DERA ISMAIL KHAN, PAKISTAN – Pakistani intelligence officials say suspected U.S. missiles have struck a home in the Kurram tribal region, killing 20 militants. Two intelligence officials said missiles fired from a suspected U.S. drone hit a compound in the Mukbal area near the Afghan border Monday evening. They added that it was being used by militants from the Haqqani network and that one of their top commanders, Sangeen Wali, was killed. They spoke on condition of anonymity because they were not authorized to brief media. The strike comes a day after roadside bombs killed four security troops engaged in a search operation for militants in Kurram.

A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations.

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the model during training. The proposed method can be considered an unusual form of regularization: the model parameters are obtained by training a relatively low-capacity model, that is relatively easy to optimize at the beginning, with only a few iterations, and these parameters are reused for the initialization of a higher-capacity model. We derive the upper and lower bounds of variance of the weight variation, and show that the initial symmetric structure of NGs helps stabilize training. We evaluate the proposed method on different frameworks of convolutional neural networks over two object recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results showed that the proposed method allows us to (1) speed up the convergence of training, (2) allow for less careful weight initialization, (3) improve or at least maintain the performance of the model at negligible extra computational cost, and (4) easily train a very deep model.

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties for the proposed index and show how to optimally choose the parameters of the proposed criterion, investigating the Restricted Isometry (RIP) and error-sensitivity properties. We also propose an iterative sparse recovery method based on a constrained $l_1$-norm minimization problem, incorporating CSIM as the fidelity criterion. The resulting convex optimization problem is solved via an algorithm based on Alternating Direction Method of Multipliers (ADMM). Taking advantage of the convexity of the CSIM index, we also prove the convergence of the algorithm to the globally optimal solution of the proposed optimization problem, starting from any arbitrary point. Simulation results confirm the performance of the new similarity index as well as the proposed algorithm for missing sample recovery of image patch signals.

Of all the legged robots built in labs all over the world, few inspire more awe and reverence than Boston Dynamics' quadrupeds. Chinese roboticist Xing Wang has long been a fan of BigDog, AlphaDog, Spot, SpotMini, and other robots that Boston Dynamics has famously introduced over the years. "Marc Raibert … is my idol," Wang once told us about the founder and president of Boston Dynamics. Now Wang, with funding from a Chinese angel investor, has founded his own robotics company, called Unitree Robotics and based in Hangzhou, outside Shanghai. Wang says his plan is making legged robots as popular and affordable as smartphones and drones. Unitree's first robot is a four-legged robodog called Laikago, which the company is announcing this week.

As IoT technology continues to make our lives more comfortable through greater intelligence enabled by networking smart devices, it becomes increasingly important to protect the information stored and transmitted by these devices. Embedded MCUs are at the core of IoT-based products, and selecting the right MCU is key to meeting the present and future needs of your customers. An MCU designed for IoT applications needs to have sufficient processing capabilities, hardware-based security, and software algorithms to provide a safe and secure solution. Secure MCUs should offer multiple levels of security elements to support various security algorithms like Advanced Encryption Standard (AES), Data Encryption Standard (DES), and Secure Hash Algorithm (SHA). The MCU needs to provide a complete chain of security, secure boot process, hardware-based root of trust, true random number generation functionality in hardware, and user application code authentication, among other capabilities.

World petrol demand will peak within 13 years thanks to the impact of electric cars and more efficient engines, energy experts have predicted. UK-based Wood Mackenzie said it expected the take-up of electric vehicles to cut gasoline demand significantly, particularly beyond 2025 as the battery-powered cars go mainstream. Combined with car manufacturers forced by regulations to produce models that run further on the same amount of oil, a new report by the analysts suggests global gasoline demand is likely to peak by 2030. The UK and France have recently said they will phase out sales of new petrol and diesel cars by 2040. China, the world's biggest car market, is mulling a similar move, which would have a significant impact on oil demand.

At first glance, you might mistake this enormous robot for a character from the latest Transformers blockbuster. But, the 16 foot (five meter) tall machine is an American robot called Eagle Prime that is now ready to take on Japan's Kuratas robot in the Megabots Giant Robot Dual league. An incredible video shows the Eagle Prime in action ahead of the dual - which will be the world's first giant robot battle - taking place tomorrow, Tuesday, October 17th, 2017 at 7pm PST (10pm EST). At first glance, you might mistake this enormous robot for a character from the latest Transformers blockbuster. A dual between an American robot called Eagle Prime and Japanese robot Kuratas will take place tomorrow, Tuesday October 17th.

As an Indian guy living in the US, I have a constant flow of money from home to me and vice versa. If the USD is stronger in the market, then the Indian rupee (INR) goes down, hence, a person from India buys a dollar for more rupees. If the dollar is weaker, you spend less rupees to buy the same dollar. If one can predict how much a dollar will cost tomorrow, then this can guide one's decision making and can be very important in minimizing risks and maximizing returns. Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

ARTIFICIAL INTELLIGENCE could be making an appearance in your daily commute, as evidenced by emerging applications for the technology in urban environments such the latest development in Alibaba's "City Brain" initiative. Zheng Yijiong became the first ever traffic officer to blend the artificial intelligence with good old human intuition in order to improve and regulate traffic condition throughout the city, according to China Plus. Working in Hangzhou, the birthplace of Alibaba in the eastern Chinese Zhejiang Province, Zheng became a test case in the Hangzhou government's efforts to boost itself to smart city status. Zheng was trained for two months in the "City Brain" project's traffic initiatives which is aimed at bringing Alibaba's Ali Cloud artificial intelligence capabilities for real-time traffic predictions which could help police officers better organize and plan strategies. The predictions will come complete with video and image recognition technologies.