In this paper, we propose a novel multi-task learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multi-task learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty, so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark data sets of multiple face attribute prediction, multi-task natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multi-task problems.

Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of symmetric weights and neurons, as well as jointly supervised and unsupervised learning. However, its advantages were only demonstrated in a one-hidden-layer implementation due to the lack of computing resources and big data at that time. In this paper, we revisit Lmser from the perspective of deep learning, develop Lmser network based on multiple convolutional layers, which is more suitable for image-related tasks, and confirm several Lmser functions with preliminary demonstrations on image recognition, reconstruction, association recall, and so on. Experiments demonstrate that Lmser indeed works as indicated in the original paper, and it has promising performance in various applications.

Zero-shot learning (ZSL) aims at recognizing unseen classes with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space (FS) shared by both seen and unseen classes, i.e., attributes or word vectors, as the bridge. However, due to the mutually disjoint of training (seen) and testing (unseen) data, existing ZSL methods easily and commonly suffer from the domain shift problem. To address this issue, we propose a novel model called AMS-SFE. It considers the Alignment of Manifold Structures by Semantic Feature Expansion. Specifically, we build up an autoencoder based model to expand the semantic features and joint with an alignment to an embedded manifold extracted from the visual FS of data. It is the first attempt to align these two FSs by way of expanding semantic features. Extensive experiments show the remarkable performance improvement of our model compared with other existing methods.

Forecasting the future traffic flow distribution in an area is an importance issue for traffic management in an intelligent transportation system. The key challenge of traffic prediction is to capture spatial and temporal relations between future traffic flows and historical traffic due to highly dynamical patterns of human activities. Most existing methods explore such relations by fusing spatial and temporal features extracted from multi-source data. However, they neglect position information which helps distinguish patterns on different positions. In this paper, we propose a position-aware neural network that integrates data features and position information. Our approach employs the inception backbone network to capture rich features of traffic distribution on the whole area. The novelty lies in that under the backbone network, we apply position embedding technique used in neural language processing to represent position information as embedding vectors which are learned during the training. With these embedding vectors, we design positionaware convolution which allows different kernels to process features of different positions. Extensive experiments on two real-world datasets show that our approach outperforms previous methods even with fewer data sources.

The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms. This paper proposes a reference vector based algorithm which uses two interacting engines to adapt the reference vectors and to evolve the population towards the true Pareto Front (PF) s.t. the reference vectors are always evenly distributed within the current PF to provide appropriate guidance for selection. The current PF is tracked by maintaining an archive of undominated individuals, and adaptation of reference vectors is conducted with the help of another archive that contains layers of reference vectors corresponding to different density. Experimental results show the expected characteristics and competitive performance of the proposed algorithm TEEA.

Amazon's Echo speakers have a broadcast feature that will help you send a message to family members that might be scattered around the house. If you have an Amazon Echo product, you aren't the only person privy to your private conversations. Thousands of people across the globe are employed by Amazon.com to listen to Echo recordings, transcribe and annotate them and feed them back to the software so that Alexa can better grasp human speech, according to a report from Bloomberg. The employees – ranging from Boston to India – signed nondisclosure agreements barring them to speak publicly about the program. According to Bloomberg, they work nine hours per day, with each reviewer going through as many as 1,000 audio clips per shift.

When Amazon customers speak to Alexa, the company's AI-powered voice assistant, they may be heard by more people than they expect, according to a report. Amazon employees around the world regularly listen to recordings from the company's smart speakers as part of the development process for new services, Bloomberg News reports. Some transcribe artist names, linking them to specific musicians in the company's database; others listen to the entire recorded command, comparing it with what the automated systems heard and the response they offered, in order to check the quality of the company's software. Technically, users have given permission for the human verification: the company makes clear that it uses data "to train our speech recognition and natural language understanding systems", and gives users the chance to opt out. But the company doesn't explicitly say that the training will involve workers in America, India, Costa Rica, and more nations around the world listening to those recordings.

Golf fans who are planning to watch the Masters this weekend will have yet more ways to check out the action. For the first time at a golf tournament, practically every one of the more than 20,000 shots from the first major of the year will be available to view on the Masters website and app within five minutes of a player striking the ball. While these videos won't be live, you'll essentially be able to watch full rounds from the likes of Tiger Woods, Rory McIlroy and Jordan Speith without such trivial matters as watching them walk between shots. There is a caveat in that cameras might not capture shots in some instances, such as those from unusual lies, or if a group's tee shots end up in wildly different spots. The Masters attracts sports aficionados who might not typically watch golf as well as devotees, so it's a high-profile way to debut this technology after a few years of development. It should be especially useful over the first two days when the field is at its most expansive, and a player might be unexpectedly putting together a killer round and rampaging up the leaderboard when they aren't a focus of the TV broadcast.

PlayStation players can finally change their PSN names, in a change set to delight everyone suffering with names they're ashamed of. But it comes with two major catches: the name can only be changed for free once, and switching it up might cause problems for some games. Many people are still left with names they set years ago, which can often become embarrassing with time. As such, it has become one of the PlayStation's most requested features and people have become more and more frustrated with time. We'll tell you what's true. You can form your own view.

Tens of millions of people use smart speakers and their voice software to play games, find music or trawl for trivia. Millions more are reluctant to invite the devices and their powerful microphones into their homes out of concern that someone might be listening. Inc. employs thousands of people around the world to help improve the Alexa digital assistant powering its line of Echo speakers. The team listens to voice recordings captured in Echo owners' homes and offices. The recordings are transcribed, annotated and then fed back into the software as part of an effort to eliminate gaps in Alexa's understanding of human speech and help it better respond to commands.