Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared to current state of the art supervised approaches. Nevertheless, the results are important, since there was 1) no explicit domain-specific data used - so we show that the transfer to the legal domain is possible for general-purpose LLMs, and 2) the LLMs where directly applied without any further training or fine-tuning - which in turn saves immensely in terms of additional computational costs.

In this paper we propose new probabilistic and dynamic (adaptive) strategies to create multi-method ensembles based on the Coral Reefs Optimization with Substrate Layers (CRO-SL) algorithm. The CRO-SL is an evolutionary-based ensemble approach, able to combine different search procedures within a single population. In this work we discuss two different probabilistic strategies to improve the algorithm. First, we defined the Probabilistic CRO-SL (PCRO-SL), which substitutes the substrates in the CRO-SL population by {\em tags} associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with a similar probability, obtaining this way an ensemble with a more intense change in the application of different operators to a given individual than the original CRO-SL. The second strategy discussed in this paper is the Dynamical Probabilistic CRO-SL (DPCRO-SL), in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned with a higher probability that those which showed a worse performance during the search. We test the performance of the proposed probabilistic and dynamic ensembles in different optimization problems, including benchmark functions and a real application of wind turbines layout optimization, comparing the results obtained with that of existing algorithms in the literature.

Advancements in reinforcement learning (RL) have inspired new directions in intelligent automation of network defense. However, many of these advancements have either outpaced their application to network security or have not considered the challenges associated with implementing them in the real-world. To understand these problems, this work evaluates several RL approaches implemented in the second edition of the CAGE Challenge, a public competition to build an autonomous network defender agent in a high-fidelity network simulator. Our approaches all build on the Proximal Policy Optimization (PPO) family of algorithms, and include hierarchical RL, action masking, custom training, and ensemble RL. We find that the ensemble RL technique performs strongest, outperforming our other models and taking second place in the competition. To understand applicability to real environments we evaluate each method's ability to generalize to unseen networks and against an unknown attack strategy. In unseen environments, all of our approaches perform worse, with degradation varied based on the type of environmental change. Against an unknown attacker strategy, we found that our models had reduced overall performance even though the new strategy was less efficient than the ones our models trained on. Together, these results highlight promising research directions for autonomous network defense in the real world.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 C and 0.5 C in CPU and server inlet temperature respectively.

我们提出协调指导矢量字段，以与机器人团队同时完成两个任务：首先，多个机器人的指导和导航到可能嵌入2D或3D中的可能不同的路径或表面；其次，他们的运动协调在跟踪他们的规定路径或表面时。运动配位是由路径或表面上的机器人之间所需的参数位移定义的。通过控制对应于指导矢量场之间的路径或表面参数的虚拟坐标来实现这种所需的位移。由动力学系统理论和Lyapunov理论支撑的严格数学保证，用于从所有初始位置上有效的分布式运动协调和机器人在路径或表面上导航。作为实用机器人应用的一个例子，我们从所提出的具有驱动饱和度的Dubins-car样模型的指导向量场中得出了一种对照算法。我们提出的算法分布并可扩展到任意数量的机器人。此外，广泛的说明性模拟和固定翼飞机户外实验验证了我们算法的有效性和鲁棒性。

为了了解材料特性的起源，三轴光谱仪（TAS）处的中子散射实验通过测量其动量（Q）和能量（E）空间中的强度分布来研究样品中的磁和晶格激发。但是，TAS实验的高需求和有限的光束时间可用性提出了自然的问题，即我们是否可以提高其效率或更好地利用实验者的时间。实际上，使用TAS，有许多科学问题需要在Q-E空间的特定区域中搜索感兴趣的信号，但是当手动完成时，这是耗时且效率低下的，因为测量点可能会放置在此类的无信息区域中作为背景。主动学习是一种有前途的通用机器学习方法，可以迭代地检测自主信号的信息区域，即不受人类干扰，从而避免了不必要的测量并加快实验。此外，自主模式允许实验者在此期间专注于其他相关任务。我们在本文中描述的方法利用了对数高斯过程，由于对数转换，该过程在信号区域中具有最大的近似不确定性。因此，将不确定性最大化为采集功能，因此直接产生了信息测量的位置。我们证明了我们方法对在Themal Tas Eiger（PSI）进行真实中子实验的结果的好处，以及在合成环境中基准的结果，包括许多不同的激发。

近年来，薄弱的监督已应用于各种自然语言理解任务。由于技术挑战范围缩小了较弱的长期文档的监督，跨越了数百页，因此在文档理解空间中的应用程序受到限制。在Lexion，我们建立了一个针对长格式（长10-200页）PDF文档量身定制的基于监督的薄弱系统。我们使用此平台来构建数十种语言理解模型，并成功地应用于从商业协议到公司编队文件的各个领域。在本文中，我们在有限的时间，劳动力和培训数据的情况下，通过弱监督进行监督学习的有效性。我们在一周的时间内建立了8个高质量的机器学习模型，借助一小组组成的小组，只有3个注释者与300个文档的数据集一起工作。我们分享有关我们的整体体系结构，如何利用弱监督以及能够实现的结果的一些细节。我们还包括想要尝试替代方法或完善我们的研究人员的数据集。此外，我们阐明了使用PDF格式扫描不良的长格式文档时出现的其他复杂性，以及一些有助于我们在此类数据上实现最新性能的技术。