The detention in Canada of Meng Wanzhou, Huawei's CFO and the daughter of its founder, is further inflaming tensions between the US and China. Her arrest is linked to a US extradition request on undisclosed charges, but China says it's a human rights violation and is demanding her swift release. Behind this very public drama is a long-running, behind-the-scenes one centered on western intelligence agencies' fears that Huawei poses a significant threat to global security. Among the spooks' biggest concerns are: The Chinese firm is the world's largest manufacturer of things like base stations and antennae that mobile operators use to run wireless networks. And those networks carry data that's used to help control power grids, financial markets, transport systems, and other parts of countries' vital infrastructure.

An accident in a swimming pool left Chieko Asakawa blind at the age of 14. For the past three decades she's worked to create technology - now with a big focus on artificial intelligence (AI) - to transform life for the visually impaired. "When I started out there was no assistive technology," Japanese-born Dr Asakawa says. "I couldn't read any information by myself. Those "painful experiences" set her on a path of learning that began with a computer science course for blind people, and a job at IBM soon followed. She started her pioneering work on accessibility at the firm, while also earning her doctorate. Dr Asakawa is behind early digital Braille innovations and created the world's first practical web-to-speech browser. Those browsers are commonplace these days, but 20 years ago, she gave blind internet users in Japan access to more information than they'd ever had before. Now she and other technologists are looking to use AI to create tools for visually impaired people.

Starting up in her 50s, Swati Padmaraj is bringing style gurus to one's home and increase a retailer's engagement with customers with machine learning platform'Style-At-Iz' by Atiz Fashion House. After 25 years of being a housewife and a mother, anyone would want to retire after the kids are off to college. But, Swati Padmaraj actually went back to college in 2011 to study a degree in designing and sourcing apparel at Seattle University to fulfil her childhood dream of being a fashion designer. Funnily enough, the master's degree in chemistry she got 33 years ago played a vital role in her becoming a fashion designer. "I realised how to use different materials and create my own brand," says Swati, founder of Atiz Fashion House, which owns startup Style-At-Iz.

O2's data network has been offline for more than 12 hours and nobody appears to have any idea when it will come back online. Both the network itself as well as Ericsson – whose software appears to be to blame for the problems – has repeatedly committed to bring the data service back online as soon as they can. But neither has offered a specific timeline for when the fix will be in place, or any real detail of whether it is close to fixing it. The network's chief executive, Mark Evans, gave the latest statement on the ongoing outage. But he failed to give any detail on the problem itself.

O2's 3G mobile data network should have returned to full functionality on Thursday night following an all-day outage, the company has said. But continuing problems with faster 4G connections apparently remained into Friday morning. O2 and its parent company Telefonica had said they were "aiming" to have the problems fixed by Friday morning, but O2 later updated the timescale. It said 3G service should have resumed at 9.30pm UK time, adding that "our technical teams will continue working hard with Ericsson engineers to restore 4G which will bring us back to full network service". Shortly after midnight on Friday morning a spokeswoman added: "Our technical teams have started to return our 4G service to our network. We anticipate this will be restored by 3am this morning meaning all our services will be fully restored. "We'll continue to monitor the service and share any further updates on our website.

Donald Trump's commitment to "remain[ing] a steadfast partner of Saudi Arabia," despite the regime's gruesome torture and murder of journalist Jamal Khashoggi in Turkey, is clearly symptomatic of the malignantly self-serving nature of US foreign policy, which has long propped up dictatorships and enabled atrocities around the world for the sake of profit and power. However, many of Trump's most vocal critics on the Saudi file show signs of an equally dangerous pathological condition: a profound historical amnesia that permits some of the most prominent proponents of the US' own torturous and murderous policies to now parade as champions of human rights, without any apparent sense of irony. Obama-era CIA Director John Brennan, for instance, has insisted that "the US should never turn a blind eye to this sort of inhumanity [referring to the murder of Khashoggi] … because this is a nation that remains faithful to its values" - a curiously self-righteous stance for a man who not only repeatedly turned a blind eye to the inhumanity of past and present CIA practices such as extraordinary rendition, torture, and drone assassination, but actively defended and (in the case of drone use) expanded them. Senate Majority Leader Mitch McConnell decried the brutal murder of Khashoggi as "completely abhorrent to everything the United States holds dear and stands for in the world". Yet he praised another perpetrator of abhorrent deeds, CIA "black site" torture prison manager Gina Haspel, as an "excellent choice" for Director of the CIA.

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In this paper, we propose: (a) a top-down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in social organizations, and (b) a bottom-up fuzzy opinion network to model how the opinions of a large number of agents are agglomerated layer-by-layer into a consensus or a few opinions in the social processes such as an election. For the top-down hierarchical fuzzy opinion network, we prove that the opinions of all the agents converge to the leaders opinion, but the uncertainties of the agents in different groups are generally converging to different values. We demonstrate that the speed of convergence is greatly improved by organizing the agents in a hierarchical structure of small groups. For the bottom-up hierarchical fuzzy opinion network, we simulate how a wide spectrum of opinions are negotiating and summarizing with each other in a layer-by-layer fashion in some typical situations.

Abstract--Batch normalization has become ubiquitous in many state-of-the-art nets. It accelerates training and yields good performance results. However, there are various other alternatives tonormalization, e.g. The objective of this paper is to explore the possible alternatives to channel normalization with orthonormalization layers. The performance of the algorithms are compared together with BN with prescribed performance measures.

As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting. We show that by solving a matrix completion-style problem, we can recover the accuracies of these multi-task sources given their dependency structure, but without any labeled data, leading to higher-quality supervision for training an end model. Theoretically, we show that the generalization error of models trained with this approach improves with the number of unlabeled data points, and characterize the scaling with respect to the task and dependency structures. On three fine-grained classification problems, we show that our approach leads to average gains of 20.2 points in accuracy over a traditional supervised approach, 6.8 points over a majority vote baseline, and 4.1 points over a previously proposed weak supervision method that models tasks separately.