Gabon striker Pierre-Emerick Aubameyang is much less successful at Arsenal this season, failing to score for a third consecutive English Premier League match.EnglandMOHAMED SALAH (Liverpool)The 2017 African Footballer of the Year fired Liverpool to the top of the Premier League with the only goal in a win over Brighton.Salah netted in the 23rd minute with a clinical finish, superbly steering a left-foot shot inside the far post from Senegalese Sadio Mane’s pass.WILFRIED ZAHA (Crystal Palace)Despite scoring, the Ivory Coast forward endured another frustrating trip to Watford, as Palace crashed to a 2-1 defeat.Vicarage Road has been a house of horrors for Zaha, who has been booked on his last two visits for diving.He was memorably taunted by Watford mascot Harry the Hornet in 2016 when he threw himself in front of Zaha after the final whistle to ridicule the Palace player’s diving.PIERRE-EMERICK AUBAMEYANG (Arsenal)His goalless start to the season extended to three games as he failed to find the net in a 3-1 win over West Ham at the Emirates Stadium.Aubameyang was superb in his first few months with Arsenal following a January move from Borussia Dortmund, but he has struggled under new boss Unai Emery, who declared last week that his forward is lacking confidence.ItalyCHRISTIAN KOUAME (Genoa)Ivorian Kouame scored on his Serie A debut with the second goal in a 2-1 win for Genoa over Empoli in his team’s first match since the bridge tragedy in the city two weeks ago.The 20-year-old beat the offside trap to run onto a through ball and scored from an angle after 18 minutes as the crowd observed 43 minutes of silence, one for each of the victims of the bridge collapse.KEVIN-PRINCE BOATENG (Sassuolo)Germany-born Ghana forward Boateng converted a penalty with the last kick of the game to snatch a point for Sassuolo after Leonardo Pavoletti’s Cagliari brace.VAR judged Filippo Romagnoli to have handled the ball and Boateng converted the penalty to equalise for a 2-2 draw.It was the 31-year-old former AC Milan player’s first goal for his new club since moving back to Italy from Eintracht Frankfurt in Germany this season.KALIDOU KOULIBALY (Napoli)Senegalese defender Kalidou Koulibaly helped stifle Argentine forward Gonzalo Higuain as Napoli fought back to beat Milan. © AFP / Alberto PIZZOLISenegalese defender Koulibaly’s Napoli suffered an early scare after going two goals down before fighting back to beat AC Milan 3-2 at their San Paulo Stadium.But the 27-year-old said he was pleased with how the Serie A runners-up reacted to turn it around for the second week in a row.“It’s a pity that we conceded those two goals, but we’ll learn from the experience and improve,” said the Senegalese. “We know that we can turn situations around, and will go far with this mentality.”GermanyNABIL BENTALEB (Schalke 04)The Algeria midfielder came off the bench to convert a late penalty for Schalke to cancel out John Anthony Brook’s first-half goal before Daniel Ginzcek scored a dramatic 94th-minute winner in Wolfsburg’s 2-1 shock win.Former Tottenham Hotspur midfielder Bentaleb, 23, made just 12 league starts for Schalke last season and only came on after 83 minutes as last season’s runners-up crashed on the opening day of the season.ANTHONY UJAH (Mainz)The Nigeria striker came off the bench to score Mainz’s late winner in a 1-0 victory over Stuttgart.The 27-year-old was sent on in the 66th minute and grabbed the winner 10 minutes later after a mistake by Stuttgart’s ex-Germany defender Holger Badstuber for his first goal since returning to Germany from Chinese side Liaoning FC in January.0Shares0000(Visited 1 times, 1 visits today) 0Shares0000Egyptian star Mohamed Salah was again Liverpool’s goal-scoring hero in a 1-0 victory over Brighton. © AFP/File / Lindsey PARNABYPARIS, France, Aug 27 – Egyptian Mohamed Salah continued to be the star African performer in the major European leagues, scoring the goal that gave Liverpool victory over Brighton at the weekend.It was his second goal this season for the Reds and 29th in 29 matches in home games at Anfield since joining them at the beginning of last season from Roma.
Twenty-three retired educators from the Ministry of Education, Youth and Information, Region Two, who have served between 18 and 43 years, were honoured during a Recognition Ceremony held on September 19 at The Jamaica Pegasus hotel in Kingston. Story Highlights esident of the Jamaica Teachers’ Association (JTA), Georgia Waugh-Richards, who brought greetings, encouraged the retirees to continue to contribute to education by sharing the wisdom that has been garnered over decades with the younger generation of teachers.
Related Items:#electioncountdown, #magneticmedianews Facebook Twitter Google+LinkedInPinterestWhatsAppProvidenciales, TCI, November 15, 2016 – One month and the 7,727 voters will go to the election polls to determine the next government of the Turks and Caicos Islands. It is a contentious race for the 15 seats; 10 from the districts, five all island and in three days it will be clear who is offering for office.Friday, November 18th is Nomination Day in the Turks and Caicos Islands. Facebook Twitter Google+LinkedInPinterestWhatsApp
How quantum effects could improve artificial intelligence Physicists have developed a quantum machine learning algorithm that can handle infinite dimensions—that is, it works with continuous variables (which have an infinite number of possible values on a closed interval) instead of the typically used discrete variables (which have only a finite number of values). More information: Hoi-Kwan Lau, Raphael Pooser, George Siopsis, and Christian Weedbrook. “Quantum Machine Learning over Infinite Dimensions.” Physical Review Letters. DOI: 10.1103/PhysRevLett.118.080501Also at arXiv:1603.06222 [quant-ph] The researchers, Hoi-Kwan Lau et al., have published a paper on generalizing quantum machine learning to infinite dimensions in a recent issue of Physical Review Letters.As the physicists explain, quantum machine learning is a new subfield within the field of quantum information that combines the speed of quantum computing with the ability to learn and adapt, as offered by machine learning. One of the biggest advantages of having a quantum machine learning algorithm for continuous variables is that it can theoretically operate much faster than classical algorithms. Since many science and engineering models involve continuous variables, applying quantum machine learning to these problems could potentially have far-reaching applications.”Our work demonstrates the ability to take advantage of photonics to perform machine learning tasks on a quantum computer that could far exceed the speed of any conventional computer,” coauthor George Siopsis at the University of Tennessee told Phys.org. “Quantum machine learning also offers potential advantages such as lower energy requirements owing to the ability to store more information per qubit, and a very low cost per qubit compared to other technologies.”Most quantum machine learning algorithms developed so far work only with problems involving discrete variables. Applying quantum machine learning to continuous-variable problems requires a very different approach.To do this, the physicists had to develop a new set of tools that work with continuous variables. This involves replacing the logic gates that are used for discrete-variable states with physical gates, which work for continuous-variable states. Building up from these basic building blocks of the algorithm, the scientists then developed new methods that power the quantum machine learning problems, called subroutines, which are represented by matrices and vectors.Although the results of the study are purely theoretical, the physicists expect that the new algorithm for continuous variables could be experimentally implemented using currently available technology. The implementation could be done in several ways, such as by using optical systems, spin systems, or trapped atoms. Regardless of the type of system, the implementation would be challenging. For example, an optical implementation that the scientists outlined here would require some of the latest technologies, such as “cat states” (a superposition of the “0” and “1” states) and high rates of squeezing (to reduce quantum noise). In the future, the scientists hope to further investigate how continuous-variable quantum machine learning can be extended to replicate some of the latest results involving discrete variables. Another interesting avenue to pursue is a hybrid approach, which would combine the methods of both discrete and continuous variables in a single algorithm. © 2017 Phys.org Explore further Citation: Physicists extend quantum machine learning to infinite dimensions (2017, March 6) retrieved 18 August 2019 from https://phys.org/news/2017-03-physicists-quantum-machine-infinite-dimensions.html The proposed optical set-up that could be used to implement the new quantum machine learning algorithm over infinite dimensions. Credit: Lau et al. ©2017 American Physical Society Journal information: Physical Review Letters This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.