Pilots operating aircraft in un-towered airspace rely on their situational awareness and prior knowledge to predict the future trajectories of other agents. These predictions are conditioned on the past trajectories of other agents, agent-agent social interactions and environmental context such as airport location and weather. This paper provides a dataset, $\textit{TrajAir}$, that captures this behaviour in a non-towered terminal airspace around a regional airport. We also present a baseline socially-aware trajectory prediction algorithm, $\textit{TrajAirNet}$, that uses the dataset to predict the trajectories of all agents. The dataset is collected for 111 days over 8 months and contains ADS-B transponder data along with the corresponding METAR weather data. The data is processed to be used as a benchmark with other publicly available social navigation datasets. To the best of authors' knowledge, this is the first 3D social aerial navigation dataset thus introducing social navigation for autonomous aviation. $\textit{TrajAirNet}$ combines state-of-the-art modules in social navigation to provide predictions in a static environment with a dynamic context. Both the $\textit{TrajAir}$ dataset and $\textit{TrajAirNet}$ prediction algorithm are open-source. The dataset, codebase, and video are available at https://theairlab.org/trajair/, https://github.com/castacks/trajairnet, and https://youtu.be/elAQXrxB2gw respectively.

In a video that went viral on Twitter Sunday night, a massive explosion rips through what appears to be a Russian convoy, scoring a direct hit on a surface-to-air missile system. The black-and-white footage, posted to the account of the Ukrainian armed forces, is one of several that have emerged on social media in recent days showing the devastating impact of Ukrainian drone strikes on Russian hardware. As the drone's payload explodes in the video--which appears to be a cellphone recording of a screen in a Ukrainian drone facility--people at the facility can be heard gasping in awe before breaking out in cheers and applause. The video racked up more than 3 million views on Twitter in two days. There will be no peace for you on our earth!" the Ukrainian armed forces wrote in the video's caption. The star of this video and others circulating on Twitter is the Bayraktar TB2 – a type of Turkish drone that the Ukrainian military has increasingly deployed against Russian forces in recent ...

Dubai International Financial Centre (DIFC), the global financial centre in the Middle East, Africa and South Asia (MEASA) region has announced the launch of an Artificial Intelligence (AI) and coding license, in cooperation with the UAE Artificial Intelligence Office. The license, which is a UAE first, will advance the country's Artificial Intelligence Strategy 2031, which aims to enhance the UAE's reputation in this field by attracting AI companies and coders from around the world. Companies holding the license will be able to work within a stimulating environment at the DIFC Innovation Hub, which is the largest cluster of FinTech and innovation companies in the region. In addition, the license provides an opportunity to obtain UAE Golden Visas for employees working in those companies. Omar Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications, said, "Such initiatives reflect positively on the country's readiness to become a global destination for pioneering the industries of the future by adopting advanced technology and stimulating innovation in various fields. The UAE Government is keen to support digital transformation processes that embody of the directives of His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai. This is being achieved by developing digital activities and providing innovative solutions that contribute to improving the performance of governments and the lives of communities. DIFC has opened new horizons for leading global companies that aspire to enhance their efforts in the field of AI and expand their businesses further."

The Digital Humanities Association of Southern Africa (DHASA) is organizing its third conference with the theme “Digitally Human, Artificially Intelligent”. The field of Digital Humanities is currently still rather underdeveloped in Southern Africa. Hence, this conference has several aims. First, to bring together researchers who are interested in showcasing their research from the broad field of Digital Humanities. By doing so, this conference provides an overview of the current state-of-the-art of Digital Humanities especially in the Southern Africa region. This includes Digital Humanities research by people from Southern Africa or research related to the geographical area of Southern Africa. The DHASA conference is an interdisciplinary platform for researchers working on all areas of Digital Humanities (including, but not limited to language, literature, visual art, performance and theatre studies, media studies, music, history, sociology, psychology, language technologies, library studies, philosophy, methodologies, software and computation, etc.). It aims to create the conditions for the emergence of a scientific Digital Humanities community of practice.

We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily. Data and code are available at https://github.com/albertwy/SWRM.

A legal challenge is being prepared to overturn the Intellectual Property Office's (IPONZ) decision not to recognise a machine as an inventor. It is being led by University of Surrey law professor Ryan Abbott, who has been testing patent law around the world, including New Zealand, to see if an invention created by an artificial intelligence (AI) programme could receive a patent. The test case centres around a "creativity machine" or AI inventor programme, known as DABUS, which was developed by US-based physicist Stephen Thaler. Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person.

February may be the shortest and coldest month of the year. But for many, it is a time to give special recognition to often overlooked aspects of world history (Black History Month) and recognize what may be the single greatest threat to health in the world. For many, February is also known as Heart Health Month, and 2022 will be the 58th consecutive year it is recognized. Cardiovascular disease is a global problem that claims the lives of more people a year than cancer, strokes, or other prevalent diseases. Luckily, advanced research is leading to effective solutions for improving cardiovascular health.

Maëva Ghonda is a scientist born in Kinshasa, the great capital city of the Democratic Republic of Congo (DRC). Maëva is the editor-in-chief of the IEEE Quantum Computing Newsletter, the host of the Quantum AI Series Podcast, and the chair of the Quantum AI Institute. As a research scientist, her work is centered on technological innovations -- i.e. Quantum Computing, Artificial Intelligence and Machine Learning -- to tackle challenges in Pharma and Healthcare (e.g. Maëva Ghonda's passion for quantum computing ignited while working as Joint Quantum Institute Scholar.

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existing literature concerned with the low-rank (i.e., constant-rank) regime. We first consider a class of rotationally invariant matrix denoising problems whose mutual information and minimum mean-square error are computable using techniques from random matrix theory. Next, we analyze the more challenging models of dictionary learning. To do so we introduce a novel combination of the replica method from statistical mechanics together with random matrix theory, coined spectral replica method. This allows us to derive variational formulas for the mutual information between hidden representations and the noisy data of the dictionary learning problem, as well as for the overlaps quantifying the optimal reconstruction error. The proposed method reduces the number of degrees of freedom from $\Theta(N^2)$ matrix entries to $\Theta(N)$ eigenvalues (or singular values), and yields Coulomb gas representations of the mutual information which are reminiscent of matrix models in physics. The main ingredients are a combination of large deviation results for random matrices together with a new replica symmetric decoupling ansatz at the level of the probability distributions of eigenvalues (or singular values) of certain overlap matrices and the use of HarishChandra-Itzykson-Zuber spherical integrals.