In Eq. 9, we reduce the soft sparsity constraints to the weighted sum of convolution kernels. Here, we will give a detailed proof of the derivation process. Formally, i denotes the index of the activated group, then s.t. Figure 1: Unified network structure for image restoration (left). In our paper, we claim the additional complexity of our method is negligible. As shown in Figure 1, the structure is stacked by multiple residual blocks and additional convolution layers for input and output.

Newly elected Pope Leo XIV addressed the College of Cardinals in the New Synod Hall at the Vatican on Saturday, May 10. He credits his Papal name choice as a response to the digital age facing the Catholic Church. In his first official remarks as pope, Leo XIV delivered a powerful message to the College of Cardinals on Saturday, warning that artificial intelligence (AI) presents serious new risks to human dignity. He called on the Catholic Church to step up and respond to these challenges with moral clarity and bold action. Speaking at the New Synod Hall, the Pope said the Catholic Church has faced similar moments before.

Pope Leo XIV has held his first meeting with the world's cardinals since his election as the head of the Catholic Church, identifying artificial intelligence (AI) as one of the most crucial issues facing humanity. Leo, the first American pope, laid out a vision of his papacy at the Vatican on Saturday, telling the cardinals who elected him that AI poses challenges to defending "human dignity, justice and labour" – a view shared with his predecessor, the late Pope Francis. Explaining his choice of name, the pontiff said he identified with the late Leo XIII, who had defended workers' rights during his 1878-1903 papacy at the dawn of the industrial age, adding that "social teaching" was now needed in response to the modern-day revolution brought by AI. The late Pope Francis, who died last month, warned that AI risked turning human relations into mere algorithms and called for an international treaty to regulate it. Francis warned the Group of Seven industrialised nations last year that AI must remain human-centric, so that decisions about when to use weapons or even less-lethal tools would not fall to machines.

Instruction-Action (IA) data pairs are valuable for training robotic systems, especially autonomous vehicles (AVs), but having humans manually annotate this data is costly and time-inefficient. This paper explores the potential of using mobile application Global Positioning System (GPS) references and Natural Language Processing (NLP) to automatically generate large volumes of IA commands and responses without having a human generate or retroactively tag the data. In our pilot data collection, by driving to various destinations and collecting voice instructions from GPS applications, we demonstrate a means to collect and categorize the diverse sets of instructions, further accompanied by video data to form complete vision-language-action triads. We provide details on our completely automated data collection prototype system, ADVLAT-Engine. We characterize collected GPS voice instructions into eight different classifications, highlighting the breadth of commands and referentialities available for curation from freely available mobile applications. Through research and exploration into the automation of IA data pairs using GPS references, the potential to increase the speed and volume at which high-quality IA datasets are created, while minimizing cost, can pave the way for robust vision-language-action (VLA) models to serve tasks in vision-language navigation (VLN) and human-interactive autonomous systems.

In 2005, cell phones were banned for the first time during the conclave, the process by which the Catholic Church elects its new pope. Twenty years later, after the death of Pope Francis, the election process is underway again. Authorities have two priorities: to protect the integrity of those attending the meeting, and to ensure that it proceeds in strict secrecy (under penalty of excommunication and imprisonment) until the final decision is made. By 2025, the Gendarmerie corps guarding Vatican City faces unprecedented technological challenges compared to other conclaves. Among them are artificial intelligence systems, drones, military satellites, microscopic microphones, a misinformation epidemic, and a world permanently connected and informed through social media.

Following the death of Pope Francis at the age of 88, the Catholic Church must now begin the lengthy process of electing his successor. Starting at least 15 days after his death, the 135 eligible cardinals will be locked away in the legendary Conclave until they have chosen the next pope. But if you just can't wait for the world's most secretive election to run its course, MailOnline has used AI to predict the result. According to OpenAI's ChatGPT, the man set to become the next head of the Roman Catholic Church is Cardinal Pietro Parolin. As the AI points out, the 70-year-old Italian priest is seen by many as the natural heir to Pope Francis' legacy and holds an edge in current betting markets. ChatGPT said: 'As Vatican Secretary of State since 2013, Parolin is viewed as the "continuity" candidate - acceptable to both reformers and traditionalists.

The evolution of technology has allowed the automation of several processes across diversified engineering industry fields, such as customer support services, which have drastically evolved with the advances in Natural Language Processing and Machine Learning. One of the major challenges of these systems is to identify users intentions, a complex Natural Language Understanding task, that vary across domains. With the evolution of Deep Learning architectures, recent works focused on modelling intentions and creating a taxonomy of intents, so they can be fed to powerful supervised clustering algorithms (Haponchyk et al., 2020; Chatterjee and Sengupta, 2021). However, these systems have the bottleneck of requiring the existence of labelled data to be trained and deployed, and, thus, they can not be easily transferred to real world customer support services, where the available data for a commercial chatbot usually consists in no more than a dataset of interactions between clients and operators. As labeling hundreds of utterances with intent labels can be time-consuming, laborious, expensive and, sometimes, even requires someone with expertise, it is not straightforward to apply current state of the art supervised models to new domains (Chatterjee and Sengupta, 2020).

Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresolved issues concerning i) the temporal consistency of their predictions, and ii) their ability to generalize across datasets. In this context, we propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects. We introduce a new private dataset, named CARDINAL, of apical two-chamber and apical four-chamber sequences, with reference segmentation over the full cardiac cycle. We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. We also report that the best models trained on CARDINAL, when tested on CAMUS without any fine-tuning, still manage to perform competitively with respect to prior methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to finally meet the standards of an everyday clinical device.

The rapid growth of Amazon's warehouse empire slowed this year as e-commerce demand cooled off. But that hasn't dissuaded the company from advancing automation efforts inside its facilities to improve operational efficiency. Amazon unveiled several new warehouse technologies and provided updates to ongoing projects this year. Many are prototypes that have yet to be deployed at scale. Still, the intent is for them to make an impact in the company's logistics network one day.