clinical notes, for mortality prediction and survival analysis. We also incorporate clinical measurements in BERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality as a binary outcome (mortality prediction). With partial log-likelihood (PLL) loss, BERTSurv predicts the probability of mortality as a time-to-event outcome (survival analysis). We apply BERTSurv on Medical Information Mart for Intensive Care III (MIMIC III) trauma patient data. For mortality prediction, BERTSurv obtained an area under the curve of receiver operating characteristic curve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of multilayer perceptron (MLP) without notes. For survival analysis, BERTSurv achieved a concordance index (C-index) of 0.7. In addition, visualizations of BERT's attention heads help to extract patterns in clinical notes and improve model interpretability by showing how the model assigns weights to different inputs.

Understanding the information contained in the increasing repository of data is of vital importance to behavior sciences [34], which aim to predict human decision making and enable wide applications, such as mental health evaluation [14], business recommendation [33], opinion mining [54], and entertainment assistance [78]. Analyzing media data on an affective (emotional) level belongs to affective computing, which is defined as "the computing that relates to, arises from, or influences emotions" [38]. The importance of emotions has been emphasized for decades since Minsky introduced the relationship between intelligence and emotion [31]. One famous claim is "The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without emotions." Based on the types of media data, the research on affective computing can be classified into different categories, such as text [13, 72], image [75], speech [45], music [64], facial expression [24], video [56, 79], physiological signals [2], and multi-modal data [52, 41, 80]. The adage "a picture is worth a thousand words" indicates that images can convey rich semantics. Therefore, images are used as an important channel to express emotions. Image emotion analysis (IEA) has recently been paid much attention. As compared to analyzing the images' cognitive aspect that is related with objective content [15], such as object classification and semantic segmentation, IEA focuses on understanding what emotions can be induced by the images in viewers.

Subspace clustering is an important unsupervised clustering approach. It is based on the assumption that the high-dimensional data points are approximately distributed around several low-dimensional linear subspaces. The majority of the prominent subspace clustering algorithms rely on the representation of the data points as linear combinations of other data points, which is known as a self-expressive representation. To overcome the restrictive linearity assumption, numerous nonlinear approaches were proposed to extend successful subspace clustering approaches to data on a union of nonlinear manifolds. In this comparative study, we provide a comprehensive overview of nonlinear subspace clustering approaches proposed in the last decade. We introduce a new taxonomy to classify the state-of-the-art approaches into three categories, namely locality preserving, kernel based, and neural network based. The major representative algorithms within each category are extensively compared on carefully designed synthetic and real-world data sets. The detailed analysis of these approaches unfolds potential research directions and unsolved challenges in this field.

This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts. Two kinds of axiomatic ternary logic, Kleene logic and {\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance (THD) for both the learning/encoding and testing/querying phases. Our work demonstrates that, with an efficient implementation of ternary logic on standard binary machines, the proposed ternary hashing is compared favorably to the binary hashing methods with consistent improvements of retrieval mean average precision (mAP) ranging from 1\% to 5.9\% as shown in CIFAR10, NUS-WIDE and ImageNet100 datasets.

There are numerous examples in machine learning, statistics, mathematics and deep learning, requiring an algorithm to solve some complicated equations: for instance, maximum likelihood estimation (think about logistic regression or the EM algorithm) or gradient methods (think about stochastic or swarm optimization). Here we are dealing with even more difficult problems, where the solution is not a set of optimal parameters (a finite dimensional object), but a function (an infinite dimensional object). The context is discrete, chaotic dynamical systems, with applications to weather forecasting, population growth models, complex econometric systems, image encryption, chemistry (mixtures), physics (how matter reaches an equilibrium temperature), astronomy (how celestial man-made or natural bodies end up having stable or unstable orbits), or stock market prices, to name a few. These are referred to as complex systems. The solutions to the problems discussed here requires numerical methods, as usually no exact solution is known.

Cryptocurrency is gaining more ground each year, which means the space demands an even higher level of understanding for anyone who wants to actually come out ahead. What was once a niche interest for very specific groups of investors will soon be accepted by MasterCard and Tesla, while PayPal started integrating the currency late last year. On top of that, crypto trading is surging in popularity as well, emerging as a niche stock market for people who want to experiment with investing from the comfort of their laptop. If you're new to the world of investing, or if you're a seasoned investor worried about losing your edge, The Quantitative Crypto Trading Strategies Bundle is definitely worth a look at $145. It offers intermediate to advanced training on every aspect of cryptocurrency training, from programming and sorting out risks to the implementation of long-term strategies.

In this article, we will implement a Non-Parametric Learning Algorithm called the Locally Weighted Linear Regression. First, we will look at the difference between the parametric and non-parametric learning algorithms, followed by understanding the weighting Function, predict function, and finally plotting the predictions using Python NumPy and Matplotlib. Parametric -- In a Parametric Algorithm, we have a fixed set of parameters such as theta that we try to find(the optimal value) while training the data. After we have found the optimal values for these parameters, we can put the data aside or erase it from the computer and just use the model with parameters to make predictions. Remember, the model is just a function.

In the early stages of performing data analysis, an important aspect is to get a high level understanding of the multi-dimensional data and find some sort of pattern between the different variables- this is where clustering comes in. This blogpost will focus upon Agglomerative Hierarchical Clustering, its applications and a practical example in R. By now, two questions should arise in your mind. 1) When we say we group the two closest nodes together, how do we define close? And 2) What will be the merging approach to group them? Let's start with a small dataset and understand how Dendrograms are formed in RStudio: I have used normal distribution to compute both x and y coordinates for our dataset and also numbered the datapoints for our understanding. First, we store our x and y datasets as x- and y-coordinates of a dataframe.

Low-code libraries can be used even with minimal experience in coding. This is an open-source machine learning library in Python that allows you to create and deploy machine learning models with minimal coding. Basically, PyCaret is a low-code alternative that can replace hundreds of lines of code with just a few words. It greatly increases the speed of software development and makes it more accessible for beginners. PyCaret is a Python wrapper over several machine learning libraries such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.

Creating AI models is not a walk in the park. So why not get AI to… build AI? Sounds simple, but with the ever-growing variety of models, data scientists first have to have the tools to better automate the model building process. In time series forecasting – models that predict future values of a time series, based on past data or features – the problem is even harder. There are just too many domains that generate time series data, with different and complex modeling approaches. We think we can help.