Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

ABSTRACT High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection which combines autoencoder regression and group lasso tasks. Compared to traditional feature selection methods, AEFS can select the most important features by excavating both linear and nonlinear information among features, which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions. Experimental results on benchmark dataset show that the proposed method is superior to the state-of-the-art method.

KILIS, TURKEY – Turkish troops and allied Syrian fighters captured a strategic hill in northwestern Syria on Sunday as their offensive to root out Kurdish fighters entered a second week. Associated Press reporters in the Turkish border town of Kilis heard constant shelling and clashes as Turkish aircraft flew overhead and plumes of smoke rose in the distance. The Turkey-backed forces have been trying to capture the hill, which separates the Kurdish-held enclave of Afrin from the Turkey-controlled town of Azaz, since the start of their offensive on Jan. 20, but have been met with stiff resistance. The Kurdish militia known as the People's Defense Units, or YPG, said Turkey sent reinforcements to the area following intense airstrikes on Sunday. The Turkish military said in a statement that its soldiers and allied Syrian opposition fighters captured Bursayah hill assisted by airstrikes, attack helicopters, armed drones and howitzers.

YOKOHAMA – The Kanagawa Prefectural Police plan to become the first in the nation to introduce predictive policing, a method of anticipating crimes and accidents using artificial intelligence, sources said Sunday. The Kanagawa police will seek research expenses under the prefecture's budget for fiscal 2018 starting April, hoping to put a predictive policing system in place on a trial basis before the 2020 Tokyo Olympics, prefectural government sources said. A system that can determine whether a single perpetrator is behind several crimes, predict an offender's next move and detect where and when crimes or accidents are likely to occur would help police officers investigate crimes and prevent some from happening, they said. It would allow them to patrol the suggested places at the most likely times to ensure safety and would also help speed up probes, the sources said. The AI-based system would employ a "deep learning" algorithm that allows the computer to teach itself by analyzing big data.

DNA rearrangement processes recombine gene segments that are organized on the chromosome in a variety of ways. The segments can overlap, interleave or one may be a subsegment of another. We use directed graphs to represent segment organizations on a given locus where contigs containing rearranged segments represent vertices and the edges correspond to the segment relationships. Using graph properties we associate a point in a higher dimensional Euclidean space to each graph such that cluster formations and analysis can be performed with methods from topological data analysis. The method is applied to a recently sequenced model organism \textit{Oxytricha trifallax}, a species of ciliate with highly scrambled genome that undergoes massive rearrangement process after conjugation. The analysis shows some emerging star-like graph structures indicating that segments of a single gene can interleave, or even contain all of the segments from fifteen or more other genes in between its segments. We also observe that as many as six genes can have their segments mutually interleaving or overlapping.

A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based Intree structure proves able to reveal the clustering structure underlying the dataset, except one imperfect place, that is, there are some undesired edges in this In-tree which require to be removed. Here, we propose an effective way to automatically remove the undesired edges in In-tree via an effective combination of the In-tree structure with affinity propagation (AP). The key for the combination is to add edges between the reachable nodes in In-tree before using AP to remove the undesired edges. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed method.

Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, operations research, statistics, and computer science. This paper provides an integrative review at the boundary of these three areas. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern large and complex "big data" sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques.

As part of an ongoing series about artificial intelligence, the Asahi Shimbun on Jan. 11 published a story that asserts the financial industry has adopted AI more readily than any other. Because finance is a data-driven endeavor, designing AI software to do things such as read and analyze reams of economic information and project future investment performance is progressing rapidly. Moreover, voice recognition is becoming so advanced that standing clients and potential customers will no longer have to talk to humans about their financial needs. Even if a person is going to make the final decision about a transaction, most of the work has already been done. This development, however, may also hasten the end of banks.

I think it's safe to assume that Shogo Shimada, a thoracic and cardiac surgeon at the University of Tokyo Hospital, did not start his career thinking he might one day be implanting robots into living animals. Yet that is just what Shimada and a team of surgeons and roboticists from around the world have now achieved. Their work is the latest in a long line of attempts to solve problems in biology using robots. When I hear about this sort of research, it usually involves micro-robots. And then there's talk of smaller, nano-size robots that can swim through the bloodstream and deliver drugs or make repairs, as referenced in the 1966 sci-fi film "Fantastic Voyage."

Theoretical physics now firmly resides within an Age wherein new physics, new mathematics and new data coexist in a symbiosis which transcends interdisciplinary boundaries and wherein concepts and developments in one field are evermore rapidly enriching another. String theory has spearheaded this vision for the past few decades and has, perhaps consequently, become a paragon of the theoretical sciences. That she engenders the cross-fertilization between physics and mathematics is without dispute: interactions on an unprecedented scale have commingled fields as diverse as quantum field theory, general relativity, condensed matter physics, algebraic and differential geometry, number theory, representation theory, category theory, etc. With the advent of increasingly powerful computers, from this fruitful dialogue has also arisen a plethora of data, ripe for mathematical experimentation. This emergence of data in some sense began with the incipience of string phenomenology [1] where compactification of the heterotic string on Calabi-Yau threefolds (CY3) was widely believed to hold the ultimate geometric unification.