传播相位对比度同步同步rotron MicrotoMography（PPC-SR $ {\ mu} $ CT）是对考古遗骸内部结构的非侵入性和非破坏性访问的黄金标准。在该分析中，需要分割虚拟标本以分开不同的部件或材料，通常需要相当多的人力努力的过程。在MicrotoMograph成像（ASEMI）项目的自动分割中，我们开发了一种自动分割这些容量图像的工具，使用手动分段样本来调谐和培训机器学习模型。对于一套四个古埃及动物木乃伊标本，与手动细分切片相比，达到了94-98％的整体准确性，使用深度学习（97-99％）接近现货商业软件的结果较低的复杂性。对分段输出的定性分析表明，我们的结果在对来自深度学习的人的可用性方面接近，证明了这些技术的使用。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

The text-to-image model Stable Diffusion has recently become very popular. Only weeks after its open source release, millions are experimenting with image generation. This is due to its ease of use, since all it takes is a brief description of the desired image to "prompt" the generative model. Rarely do the images generated for a new prompt immediately meet the user's expectations. Usually, an iterative refinement of the prompt ("prompt engineering") is necessary for satisfying images. As a new perspective, we recast image prompt engineering as interactive image retrieval - on an "infinite index". Thereby, a prompt corresponds to a query and prompt engineering to query refinement. Selected image-prompt pairs allow direct relevance feedback, as the model can modify an image for the refined prompt. This is a form of one-sided interactive retrieval, where the initiative is on the user side, whereas the server side remains stateless. In light of an extensive literature review, we develop these parallels in detail and apply the findings to a case study of a creative search task on such a model. We note that the uncertainty in searching an infinite index is virtually never-ending. We also discuss future research opportunities related to retrieval models specialized for generative models and interactive generative image retrieval. The application of IR technology, such as query reformulation and relevance feedback, will contribute to improved workflows when using generative models, while the notion of an infinite index raises new challenges in IR research.

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.

In this paper, we elaborate on the design and discuss the results of a multi-agent simulation that we have developed using the PSI cognitive architecture. We demonstrate that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents. This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents. Our simulation also shows how parameterization can have dramatic effects on agent behavior. Introducing an out-group bias, for example, not only made agents behave aggressively toward members of the other group, but it also increased in-group cohesion. Similarly, environmental and situational factors facilitated the emergence of outliers: agents from adversarial groups becoming close friends. Overall, this simulation showcases the power of psychological frameworks, in general, and the PSI paradigm, in particular, to bring about human-like behavioral patterns in an emergent fashion.

The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99\% and, accordingly, classify Russian trolls and organic users with an AUC of 97\%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

在最初出生在太空行业的基于时间轴的计划方法中，一组状态变量（时间表）的演变受一组时间约束的控制。基于传统时间表的计划系统在整合计划与处理时间不确定性的执行方面表现出色。为了处理一般的非确定主义，最近引入了基于时间轴的游戏的概念。已经证明，发现此类游戏是否存在获胜策略是2Exptime-Complete。但是，缺少合成实施此类策略的控制器的具体方法。本文填补了这一空白，概述了基于时间轴游戏的控制器合成方法。

该技术报告描述了在Robocup SPL（Mario）中计算视觉统计的模块化且可扩展的体系结构，该结构在Robocup 2022的SPL Open Research Challenge期间提出，该挑战在曼谷（泰国）举行。马里奥（Mario）是一个开源的，可用的软件应用程序，其最终目标是为Robocup SPL社区的发展做出贡献。Mario带有一个GUI，该GUI集成了多个机器学习和基于计算机视觉的功能，包括自动摄像机校准，背景减法，同型计算，玩家 +球跟踪和本地化，NAO机器人姿势估计和跌落检测。马里奥（Mario）被排名第一。1在开放研究挑战中。

事实证明，图形神经网络（GNN）在图形结构数据的几个预测建模任务中已被证明。在这些任务中，链接预测是许多现实世界应用（例如推荐系统）的基本问题之一。但是，GNN不能免疫对抗攻击，即精心制作的恶意例子，旨在欺骗预测模型。在这项工作中，我们专注于对基于GNN的链接预测模型进行特定的白盒攻击，其中恶意节点的目的是出现在给定目标受害者的推荐节点列表中。为了实现这一目标，攻击者节点还可以指望它直接控制的其他现有同伴的合作，即在网络中注入许多``vicious''节点的能力。具体而言，所有这些恶意节点都可以添加新的边缘或删除现有的节点，从而扰乱原始图。因此，我们提出了野蛮人，一种新颖的框架和一种安装这种链接预测攻击的方法。野蛮人将对手的目标制定为一项优化任务，从而达到了攻击的有效性与所需的恶意资源的稀疏之间的平衡。在现实世界和合成数据集上进行的广泛实验表明，通过野蛮人实施的对抗性攻击确实达到了很高的攻击成功率，但使用少量恶性节点。最后，尽管这些攻击需要完全了解目标模型，但我们表明它们可以成功地转移到其他黑框方法以进行链接预测。

聊天机器人用于许多应用程序中，例如自动化代理，智能家庭助理，在线游戏中的互动角色等。因此，确保他们不会以不希望的方式行事，对用户提供令人反感或有毒的反应。这并不是一项琐碎的任务，因为最先进的聊天机器人模型是在从互联网公开收集的大型公共数据集上培训的。本文提出了对聊天机器人中毒性的首次大规模测量。我们表明，公开可用的聊天机器人很容易在喂养有毒的查询时提供有毒的反应。更令人担忧的是，一些无毒的查询也会触发有毒反应。然后，我们着手设计和实验攻击，即毒性，该攻击依赖于微调的GPT-2来产生无毒的查询，使聊天机器人以有毒的方式做出反应。我们广泛的实验评估表明，我们的攻击对公共聊天机器人模型有效，并且优于先前工作提出的手动制作的恶意查询。我们还评估了针对毒性的三种防御机制，表明它们要么以影响聊天机器人的效用而降低攻击性能，要么仅有效地减轻了一部分攻击。这强调了对计算机安全和在线安全社区进行更多研究的需求，以确保聊天机器人模型不会伤害其用户。总体而言，我们有信心有毒可以用作审计工具，我们的工作将为设计更有效的聊天机器人安全防御措施铺平道路。