Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively.

A random dot product graph (RDPG) is a generative model for networks in which vertices correspond to positions in a latent Euclidean space and edge probabilities are determined by the dot products of the latent positions. We consider RDPGs for which the latent positions are randomly sampled from an unknown $1$-dimensional submanifold of the latent space. In principle, restricted inference, i.e., procedures that exploit the structure of the submanifold, should be more effective than unrestricted inference; however, it is not clear how to conduct restricted inference when the submanifold is unknown. We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference. To illustrate, we test a hypothesis about the Fr\'{e}chet mean of a small community of vertices, using the complete set of vertices to infer latent structure. We propose test statistics that deploy the Isomap procedure for manifold learning, using shortest path distances on neighborhood graphs constructed from estimated latent positions to estimate arc lengths on the unknown $1$-dimensional submanifold. Unlike conventional applications of Isomap, the estimated latent positions do not lie on the submanifold of interest. We extend existing convergence results for Isomap to this setting and use them to demonstrate that, as the number of auxiliary vertices increases, the power of our test converges to the power of the corresponding test when the submanifold is known.

in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R., 1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were widely translated to dozens of languages, provide rich materials for analyzing languages differences from several perspectives and within a number of disciplines like linguistics, philosophy and cognitive science. Taking motion events conceptualization as a case study, this paper, focus on the morphologic, syntactic, and semantic annotation process of English-Arabic aligned corpus created from a digitized novels, in order to re-examine the linguistic encodings of motion events in English and Arabic in terms of Frame Semantics. The present study argues that differences in motion events conceptualization across languages can be described with frame structure and frame-to-frame relations.

The partnership launched Thursday after a virtual meeting between national technology ministers. It was nearly two years after the leaders of Canada and France announced they were forming a group to guide the responsible adoption of AI based on shared principles of "human rights, inclusion, diversity, innovation and economic growth."

Zeynep Temel, a robotics researcher who uses inspiration from nature to design novel means of motion and locomotion for tiny robots, has been named by the World Economic Forum to its Young Scientists Class of 2020. Temel, an assistant professor in the Robotics Institute, and Stephanie Sydlik, an assistant professor of chemistry, are the latest Carnegie Mellon University faculty members to join the WEF's Young Scientists community. The distinction recognizes scientific rising stars under the age of 40 who are pursuing high-impact research. "I am very excited to be a part of the WEF Young Scientists community and incredibly honored to be representing CMU," Temel said. "It will be a great adventure to learn from amazing scientists and develop projects that will improve the state of the world.

Payments solutions provider Vesta today announced that it raised $125 million in capital, bringing its total raised to over $145 million. The company says it will use the financing to grow and accelerate the deployment of its fraud protection and ecommerce payment products. Payment fraud is pervasive -- in 2018, $24.26 billion was lost due to credit card fraud worldwide, reports Shift Processing. That same year, the rate of card fraud increased by nearly 20% as the U.S. took the lead in reported losses. Vesta says its AI-powered decisioning platform helps clients to assess the risk of this fraud and ultimately to prevent fraud from occurring, with connectors that tie into existing software from vendors including Magento, Shopify, WooCommerce, BigCommerce, and SAP Commerce Cloud.

The work of an Italian mathematician in the 1930s may hold the key to epidemic modeling. That's because models that try to replicate reality in all its detail have proven hard to steer during this crisis, leading to poor predictions despite noble and urgent efforts to recalibrate them. On the other hand overly stylized compartmental models have run headlong into paradoxes such as Sweden's herd immunity. This approach is represented in the picture above. The important thing to note is that we are not attempting to find a model that is close to the truth, only close to the orbit. This will make a lot more sense after Section 1, I promise.

As scientists and researchers strive harder to make Artificial Intelligence (AI) mainstream, this ingenious technology is already making its way to our day to day lives and continues ushering across several industry verticals. From voice-powered personal assistants like Siri and Alexa to autonomously-powered self-driving vehicles, AI has been rearing itself as a force to be reckoned with. Many tech giants such as Apple, Google, Facebook, and Microsoft have been making huge bets on the long-term growth potential of Artificial Intelligence. According to a report published by the research firm Markets and Markets, the AI market is expected to grow to a $190 billion industry by 2025. More and more businesses are looking to boost their ROI by leveraging the capabilities of AI.

Customer's expectations are the guard rails that guide how their relationships progress with any business. The pandemic has made the predictable unpredictable, erasing marketing personas of the past and re-writing them in real-time. Old guard rails and expectations are changing fast. Having an accurate outside-in view from the customer's perspective is the value VoC programs deliver, with the best ones providing data to guide strategy. Pure e-commerce orders have grown 110% since January, and e-commerce revenue has increased by 96%.

When Kumesh Aroomoogan was working in the public finance department at Citigroup, he spent hours looking at financial statements, copying and pasting from one document to the next, arranging and rearranging his Excel sheet, and performing a lot of repeated manual tasks. "They were scaling on labor versus technology," Aroomoogan says. "I thought there has to be a better way to automate this entire process." So in 2014, Aroomoogan, who had an idea for a product, teamed up with Anshul Vikram Pandey, a data visualization PhD student at NYU at the time, and the two started working in Aroomoogan's basement in Queens, NY. What came out of the effort of the two entrepreneurs is Accern, an enterprise whose AI Platform contains ready-made solutions for the financial service industry.