Building height estimation is important in many applications such as 3D city reconstruction, urban planning, and navigation. Recently, a new building height estimation method using street scene images and 2D maps was proposed. This method is more scalable than traditional methods that use high-resolution optical data, LiDAR data, or RADAR data which are expensive to obtain. The method needs to detect building rooflines and then compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images in which buildings overlap with each other and the rooflines are difficult to locate. We propose CBHE, a building height estimation algorithm considering both building corners and rooflines. CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters. Then, we use a deep neural network named BuildingNet to classify and filter corner and roofline candidates. Based on the valid corners and rooflines from BuildingNet, CBHE computes building height via the pinhole camera model. Experimental results show that the proposed BuildingNet yields a higher accuracy on building corner and roofline candidate filtering compared with the state-of-the-art open set classifiers. Meanwhile, CBHE outperforms the baseline algorithm by over 10% in building height estimation accuracy.

This paper proposes a novel Stochastic Split Linearized Bregman Iteration ($S^{2}$-LBI) algorithm to efficiently train the deep network. The $S^{2}$-LBI introduces an iterative regularization path with structural sparsity. Our $S^{2}$-LBI combines the computational efficiency of the LBI, and model selection consistency in learning the structural sparsity. The computed solution path intrinsically enables us to enlarge or simplify a network, which theoretically, is benefited from the dynamics property of our $S^{2}$-LBI algorithm. The experimental results validate our $S^{2}$-LBI on MNIST and CIFAR-10 dataset. For example, in MNIST, we can either boost a network with only 1.5K parameters (1 convolutional layer of 5 filters, and 1 FC layer), achieves 98.40\% recognition accuracy; or we simplify $82.5\%$ of parameters in LeNet-5 network, and still achieves the 98.47\% recognition accuracy. In addition, we also have the learning results on ImageNet, which will be added in the next version of our report.

This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset.

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS. To address these issues, a concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of enhanced soft subspace clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to generate the antecedents and various sparse subspace for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules, based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.

Tesla has sent a team to investigate a video on Chinese social media which showed a parked Tesla Model S car exploding, the latest in a string of fire incidents involving the company's cars. The video, time stamped Sunday evening and widely shared on China's Twitter-like Weibo, shows the parked EV emit smoke and burst into flames seconds later. A video purportedly of the aftermath showed a line of three cars completely destroyed. The video comes as Tesla is preparing to unveil its "full self-driving" tech at a conference in Palo Alto, California, on Monday. The video is likely to overshadow the company's unveiling of its latest autonomous driving software and hardware.

Tesla has sent a team of investigators to China after a video emerged appearing to show one of its electric cars bursting into flames. Smoke billows from beneath a parked Model S in Shanghai, before flames appear and the vehicle appears to explode. CCTV footage of the incident was posted on Chinese social media. "After learning about the incident in Shanghai, we immediately sent the team to the scene last night," Tesla said in a statement shared on the social media platform Weibo. "From what we know now, no one was harmed."

Elon Musk has revealed his Neuralink startup is close to announcing the first brain-machine interface to connect humans and computers. The entrepreneur took to Twitter to tell followers the technology would be "coming soon" – though he failed to provide details. Neuralink was set up in 2016 with the ambitious goal of developing hardware to enhance the human brain, however, little about how this will work has been made public. We'll tell you what's true. You can form your own view.

Google is looking to artificial intelligence as a way to make a mark in health care. Google is looking to artificial intelligence as a way to make a mark in health care. One of the biggest corporations on the planet is taking a serious interest in the intersection of artificial intelligence and health. Google and its sister companies, parts of the holding company Alphabet, are making a huge investment in the field, with potentially big implications for everyone who interacts with Google -- which is more than a billion of us. The push into AI and health is a natural evolution for a company that has developed algorithms that reach deep into our lives through the Web.

A reliable and accurate forecasting method for crop yields is very important for the farmer, the economy of a country, and the agricultural stakeholders. However, due to weather extremes and uncertainties as a result of increasing climate change, most crop yield forecasting models are not reliable and accurate. In this paper, a hybrid crop yield probability density forecasting method via quantile regression forest and Epanechnikov kernel function (QRF-SJ) is proposed to capture the uncertainties and extremes of weather in crop yield forecasting. By assigning probability to possible crop yield values, probability density forecast gives a complete description of the yield of crops. A case study using the annual crop yield of groundnut and millet in Ghana is presented to illustrate the efficiency and robustness of the proposed technique. The proposed model is able to capture the nonlinearity between crop yield and the weather variables via random forest. The values of prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals cover the target values with perfect probability. The probability density curves show that QRF-SJ method has a very high ability to forecast quality prediction intervals with a higher coverage probability. The feature importance gave a score of the importance of each weather variable in building the quantile regression forest model. The farmer and other stakeholders are able to realize the specific weather variable that affect the yield of a selected crop through feature importance. The proposed method and its application on crop yield dataset is the first of its kind in literature.

Sparse PCA has shown its effectiveness in high dimensional data analysis, while there is still a gap between the computational method and statistical theory. This paper presents algorithms to solve the row-sparsity constrained PCA, named Feature Sparse PCA (FSPCA), which performs feature selection and PCA simultaneously. Existing techniques to solve the FSPCA problem suffer two main drawbacks: (1) most approaches only solve the leading eigenvector and rely on the deflation technique to estimate the leading m eigenspace, which has feature sparsity inconsistence, identifiability, and orthogonality issues; (2) some approaches are heuristics without convergence guarantee. In this paper, we present convergence guaranteed algorithms to directly estimate the leading m eigenspace. In detail, we show for a low rank covariance matrix, the FSPCA problem can be solved globally (Algorithm 1). Then, we propose an algorithm (Algorithm 2) to solve the FSPCA for general covariance by iteratively building a carefully designed low rank proxy covariance. Theoretical analysis gives the convergence guarantee. Experimental results show the promising performance of the new algorithms compared with the state-of-the-art method on both synthetic and real-world datasets.