与今天使用的空中冲突检测和分辨率（CD \＆R）工具（ATCO）使用的战术冲突检测和分辨率（CD \＆R）相比，密集且复杂的空中交通情况需要更高的自动化水平。但是，空中交通管制（ATC）域（安全至关重要）需要操作员舒适地放弃控制的AI系统，从而确保运营完整性和自动化采用。实现该目标的两个主要因素是解决方案的质量和决策的透明度。本文建议使用在多构想环境中运行的图形卷积加强学习方法，每个代理（飞行）执行CD \＆R任务，并与其他代理共同执行。我们表明，这种方法可以提供有关利益相关者利益（空中交通管制员和空域用户）的高质量解决方案，从而解决了运营透明度问题。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

可以使用具有快速有效分割网络的深度学习方法来实施医疗图像分割。单板计算机（SBC）由于内存和处理限制而难以用于训练深网。诸如Google Edge TPU之类的特定硬件使其适合使用复杂的预训练网络进行实时预测。在这项工作中，我们研究了两个SBC的性能，具有和不进行硬件加速度进行底面图像分割，尽管这项研究的结论可以通过其他类型的医学图像的深层神经网络应用于分割。为了测试硬件加速的好处，我们使用先前已发布的工作中的网络和数据集，并通过使用具有超声甲状腺图像的数据集进行测试来概括它们。我们在SBC中测量预测时间，并将其与基于云的TPU系统进行比较。结果表明，使用Edge TPU，机器学习加速SBC的可行性可加速光盘和杯赛分段，每图像可获得低于25毫秒的时间。

近年来，已经开发了时间序列异常检测算法的特定评估指标来处理经典精度和召回的局限性。但是，这样的指标是作为多个理想方面的总体构建的，引入参数并消除输出的解释性。在本文中，我们首先强调了经典精度/召回的局限性，以及最近基于事件的指标的主要问题 - 例如，我们表明，对手算法可以达到高精度和几乎所有数据集中的回忆在虚弱的假设下。为了应对上述问题，我们根据基于地面真相和预测集之间的``隶属关系''的概念提出了理论上扎根，健壮，无参数和可解释的扩展到精确/回忆指标。我们的指标利用了地面真理和预测之间持续时间的衡量标准，因此具有直观的解释。通过与随机抽样的进一步比较，我们获得了归一化的精度/召回，从而量化了给定的结果一组比随机基线预测更好。通过构造，我们的方法使有关地面真理事件的本地评估保持了本地，从而实现了细粒度的可视化和算法结果的解释。我们将建议与各种公共时间序列检测数据集，算法和指标进行比较。我们进一步得出了隶属指标的理论特性，这些属性给出了对其行为的明确期望，并确保针对对手策略的稳健性。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

Grasping is an incredible ability of animals using their arms and limbs in their daily life. The human hand is an especially astonishing multi-fingered tool for precise grasping, which helped humans to develop the modern world. The implementation of the human grasp to virtual reality and telerobotics is always interesting and challenging at the same time. In this work, authors surveyed, studied, and analyzed the human hand-grasping behavior for the possibilities of haptic grasping in the virtual and remote environment. This work is focused on the motion and force analysis of fingers in human hand grasping scenarios and the paper describes the transition of the human hand grasping towards a tripod haptic grasp model for effective interaction in virtual reality.