The graph represents a network of 5,296 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 October 2021 at 00:42 UTC. The requested start date was Friday, 01 October 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 20-hour, 38-minute period from Sunday, 26 September 2021 at 06:21 UTC to Thursday, 30 September 2021 at 03:00 UTC.

We're looking for an experienced Data Analyst to join our team and create insights that drive the growth of our bank products. Nestled within the broader Merchant Bank Tribe, the Growth Team is responsible for ensuring that small businesses discover, choose and start using SumUp's banking products. Together we define strategies to increase the adoption and usage of SumUp's banking products, and validate these strategies through regular experimentation. If you're excited about giving small businesses access to the banking services they deserve, we'd love to hear from you. You'll be great for this position if We believe in the everyday hero.

New Jersey (United States) – A2Z Market Research published new research on Global Artificial Intelligence Drug R&D covering the micro-level of analysis by competitors and key business segments (2022-2029). The Global Artificial Intelligence Drug R&D explores a comprehensive study on various segments like opportunities, size, development, innovation, sales, and overall growth of major players. The research is carried out on primary and secondary statistics sources and it consists of both qualitative and quantitative detailing. Various factors are responsible for the market's growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Artificial Intelligence Drug R&D market.

Machine learning techniques can provide an assumption-free analysis of epidemic case data with surprisingly good prediction accuracy and the ability to dynamically incorporate the latest data, a new KAUST study has shown. The proof of concept developed by Yasminah Alali, a student in KAUST's 2021 Saudi Summer Internship (SSI) program, demonstrates a promising alternative approach to conventional parameter-driven mechanistic models that removes human bias and assumptions from analysis and shows the underlying story of the data. Working with KAUST's Ying Sun and Fouzi Harrou, Alali leveraged her experience working with artificial intelligence models to develop a framework to fit the characteristics and time-evolving nature of epidemic data using publicly reported COVID-19 incidence and recovery data from India and Brazil. "My major at college was artificial intelligence, and I previously worked on a medical project using various ML algorithms," says Alali. "Working with Professor Sun and Dr Harrou during my internship, we considered whether the Gaussian Process Regression method would be useful for predicting pandemic spread because it gives confidence intervals for the predictions, which can greatly assist decision-makers." Accurate forecasting of cases during a pandemic is essential to help mitigate and slow transmission.

It is better to launch products off a leaner base and, should a bank seek an acquirer, a lower cost base would also help strengthen valuations. While the jury is still out on whether the current market uncertainty will result in an imminent recession or a prolonged period of slow growth, the fact is that growth has slowed. As growth is unlikely to quicken in the medium term, we have, without question, entered the late cycle. Compounding this situation is the continued threat posed by fintechs and big technology companies, as they take stakes in banking businesses. The call to action is urgent: whether a bank is a leader and seeks to "protect" returns or is one of the underperformers looking to turn the business around and push returns above the cost of equity, the time for bold and critical moves is now.

Argentine national security agencies have acquired irregular access to the biometric records of seven million people, including the president, and footage from Buenos Aires for identifying demonstrators via facial recognition cameras when authorized to access a list of fewer than 50,000 persons of interest, reports Página 12 via Público. The Buenos Aires judge who discovered the scandal has now demanded explanations from the city's Minister of Security and Justice as to how biometric data of a set of 62 cases relating to the capital, including those of the Argentine president and vice president, were transferred from the national ID database – the Registro Nacional de las Personas (ReNaPer) – to the city's authorities, namely the police, reports Página 12/Público. A massive breach of ReNaPer's digital ID database was reported last year. Judge Roberto Andrés Gallardo has suspended the use of the facial recognition system in question and has given Marcel D'Alessandro, Minister of Security and Justice for the City of Buenos Aires Government, two days to explain how the biometrics of persons such as former president and current vice president Cristina Fernández de Kirchner and fellow former president Alberto Fernández were used. D'Alessandro had previously said that the system had been deactivated during the pandemic.

Latest survey on Artificial Intelligence for Blockchains Market is conducted to provide hidden gems performance analysis of Artificial Intelligence for Blockchains to better demonstrate competitive environment . The study is a mix of quantitative market stats and qualitative analytical information to uncover market size revenue breakdown by key business segments and end use applications. The report bridges the historical data from 2017 to 2022 and forecasted till 2027*, the outbreak of latest scenario in Artificial Intelligence for Blockchains market have made companies uncertain about their future outlook as the disturbance in value chain have made serious economic slump. If you are part of the Artificial Intelligence for Blockchains industry or intend to be, then study would provide you comprehensive outlook. It is vital to keep your market knowledge up to date analysed by major players and high growth emerging players.

Depth prediction is a critical problem in robotics applications especially autonomous driving. Generally, depth prediction based on binocular stereo matching and fusion of monocular image and laser point cloud are two mainstream methods. However, the former usually suffers from overfitting while building cost volume, and the latter has a limited generalization due to the lack of geometric constraint. To solve these problems, we propose a novel multimodal neural network, namely UAMD-Net, for dense depth completion based on fusion of binocular stereo matching and the weak constrain from the sparse point clouds. Specifically, the sparse point clouds are converted to sparse depth map and sent to the multimodal feature encoder (MFE) with binocular image, constructing a cross-modal cost volume. Then, it will be further processed by the multimodal feature aggregator (MFA) and the depth regression layer. Furthermore, the existing multimodal methods ignore the problem of modal dependence, that is, the network will not work when a certain modal input has a problem. Therefore, we propose a new training strategy called Modal-dropout which enables the network to be adaptively trained with multiple modal inputs and inference with specific modal inputs. Benefiting from the flexible network structure and adaptive training method, our proposed network can realize unified training under various modal input conditions. Comprehensive experiments conducted on KITTI depth completion benchmark demonstrate that our method produces robust results and outperforms other state-of-the-art methods.

Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, directly applying federated learning to real-world tasks faces two challenges: (1) heterogeneity in the data among different organizations; and (2) data noises inside individual organizations. In this paper, we propose a general framework to solve the above two challenges simultaneously. Specifically, we propose using distributionally robust optimization to mitigate the negative effects caused by data heterogeneity paradigm to sample clients based on a learnable distribution at each iteration. Additionally, we observe that this optimization paradigm is easily affected by data noises inside local clients, which has a significant performance degradation in terms of global model prediction accuracy. To solve this problem, we propose to incorporate mixup techniques into the local training process of federated learning. We further provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability. Furthermore, we conduct empirical studies across different drug discovery tasks, such as ADMET property prediction and drug-target affinity prediction.

Let's explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being.