The National Basketball Association (NBA) and Chinese e-commerce company Alibaba have announced a multiyear partnership, as the league stages two games in Macau to mark its return to the Chinese market for the first time since 2019. The announcement by Alibaba Group on Thursday said it would provide artificial intelligence and cloud computing services with the NBA and enhance fan experiences on the NBA app in China. The NBA is due to play two preseason games in the Chinese special administrative region on Friday and Sunday, part of a five-year contract with Las Vegas Sands' Macau unit Sands China. The games mark the first time the NBA is playing in Macau, the world's largest gambling hub, and follow a years-long absence amid controversy over the 2019 Hong Kong protests. The Macau games aim to bolster the NBA's profile in China, where the league estimates say about 300 million people play basketball, at a time of rising political tensions between the United States and China.

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.

B.T. had proven easy enough to find. When he went dark, Lily figured he was in one of the world's three gambling capitals--Vegas, Monte Carlo, or Macau. It only became a matter of checking with a handful of five-star hotels in each, something Sherman was happy to handle for her. In the days since Castro's death, it was Sherman who'd stoked Lily's concern for B.T. Each morning, Sherman came in parroting another conspiracy theory as to who or what was behind the president's untimely demise. He'd even gone so far as to place a #TRUTHNOTDREAMS sticker adjacent to the US Marine Corps sticker on his wheelchair.

A suspect in the 1MDB scandal has died weeks after being deported to Malaysia to face questioning over his role in the $4.5bn fraud. Kee Kok Thiam died in hospital on Monday following a "sudden massive stroke" and was cremated on Wednesday morning, Kee's family said in a statement. "We urge all parties not to entertain any speculations on this unfortunate event and allow the family the space to grief [sic] on his passing," the statement said. News of the 56-year-old businessman's death comes hours after Al Jazeera reported that the Malaysian Anti-Corruption Commission (MACC) had confirmed the whereabouts of fugitive Malaysian financier Jho Taek Low – the alleged mastermind of the 1MDB scandal – in Macau based on its questioning of Kee. The MACC said that Kee, who was deported from Macau earlier this month, revealed he had met with Low and other 1MDB fugitives in the Chinese territory and that Low had instructed him "not to return to Malaysia as a witness in the 1MDB case".

Consistent in their quest to spearhead innovative, groundbreaking events, Eventus International is hosting the first ever AI In Gaming 2020 summit in Dubai on 26 and 27 February at Crowne Plaza Dubai. Joining a lineup of top international industry experts, is Andrew Pearson, founder and MD of Intelligencia Limited, who will be speaking at AI In Gaming 2020. Andrew Pearson was born in Pakistan, grew up in Singapore and was educated in England and America. With a degree in psychology from UCLA, Pearson has had a varied career in IT, marketing, mobile technology, social media and entertainment.In 2011, Pearson relocated to Hong Kong to open Qualex Asia Limited, bringing its parent company's experience into the ASEAN region. Pearson is the Managing Director of Intelligencia Limited, a leading implementer of BI, CI, data warehousing, data modeling, predictive analytics, data visualisation, digital marketing, mobile, social media and cloud solutions for the gaming, finance, telco, hospitality and retail industries.

Thank you so much for doing this with us! Can you tell us a story about what brought you to this specific career path? It's been a rather circuitous route to get to where I am today, but in some ways moving to Asia in 2012 was a sort of homecoming for me because I had spent much of my youth in Singapore. I was asked to head up a small operation in Macau because my former company had closed a limited training deal with the Venetian Macao. I arrived here, oversaw that project and then teamed up with SAS, our main software partner at the time, and helped close deals with other casinos in Macau, a cruise line in Hong Kong, the Resorts World group in the US, as well as a sports book in Australia. After about 18 months, I had overseen work in Macau, the Philippines, Australia, Malaysia, Hong Kong, and the US.

Facial recognition technology is likely to be among innovations on the agenda when bankers, financial regulators and officials from across Asia gather in Hong Kong on Wednesday for a landmark summit. The high-powered meeting comes as the race to regulate the fintech (financial technology) revolution is intensifying amid fears that a regulatory "vacuum" could be exploited by money launderers, fraudsters and underground bankers whose activities threaten national, regional and international financial stability. Organisers of the summit to launch the Alliance for Financial Stability with Information Technology, also known as AFS-IT, say speakers and guests will include top-level representation across the banking, financial, technology and regulatory sectors as well as government officials from China, Hong Kong, Macau, the Philippines, Thailand, Cambodia, Myanmar, Vietnam and Singapore. A key focus will be the fostering of innovation in regulation technology, or regtech, a new field in which information technology is used to enhance regulation. Innovative fintech can be used and adapted to benefit both service providers and regulators, and it includes know-your-customer facial recognition technology recently installed across the ATM network of casino hub Macau as part of an ongoing crackdown on money laundering and capital flight from China.

Macau, the gambling capital of the world, is upgrading all 1,200 of its ATMs with facial recognition cameras. According to Bloomberg, any user looking to make a withdrawal will need to enter their PIN and then stare into a lens for six seconds to verify your identity. The move is partly to improve bank security, but mostly to enable China to keep an eye on who's doing what with their cash, and when. The backstory here is that huge amounts of Chinese money flows out of the country without the consent of the government. Apparently, around $816 billion was expropriated from the land, both by private citizens and officials with their hand in the till.

We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data. Our model can factorize any set of entities and relations that can be represented by a relational model, including tensors and also multiple relations for each entity. Macau can also incorporate side information, specifically entity and relation features, which are crucial for predicting sparsely observed relations. Macau scales to millions of entity instances, hundred millions of observations, and sparse entity features with millions of dimensions. To achieve the scale up, we specially designed sampling procedure for entity and relation features that relies primarily on noise injection in linear regressions. We show performance and advanced features of Macau in a set of experiments, including challenging drug-protein activity prediction task.

The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-competitive interaction to particular protein targets. Method: to analyze the interaction types we propose factorization method Macau which allows us to combine different measurement types into a single tensor together with proteins and compounds. The compounds are characterized by high dimensional 2D ECFP fingerprints. The novelty of the proposed method is that using a specially designed noise injection MCMC sampler it can incorporate high dimensional side information, i.e., millions of unique 2D ECFP compound features, even for large scale datasets of millions of compounds. Without the side information, in this case, the tensor factorization would be practically futile. Results: using public IC50 and Ki data from ChEMBL we trained a model from where we can identify the latent subspace separating the two measurement types (IC50 and Ki). The results suggest the proposed method can detect the competitive inhibitory activity between compounds and proteins.