US to announce'substantial' Russia sanctions The US government will impose a substantial pickup in sanctions against Russia as the fighting in Ukraine continues, according to US Treasury Secretary Scott Bessent. Bessent's comments came just before Nato Secretary-General Mark Rutte was due at the White House, in which he said he hopes to discuss how to deliver Trump's vision of peace in the conflict. Earlier in the day, Rutte said he believes that Trump is the only one who can get this done. At least seven people were killed, including two children, during intense Russian drone and missile strikes on Ukraine - just hours after Trump said plans for a meeting with Vladimir Putin in Budapest had been shelved. Bessent provided no further details on the incoming sanctions, but said they would be announced either after the close this afternoon or first thing tomorrow morning.

Ukrainian President Volodymyr Zelensky has said he would be ready to join Donald Trump and Vladimir Putin at a proposed summit in Hungary if he were invited. The US and Russian presidents announced on Thursday they planned to hold talks on the war in Ukraine in Budapest, possibly in the coming weeks. In comments released on Monday, Zelensky told reporters: If it is an invitation in a format where we meet as three or, as it's called, shuttle diplomacy then in one format or another, we will agree. Meanwhile, media reports have suggested his White House meeting with Trump on Friday descended into a shouting match - with the US side urging Ukraine to accept Russia's terms to end the war. Zelensky was guarded during his first press briefing since the talks, but still his comments made clear there were large areas of disagreement between the two sides.

Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed dataencoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, Figure 1: A high level view of local learning in the proposed shows promising results.

The largest ICT employer in Hungary, Deutsche Telekom IT Solutions (formerly IT-Services Hungary, ITSH) is a subsidiary of the Deutsche Telekom Group. Established in 2006, the company provides a wide portfolio of IT and telecommunications services with more than 5000 employees. ITSH was awarded with the Best in Educational Cooperation prize by HIPA in 2019, acknowledged as one of the most attractive workplaces by PwC Hungary's independent survey in 2021 and rewarded with the title of the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team. We seek our new passionate Tech Lead for our existing MLOps platform.

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated. Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. With the proposed domain randomization method, we could shrink the reality gap to a satisfactory level, achieving 86.32% and 97.38% mAP50 scores, respectively, in the case of zero-shot and one-shot transfers, on our manually annotated dataset containing 190 real images. Our solution fits for industrial use as the data generation process takes less than 0.5 s per image and the training lasts only around 12 h, on a GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar classes of objects by having access to only one real image for training. To our best knowledge, this is the only work thus far satisfying these constraints.

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Every dog owner knows that saying Good dog! in a happy, high-pitched voice will evoke a flurry of joyful tail wagging in their pet. That made scientists curious: What exactly happens in your dog's brain when it hears praise, and is it similar to the hierarchical way our own brain processes such acoustic information? When a person gets a compliment, the more primitive, subcortical auditory regions first reacts to the intonation--the emotional force of spoken words. Next, the brain taps the more recently evolved auditory cortex to figure out the meaning of the words, which is learned. In 2016, a team of scientists discovered that dogs' brains, like those of humans, compute the intonation and meaning of a word separately--although dogs use their right brain hemisphere to do so, whereas we use our left hemisphere.

Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. This lightness of TPGs, both for training and inference, makes them an interesting model to implement Artificial Intelligences (AIs) on embedded systems with limited computational and storage resources. In this paper, we introduce the Gegelati library for TPGs. Besides introducing the general concepts and features of the library, two main contributions are detailed in the paper: 1/ The parallelization of the deterministic training process of TPGs, for supporting heterogeneous Multiprocessor Systems-on-Chips (MPSoCs). 2/ The support for customizable instruction sets and data types within the genetically evolved programs of the TPG model. The scalability of the parallel training process is demonstrated through experiments on architectures ranging from a high-end 24-core processor to a low-power heterogeneous MPSoC. The impact of customizable instructions on the outcome of a training process is demonstrated on a state-of-the-art reinforcement learning environment. CCS Concepts: $\bullet$ Computer systems organization $\rightarrow$ Embedded systems; $\bullet$ Computing methodologies $\rightarrow$ Machine learning.

    Requisition ID: 266525 Work Area: Information Technology Expected Travel: 0 - 10% Career Status: Student Employment Type: Limited Full Time   COMPANY DESCRIPTION SAP started in 1972 as a team of five colleagues with a desire to do something new. Together, they changed enterprise software and reinvented how business was done. Today, as a market leader in enterprise application software, we remain true to our roots. That’s why we engineer solutions to fuel innovation, foster equality and spread...

Dogs, which evolved alongside humans for 10,000 years, are especially attuned to our emotions. Every dog owner knows that saying Good dog! in a happy, high-pitched voice will evoke a flurry of joyful tail wagging in their pet. That made scientists curious: What exactly happens in your dog's brain when it hears praise, and is it similar to the hierarchical way our own brain processes such acoustic information? When a person gets a compliment, the more primitive, subcortical auditory regions first reacts to the intonation--the emotional force of spoken words. Next, the brain taps the more recently evolved auditory cortex to figure out the meaning of the words, which is learned.