China plans to cooperate with Saudi Arabia and other Gulf countries in the fields of nuclear energy, nuclear security and space exploration, President Xi Jinping said on Friday, showcasing his nation's strengthening ties with a region that was once firmly in the U.S. sphere of influence. Mr. Xi was speaking in the Saudi capital, Riyadh, at a summit with rulers and officials from the six Gulf Cooperation Council countries -- Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates -- during a three-day visit to Saudi Arabia. Later on Friday, he held his third and final summit of the visit with other Arab and African leaders. Both China and Saudi Arabia described Mr. Xi's visit this week as a historic event ushering in a new era of relations between Beijing and the Middle East, a region that once had a mainly oil-based relationship with China, a major consumer of the Gulf's fossil fuel exports. Arab states are increasingly building broader ties with China that extend into arms sales, technology transfers and infrastructure projects.

Qatar's World Cup has seen the round-of-16 games. Now, it is time for the quarter-finals. Among the biggest shocks during the World Cup this year have been the games that former champions – Brazil, Spain, Argentina and Germany – did not win. This tournament also saw teams playing for Africa, Asia, and North America represented in the round of 16 along with traditional football powerhouses South America and Europe. But behind the great football spectacle, there has been a battle taking place at the Al Jazeera offices.

Alex Svinov is the CEO and Co-founder of Insquad, the platform to build remote development teams. He believes that the future of work is in remote teams – and this notion will radically change the world as it will bring opportunity and talent closer to each other. Alex launched Insquad after facing challenges in hiring senior tech talent for his previous startup. He tried staffing services, but they were expensive and did not give a lot of value to him as a startup. So he decided to solve this problem and help the startup community as well as offer great opportunities to the talent in underprivileged countries. Alex is a serial entrepreneur and angel investor -- 10 investments in IT companies all over the world -- Forbes council member and Alchemist mentor. In the past 10 years, he has created several successful startups in industry areas that he had no experience in before -- FinTech, outsourcing, HRTech, and food service. He is passionate about making new tech products and services and helping distributed teams achieve their goals. Alex met with Bill Clinton and Queen Elizabeth in Moscow's high school. Outside of work, he's a father of 3, plays tennis and regularly participates in amateur tournaments. Today I have with me, Alex Svinov. Now, Alex is the CEO and Co-founder of Insquad, which is a platform to build remote development teams and as the world basically circulates around technology these days, it's very very important to get a great development team and remote now as we know with the pandemic has produced change the way we work. He believes that the future work is in remote teams and this notion will radically change the world as well bring opportunity and talent closer to each other. Alex launched Insquad one year ago because he faced challenges hiring senior tech talents for his previous startup. He tried staffing services but they were expensive and did not give a lot of value to him as a startup.

Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient's functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients' movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients' progress during the rehabilitation sessions that correspond to the clinicians' findings about each case.

Process mining is a family of techniques that support the analysis of operational processes, in terms of key performance indicators, using event data Van Der Aalst (2012). Process mining can be used in number of ways, such as in identifying insights into current processes or in identifying actions or places within workflows where interventions should be made to improve performance. Although processing mining is typically used in the context of commercial business environments, there is crossover to other areas where processes play an important role, such as in water quality management processes administered by local government authorities or citizen science projects that use the Business Process Model and Notation (BPMN) Higgins, Williams, Leibovici, Simonis, Davis, Muldoon, van Genuchten, O'Hare and Wiemann (2016). In the case of water quality management, traditional event log data from information technology systems is often lacking in that many tasks, such as the manual sampling of water and the microbial culturing by biologists and laboratory technicians to identify faecal coliforms, are not performed using computers and are not logged. Nevertheless, it is likely that techniques developed to aid explainability and in the evaluation of machine learning algorithms in such cases will prove using in traditional process mining systems where similar problems must be addressed. This paper focuses on mining suitable features to perform inference for the level of bacteria, and specifically Enterococci and Escherichia coli (E.

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Many market models implement this principle to predict electricity prices but typically require certain assumptions and simplifications. In this article, we present an explainable machine learning model for the prices on the German day-ahead market, which substantially outperforms a benchmark model based on the merit order principle. Our model is designed for the ex-post analysis of prices and thus builds on various external features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle the role of the different features and quantify their importance from empiric data. Load, wind and solar generation are most important, as expected, but wind power appears to affect prices stronger than solar power does. Fuel prices also rank highly and show nontrivial dependencies, including strong interactions with other features revealed by a SHAP interaction analysis. Large generation ramps are correlated with high prices, again with strong feature interactions, due to the limited flexibility of nuclear and lignite plants. Our results further contribute to model development by providing quantitative insights directly from data.

Even for us, it can be challenging to comprehend the meaning of songs. As part of this project, we explore the process of generating the meaning of songs. Despite the widespread use of text-to-text models, few attempts have been made to achieve a similar objective. Songs are primarily studied in the context of sentiment analysis. This involves identifying opinions and emotions in texts, evaluating them as positive or negative, and utilizing these evaluations to make music recommendations. In this paper, we present a generative model that offers implicit meanings for several lines of a song. Our model uses a decoder Transformer architecture GPT-2, where the input is the lyrics of a song. Furthermore, we compared the performance of this architecture with that of the encoder-decoder Transformer architecture of the T5 model. We also examined the effect of different prompt types with the option of appending additional information, such as the name of the artist and the title of the song. Moreover, we tested different decoding methods with different training parameters and evaluated our results using ROUGE. In order to build our dataset, we utilized the 'Genious' API, which allowed us to acquire the lyrics of songs and their explanations, as well as their rich metadata.

Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.