The protection of non-combatants in times of (fully) autonomous warfare raises the question of the timeliness of the international protective emblem. Incidents in the recent past indicate that it is becoming necessary to transfer the protective emblem to other dimensions of transmission and representation. (Fully) Autonomous weapon systems are often launched from a great distance to the aiming point and there may be no possibility for the operators to notice protective emblems at the point of impact. In this case, the weapon system would have to detect such protective emblems and, if necessary, disintegrate autonomously or request an abort via human-in-the-loop. In our paper, we suggest ways in which a cross-frequency protective emblem can be designed. On the one hand, the technical deployment, e.g. in the form of RADAR beacons, is considered, as well as the interpretation by methods of machine learning. With regard to the technical deployment, possibilities are considered to address different sensors and to send signals out as resiliently as possible. When considering different signals, approaches are considered as to how software can recognise the protective emblems under the influence of various boundary conditions and react to them accordingly. In particular, a distinction is made here between the recognition of actively emitted signals and passive protective signals, e.g. the recognition of wounded or surrendering persons via drone-based electro-optical and thermal cameras. Finally, methods of distribution are considered, including encryption and authentication of the received signal, and ethical aspects of possible misuse are examined.

Selective forgetting or removing information from deep neural networks (DNNs) is essential for continual learning and is challenging in controlling the DNNs. Such forgetting is crucial also in a practical sense since the deployed DNNs may be trained on the data with outliers, poisoned by attackers, or with leaked/sensitive information. In this paper, we formulate selective forgetting for classification tasks at a finer level than the samples' level. We specify the finer level based on four datasets distinguished by two conditions: whether they contain information to be forgotten and whether they are available for the forgetting procedure. Additionally, we reveal the need for such formulation with the datasets by showing concrete and practical situations. Moreover, we introduce the forgetting procedure as an optimization problem on three criteria; the forgetting, the correction, and the remembering term. Experimental results show that the proposed methods can make the model forget to use specific information for classification. Notably, in specific cases, our methods improved the model's accuracy on the datasets, which contains information to be forgotten but is unavailable in the forgetting procedure. Such data are unexpectedly found and misclassified in actual situations.

WASHINGTON – President Donald Trump unveiled the logo for the U.S. Space Force on Friday, attracting critics who said America's newest military branch had boldly gone where "Star Trek" went before. With a central symbol resembling an arrowhead, ringed by an orbiting object and set to a starry backdrop, many people argued the design was pilfered from the famous science fiction franchise. But a spokesman for the branch hit back, arguing that the "Delta" emblem had been used by U.S. Air Force space organizations as early as 1961, before the first Star Trek show aired. The emblem also closely resembles the "widget" logo adopted by Delta Air Lines in 1959. "After consultation with our Great Military Leaders, designers, and others, I am pleased to present the new logo for the United States Space Force, the Sixth Branch of our Magnificent Military!" wrote Trump of the branch he championed and which came into being in December 2019.