神经形态的神经网络处理器，以记忆中的计算横杆阵列的形式，或以亚阈值模拟和混合信号ASIC的形式，有望在基于NN的ML任务的计算密度和能源效率方面具有巨大优势。但是，由于过程变化和内在的设备物理学，这些技术容易出现计算非理想性。通过将参数噪声引入部署模型中，这会降低部署到处理器的网络的任务性能。虽然可以为每个处理器校准每个设备或单独训练网络，但这些方法对于商业部署而言是昂贵且不切实际的。因此，由于网络体系结构和参数的结果，需要替代方法来训练与参数变化固有强大的网络。我们提出了一种新的对抗网络优化算法，该算法在训练过程中攻击网络参数，并在参数变化时促进推断期间的稳健性能。我们的方法引入了正规化术语，惩罚网络对权重扰动的敏感性。我们将与先前产生参数不敏感的方法进行比较，例如辍学，体重平滑和训练过程中引入参数噪声。我们表明，我们的方法产生的模型对目标参数变化更强大，并且对随机参数变化同样强大。与其他方法相比，我们的方法在减肥景观的平坦位置中发现了最小值，这强调了我们技术发现的网络对参数扰动不太敏感。我们的工作提供了一种将神经网络体系结构部署到遭受计算非理想性的推理设备的方法，而性能的损失最少。 ...

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

尖峰神经网络（SNN）为时间信号处理提供了有效的计算机制，尤其是与低功率SNN推理相结合时。历史上很难配置SNN，缺乏为任意任务寻找解决方案的一般方法。近年来，逐渐发芽的优化方法已应用于SNN，并且越来越轻松。因此，SNN和SNN推理处理器为在没有云依赖性的能源约束环境中为商业低功率信号处理提供了一个良好的平台。但是，迄今为止，行业中的ML工程师无法访问这些方法，需要研究生级培训才能成功配置单个SNN应用程序。在这里，我们演示了一条方便的高级管道，用于设计，训练和部署任意的时间信号处理应用程序，向子-MW SNN推理硬件。我们使用用于时间信号处理的新型直接SNN体系结构，使用突触时间常数的金字塔在一系列时间尺度上提取信号特征。我们在环境音频分类任务上演示了这种体系结构，该任务部署在流式传输模式下的Xylo SNN推理处理器上。我们的应用以低功率（<4MUW推理功率）达到了高准确性（98％）和低潜伏期（100ms）。我们的方法使培训和部署SNN应用程序可用于具有通用NN背景的ML工程师，而无需先前的Spiking NNS经验。我们打算将神经形态硬件和SNN成为商业低功率和边缘信号处理应用程序的吸引人选择。

从我们生命的最早几年开始，人类使用语言来表达我们的信念和欲望。因此，能够与人造代理讨论我们的偏好将实现价值一致性的核心目标。然而，今天，我们缺乏解释这种灵活和抽象语言使用的计算模型。为了应对这一挑战，我们考虑在线性强盗环境中考虑社会学习，并询问人类如何传达与行为的偏好（即奖励功能）。我们研究两种不同类型的语言：指令，提供有关所需政策的信息和描述，这些信息提供了有关奖励功能的信息。为了解释人类如何使用这些形式的语言，我们建议他们推理出已知和未知的未来状态：对当前的说明优化，同时描述对未来进行了推广。我们通过扩展奖励设计来考虑对国家的分配来形式化此选择。然后，我们定义了一种务实的听众，该代理人通过推理说话者如何表达自己来侵犯说话者的奖励功能。我们通过行为实验来验证我们的模型，表明（1）我们的说话者模型预测了自发的人类行为，并且（2）我们的务实的听众能够恢复其奖励功能。最后，我们表明，在传统的强化学习环境中，务实的社会学习可以与个人学习相结合并加速。我们的发现表明，从更广泛的语言中的社会学习，特别是，扩大了该领域的目前对指示的关注，以包括从描述中学习 - 是一种有前途的价值一致性和强化学习的有前途的方法。

在本文中，我们为Pavlovian信号传达的多方面的研究 - 一个过程中学到的一个过程，一个代理商通过另一个代理商通知决策的时间扩展预测。信令紧密连接到时间和时间。在生成和接收信号的服务中，已知人类和其他动物代表时间，确定自过去事件以来的时间，预测到未来刺激的时间，并且都识别和生成展开时间的模式。我们调查通过引入部分可观察到的决策域来对学习代理之间的影响和信令在我们称之为霜冻空心的情况下如何影响学习代理之间的影响和信令。在该域中，预测学习代理和加强学习代理被耦合到两部分决策系统，该系统可以在避免时间条件危险时获取稀疏奖励。我们评估了两个域变型：机器代理在七态线性步行中交互，以及虚拟现实环境中的人机交互。我们的结果展示了帕夫洛维亚信号传导的学习速度，对药剂 - 代理协调具有不同时间表示（并且不）的影响，以及颞次锯齿对药剂和人毒剂相互作用的影响方式不同。作为主要贡献，我们将Pavlovian信号传导为固定信号范例与两个代理之间完全自适应通信学习之间的天然桥梁。我们进一步展示了如何从固定的信令过程计算地构建该自适应信令处理，其特征在于，通过快速的连续预测学习和对接收信号的性质的最小限制。因此，我们的结果表明了加固学习代理之间的沟通学习的可行建设者的途径。

我们来看看如何机器学习是获得独立的媒体集合中的项目的性质技术可用于自动嵌入故事写成这样的集合。要做到这一点，我们使用抽取歌曲的节奏，使音乐播放列表遵循叙事弧模型。我们的工作规定了一个开源的工具，使用预训练神经网络模型，以提取一组原始音频文件的全球节奏和应用这些措施，创造一个叙事的播放清单。此工具可在https://github.com/dylanashley/playlist-story-builder/releases/tag/v1.0.0

充电站在开发充电基础设施的区域中的放置是电动汽车未来成功（EV）的关键组成部分。在纽约的奥尔巴尼县，EV人口的预期增加需要额外的充电站，以在整个充电基础设施中保持足够的效率。鉴于预测的充电需求和当前的充电位置，增强学习（RL）的新型应用程序（RL）能够找到新的充电站的最佳位置。影响收费需求预测的最重要因素包括交通密度，EV登记和靠近某些类型的公共建筑。建议的RL框架可以完善并应用于世界各地的城市，以优化充电站的放置。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.