The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life. However, the digital medicine revolution also creates unforeseen and complex ethical, regulatory, and societal issues. In this article, we reflect on the ethical challenges facing digital medicine. We discuss the perils of ethical oversights in medical devices, and the role of professional codes and regulatory oversight towards the ethical design, deployment, and operation of digital medicine devices that safely and effectively meet the needs of patients. We advocate for an ensemble approach of intensive education, programmable ethical behaviors, and ethical analysis frameworks, to prevent mishaps and sustain ethical innovation, design, and lifecycle management of emerging digital medicine devices.

We propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs. Our key observation is that the functional roles of sentences used for profiling news discourse signify different time frames relevant to a news story and can, therefore, help to recover the global temporal structure of a document. Our analyses and experiments with the widely used knowledge distillation technique show that discourse profiling effectively identifies distant inter-sentence event and (or) time expression pairs that are temporally related and otherwise difficult to locate.

Masked language modeling is widely used for pretraining large language models for natural language understanding (NLU). However, random masking is suboptimal, allocating an equal masking rate for all tokens. In this paper, we propose InforMask, a new unsupervised masking strategy for training masked language models. InforMask exploits Pointwise Mutual Information (PMI) to select the most informative tokens to mask. We further propose two optimizations for InforMask to improve its efficiency. With a one-off preprocessing step, InforMask outperforms random masking and previously proposed masking strategies on the factual recall benchmark LAMA and the question answering benchmark SQuAD v1 and v2.

Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training. However, contrastive objectives which dominate the current sentence representation learning bring little linguistic interpretability and no performance guarantee on downstream semantic tasks. We instead propose a novel generative self-supervised learning objective based on phrase reconstruction. To overcome the drawbacks of previous generative methods, we carefully model intra-sentence structure by breaking down one sentence into pieces of important phrases. Empirical studies show that our generative learning achieves powerful enough performance improvement and outperforms the current state-of-the-art contrastive methods not only on the STS benchmarks, but also on downstream semantic retrieval and reranking tasks. Our code is available at https://github.com/chengzhipanpan/PaSeR.

Between 2019 and 2022, Greek media attention has been attracted by a rather unusually high number of femicide cases which have been trending for several weeks up to months in the public debate and one of the contributing factors is the feedback loop between traditional media and social media. In this paper we are investigating the use of the term "\{gamma}\u{psion}{\nu}{\alpha}{\iota}\k{appa}{\omicron}\k{appa}{\tau}{\omicron}{\nu}{\iota}{\alpha}" (femicide) in Greek speaking twitter. More specifically, we approach the problem from a stance detection perspective, aiming to automatically identify user position with regards to the feministic semantics of the word. We also discuss findings from an identity analysis perspective and intercorrelations with hate speech that have been identified in the collected corpus of tweets.

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

A little before 7am on Monday, people in Kyiv heard a whining sound overhead before identifying where it was coming from – a group of "kamikaze" drones flying into the city. Drones have been widely used on both sides of the Ukraine conflict, but these were the first Russian attacks that deployed swarms of the aircraft. Videos and images began to circulate on social media of the drones flying directly over urban infrastructure such as power stations, residential buildings and railways as civilians and soldiers tried to shoot them down with guns. About 28 were launched on Monday morning in Kyiv. At least four civilians were killed after one of the aircraft hit a residential building.

Waging brutal repression at home and allegedly helping Russia in its war against Ukraine, Iran is becoming an unsolvable challenge for Western powers eager to avoid a new nuclear power in the Middle East. "We're in a delicate situation and an obvious impasse," a French diplomat admitted before Wednesday's UN Security Council meeting on suspected Iranian drone use by Russian forces. Despite Tehran's new support for an increasingly isolated Moscow, the United States and the European Union still hope to revive the 2015 deal aimed at curtailing Iran's nuclear programme -- even though the prospect is dimming. "Iran's repression at home and aggression in Ukraine have increased the political cost for and decreased the appetite of the West to grant Tehran sanctions relief," said analyst Ali Vaez of the International Crisis Group. "But the West has no good options, as the only thing worse than a repressive regime that kills its own people is a nuclear armed one that does so."

Data sets are essential for training and validating machine-learning algorithms. But these data are typically sourced from the Internet, so they encode all the stereotypes, inequalities and power asymmetries that exist in society. These biases are exacerbated by the algorithmic systems that use them, which means that the output of the systems is discriminatory by nature, and will remain problematic and potentially harmful until the data sets are audited and somehow corrected. Although this has long been the case, the first major steps towards overcoming the issue were taken only four years ago, when Joy Buolamwini and Timnit Gebru1 published a report that kick-started sweeping changes in the ethics of artificial intelligence (AI). As a graduate student in computer science, Buolamwini was frustrated that commercial facial-recognition systems failed to identify her face in photographs and video footage.

Saildrone Explorer unmanned surface vessels (USV) operate with USS Delbert D. Black (DDG-119) on Oct. 7, 2022. The Navy is thinking about artificial intelligence in two ways: the infrastructure to make unmanned systems work and technology meant to enhance how sailor and their commanders make decisions, a panel of technical and policy experts said Tuesday. The output provided by AI is there help the human or supplement manned operations with unmanned assets, said Brett Vaughan, Navy Chief AI Officer, speaking at the U.S. Naval Institute on Tuesday. A human will always be in the loop and play a central role. "By and large, the AI is there to augment and provide a human decision maker a range of options and recommendations," Vaughan said.