Finding an energy minimum in the Ising model is an exemplar objective, associated with many combinatorial optimization problems, that is computationally hard in general, but occurs in all areas of modern science. There are several numerical methods, providing solution for the medium size Ising spin systems. However, they are either computationally slow and badly parallelized, or do not give sufficiently good results for the large systems. In this paper, we present a highly parallel algorithm, called Mean-field Annealing from a Random State (MARS), incorporating the best features of the classical simulated annealing (SA) and Mean-Field Annealing (MFA) methods. The algorithm is based on the mean-field descent from a randomly selected configuration and temperature. Since a single run requires little computational effort, the effectiveness can be achieved by massive parallelisation. MARS shows excellent performance both on the large Ising spin systems and on the set of exemplary maximum cut benchmark instances in terms of both solution quality and computational time.

In this paper we demonstrate end to end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition. We further demonstrate CSR for noisy speech by fusing with EEG features.

We consider a multi-agent resource allocation setting in which an agent's utility may decrease or increase when an item is allocated. We take the group envy-freeness concept that is well-established in the literature and present stronger and relaxed versions that are especially suitable for the allocation of indivisible items. Of particular interest is a concept called group envy-freeness up to one item (GEF1). We then present a clear taxonomy of the fairness concepts. We study which fairness concepts guarantee the existence of a fair allocation under which preference domain. For two natural classes of additive utilities, we design polynomial-time algorithms to compute a GEF1 allocation. We also prove that checking whether a given allocation satisfies GEF1 is coNP-complete when there are either only goods, only chores or both.

Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple BERT-based LS approach that makes use of the pre-trained unsupervised deep bidirectional representations BERT. We feed the given sentence masked the complex word into the masking language model of BERT to generate candidate substitutions. By considering the whole sentence, the generated simpler alternatives are easier to hold cohesion and coherence of a sentence. Experimental results show that our approach obtains obvious improvement on standard LS benchmark.

An Elon Musk-backed startup looking to connect human brains to computers will make a major announcement on Tuesday, according to a recent tweet from the CEO. The mysterious announcement, which Musk chose not to elaborate on in his tweet, follows years of radio silence from the company and was foreshadowed by Musk -- the CEO of Tesla and SpaceX -- earlier this year. The event will take place in San Francisco and will presumably have something to with what the company's website calls an'ultra high bandwidth brain-machine interfaces to connect humans and computers.' Elon Musk said his mysterious Neuralink startup will make an announcement tomorrow for the first time in two years. While many tech leaders push that AI will become invaluable to humanity, others argue it poses a threat to our species.

WASHINGTON - The use of robots in U.S. manufacturing has more than tripled over the two decades, and has doubled in the rest of the world, replacing certain categories of worker, according to a report published Monday. As of 2017, automation in the United States had risen to 1.8 robots for every 1,000 workers from just 0.5 recorded 22 years earlier, according to research by the Federal Reserve Bank of St. Louis. The report found the highest prevalence of robots in the auto sector, with France in the lead, followed by the United States and Germany. Automation has eroded the number of intermediate "middle-skill" occupations, while the share of high-skill and low-skill positions has grown, it said. France leads the way in employing robots to build cars, using 148 robots for every 1,000 workers, compared to 136 in the United States, while Italy and Germany each use about 120, the study found.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. It's cost effective to snag smart home products when they're on sale, and right now it just so happens that Amazon is having a huge sale on just about everything. That includes otherwise pricey smart home devices from brands like Hue, Sonos, Arlo, Ring, and many more. We've rounded up the most impressive best Prime Day deals on smart home products below.

The Bank of England's new 50-pound note will feature mathematician Alan Turing, honoring the code-breaker who helped lay the foundation for computer science. The Bank of England's new 50-pound note will feature mathematician Alan Turing, honoring the code-breaker who helped lay the foundation for computer science. Alan Turing, the father of computer science and artificial intelligence who broke Adolf Hitler's Enigma code system in World War II -- but who died an outcast because of his homosexuality -- will be featured on the Bank of England's new 50-pound note. The new note will be printed on polymer and will bear a 1951 photo of Turing, the bank announced Monday. It's expected to enter circulation by the end of 2021. It will include a quote from Turing: "This is only a foretaste of what is to come and only the shadow of what is going to be." Turing was just 41 when he died from poisoning in 1954, a death that was deemed a suicide.

Bottom line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users' behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren't expending the time and effort to hack into systems; they're looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon's 2019 Data Breach Investigations Report, 'Phishing' (as a pre-cursor to credential misuse), 'Stolen Credentials', and'Privilege Abuse' account for the majority of threat actions in breaches (see page 9 of the report).