Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.

Heterosis is the improved or increased function of any biological quality in a hybrid offspring. We have studied yet the largest maize SNP dataset for traits prediction. We develop linear and non-linear models which consider relationships between different hybrids as well as other effect. Specially designed model proved to be efficient and robust in prediction maize's traits.

Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and independently distributed (i.i.d). However, in many real-world applications, sequences are naturally linked. For example, web documents are connected by hyperlinks; and genes interact with each other. On the one hand, linked sequences are inherently not i.i.d., which poses tremendous challenges to existing RNN models. On the other hand, linked sequences offer link information in addition to the sequential information, which enables unprecedented opportunities to build advanced RNN models. In this paper, we study the problem of RNN for linked sequences. In particular, we introduce a principled approach to capture link information and propose a linked Recurrent Neural Network (LinkedRNN), which models sequential and link information coherently. We conduct experiments on real-world datasets from multiple domains and the experimental results validate the effectiveness of the proposed framework.

Abstract--Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism. Deploying a number of (video) cameras is a common surveillance approach. Given the never-ending power offered by vehicles to metropolises, exploiting vehicle traffic to design camera placement strategies could potentially facilitate security surveillance. This article constitutes the first effort toward building the linkage between vehicle traffic and security surveillance, which is a critical problem for smart cities. We expect our study could influence the decision making of surveillance camera placement, and foster more research of principled ways of security surveillance beneficial to our physical-world life. Security surveillance is one of the most important issues in smart cities. Due to the continuous growth of cities in size and complexity, keeping cities safe becomes critical to attracting skilled people and investments necessary for economic growth and development. Compounded by terrorism, cities, especially metropolises, have to carefully conduct security surveillance. To fight against the adversary, deploying a number of (video) cameras is a common surveillance approach, which has gained prominence in policy proposals on combating terrorism [1].

Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscript, we develop a novel method, transfer latent representation (TLR), to learn a better latent space. Specifically, we design an objective function based on a simple linear autoencoder to derive the latent representations of both domains. The encoder in the autoencoder aims to project the data of both domains into a robust latent space. Besides, the decoder imposes an additional constraint to reconstruct the original data, which can preserve the common properties of both domains and reduce the noise that causes domain shift. Experiments on cross-domain tasks demonstrate the advantages of TLR over competing methods.

Only a handful of countries and companies have any real hope of winning the race for artificial intelligence supremacy. China, Germany, Japan, Russia, South Korea, and the US are among the national contenders and primarily Chinese and American companies lead the pack of commercial contenders, including Alibaba, Baidu, Tencent, Amazon, Facebook, and Google. What distinguishes all of them is the resources they have already devoted and the achievements they have already made in the AI arena. They are poised to leap further and further ahead of those who are lagging behind or have not yet even entered the race. While Germany, Japan, and South Korea are focused primarily on commercial applications, Russia excels in military applications, and the US maintains its general lead (for the time being) in the space, but only China and these leading Chinese companies have positioned themselves to sprint ahead of all the others in the coming decade.

In May, SenseTime Group, a Chinese technology company specialising in artificial intelligence (AI) and facial recognition, raised $620 million in a funding round led by Fidelity International, Hopu Capital, Tiger Global Management and Silver Lake Partners. The three-year-old startup, arguably the world's most valuable startup in AI, was valued at $4.5 billion. The funding came at a time when the Chinese government is pulling all the stops to make the country a global leader in AI over the next seven years. By 2025, the Chinese government intends to grow AI industry's value over $60 billion. In India, the picture is different.

AI and Big Data allow innovators to leverage neuroscientific knowledge at scale to untapped markets. We would all like our brains to be more powerful – especially those of us with reason to fear the effects of advancing age or a degenerative illness. For the entrepreneurs behind widely promoted "brain-training" apps like Lumosity, the universal desire for cognitive improvement translates to lucrative opportunities. Some scientists and clinicians, too, have hopped on the bandwagon, seeking avenues to lend their expertise to the business world while theoretically bringing cognitive enhancement to the masses. It seems like a win-win for all – except, in Lumosity's case, results have fallen short of the formidable hype.

So many businesses and governments are scurrying to get into the artificial intelligence race that many appear to be losing sight of some important things that should matter along the way – such as legality, good governance, and ethics. In the AI arena the stakes are extremely high and it is quickly becoming a free-for-all from data acquisition to the stealing of corporate and state secrets. The "rules of the road" are either being addressed along the way or not at all, since the legal regime governing who can do what to whom, and how, is either wholly inadequate or simply does not exist. As is the case in the cyber world, the law is well behind the curve. Ethical questions abound with AI systems, raising questions about how machines recognize and process values and ethical paradigms.

Cape Town - The use of Artificial Intelligence (AI) offers exciting opportunities for the retail industry, according to Sven Schoof, head of customer experience at ecommerce fashion website Spree. "The big question is whether AI will become a super convenient feature where we are all lying on the beach while machines do everything for us; or whether machines are taking over and we cannot find the off-switch and it is'game over' for us?" Schoof said at the DHL eCommerce Money Africa conference in Cape Town on Wednesday. "There is currently a lot of debate about whether AI is taking us to the next level or not. Some people are strong opponents, fearing that machines will'take over', while other people are in favour of the use of AI. The best approach is probably somewhere in between."