Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear manifold matching algorithm using shortest-path distance and joint neighborhood selection. Specifically, a joint nearest-neighbor graph is built for all modalities. Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space. Compared to existing algorithms, our approach exhibits superior performance for matching disparate datasets of multiple modalities.

Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the physicians practice variation and reduce medical costs and errors. In this paper, our main motivation is to develop an effective intelligent medical decision support system based on data mining techniques. In this context, five data mining classifying algorithms, with large datasets, have been utilized to assess and analyze the risk factors statistically related to heart diseases in order to compare the performance of the implemented classifiers (e.g., Na\"ive Bayes, Decision Tree, Discriminant, Random Forest, and Support Vector Machine). To underscore the practical viability of our approach, the selected classifiers have been implemented using MATLAB tool with two datasets. Results of the conducted experiments showed that all classification algorithms are predictive and can give relatively correct answer. However, the decision tree outperforms other classifiers with an accuracy rate of 99.0% followed by Random forest. That is the case because both of them have relatively same mechanism but the Random forest can build ensemble of decision tree. Although ensemble learning has been proved to produce superior results, but in our case the decision tree has outperformed its ensemble version.

The first thought is about open sourcing technologies. I have already written on this trend, which is quite unusual at a first look if you think about it, but my thinking around open source has been highly stimulated by the talk given by Wes McKinney -- for who doesn't know who he is, well, he is definitely not a random guy but is THE open source guy (creator of pandas and author of Python for Data Analysis). The open source model is quite hard to be reconciled with the traditional SaaS model, especially in the financial sector. However, we are observing many firms providing cutting-edge technologies and algorithms for free. While in some cases there is a specific business motivation behind it (e.g., Google releasing Tensorflow to avoid conflict of interests with their cloud offering), the decision of open sourcing (part of) the technology actually represents an emerging trend.

Growth is shifting, disruption is accelerating, and societal tensions are rising. Confronting these dynamics will help you craft a better strategy, and forge a brighter future. "The trend is your friend." It's the oldest adage in investing, and it applies to corporate performance, too. We've found through our work on the empirics of strategy that capturing tailwinds created by industry and geographic trends is a pivotal contributor to business results: a company benefiting from such tailwinds is four to eight times more likely to rise to the top of the economic-profit performance charts than one that is facing headwinds. It's easy, however, to lose sight of long-term trends amid short-term gyrations, and there are moments when the nature and direction of those trends become less clear. Today, for example, technology is delivering astounding advances, and more people are healthy, reading, and entering the global middle class than at any period in human history. At the same time, the post–Cold War narrative of progress fueled by competitive markets, globalization, and innovation has lost some luster. Those contradictions are showing up in politics, and the long-term trends underlying them are reshaping the business environment.

University teaching is under the microscope as institutions brace themselves for the first Teaching Excellence Framework, which will accord them gold, silver and bronze status. The biggest developments in university teaching are being driven by technology. The old techniques of talk and chalk are being challenged by lecture capture, flipped learning and decision-making based on data analysis. But technology can have worrying consequences. One (unnamed) university was recently brought under attack by its smart devices – a network including vending machines and light sensors was hacked, wreaking havoc with internet speeds across campus. And then there are the concerns about privacy raised by such developments.

Most customers are confused about the use of artificial intelligence (AI) and are, therefore, reluctant to embrace this new technology, a study said. Released by US-based software firm Pegasystems, it revealed that these fears are often eased once the users gain firsthand AI experience -- which ironically many enjoy without even realizing it. "Our study suggests the recent hype is causing some confusion and fear among consumers, who may not really understand how it's already being used and helping them every day," said Don Schuerman, Vice President (Product Marketing) Pegasystems. The study that involved 6,000 customers in six countries found that consumers were hesitant to fully embrace AI devices and services. "Only 36 percent are comfortable with businesses using AI to engage with them. Almost 72 percent express some sort of fear about AI," the study found.

Google's DeepMind announced today that it was open sourcing Sonnet, its object-oriented neural network library. Sonnet doesn't replace TensorFlow, it's simply a higher-level library that meshes well with DeepMind's internal best-practices for research. Specifically, DeepMind says in its blog post that the library is optimized to make it easier to switch between different models when conducting experiments so that engineers don't have to upend their entire projects. To this avail, the team made changes to TensorFlow to make it easier to consider models as hierarchies. DeepMind also added transparency to variable sharing. It's in DeepMind's own interest to open source Sonnet.

Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data.

The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite, number of loss functions. In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space. To this end, we show the developments on the Grassmann manifold. The key challenges of averaging, addition, and subtraction of multiple gradients are addressed with notions like logarithm mapping and parallel translation of vectors on the Grassmann manifold. We present a global convergence analysis of the proposed algorithm with decay step-sizes and a local convergence rate analysis under fixed step-size with some natural assumptions. The proposed algorithm is applied on a number of problems on the Grassmann manifold like principal components analysis, low-rank matrix completion, and the Karcher mean computation. In all these cases, the proposed algorithm outperforms the standard Riemannian stochastic gradient descent algorithm.