Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant's influence over the event culmination.

叙事中的事件可以通过其参与者的基本状态理解为一致的整体。通常，这些参与者在叙述中没有明确提及，而是通过常识性或推论填写。理解叙述的模型应该能够推断出这些隐性参与者状态，以及有关这些状态对叙事的影响的原因。为了促进这一目标，我们介绍了一个新的众包参与者指出的数据集意大利面。该数据集包含有效的，可推断的参与者状态；对国家的反事实扰动；如果反事实是真实的，那么故事的变化将是必要的。我们介绍了三项基于州的推理任务，这些任务测试了一个故事何时由故事启用，修改一个反事实状态的故事，并解释给定经过修订的故事的最有可能的状态变化。我们的基准测试实验表明，尽管当今的LLM能够在某种程度上推理有关州的推理，但仍有很大的改进空间，这表明了未来研究的潜在途径。

许多度量学习任务，例如三胞胎学习，最近的邻居检索和可视化，主要是将最终度量是欧几里得距离的某种变体（例如余弦或玛哈拉诺省）的嵌入任务，并且算法必须学会嵌入点进入预选空间。通常不探索对非欧国人几何形状或适当性的研究，我们认为这是由于缺乏学习非欧盟距离距离的工具所致。在认为使用不对称方法特别研究的情况下，我们提出了一种通过输入凸神经网络以可微分方式学习任意伯格曼分歧的新方法。在一组新的和先前研究的任务中，包括不对称回归，排名和聚类，我们证明我们的方法比以前的布雷格曼学习方法更忠实地学习分歧。为此，我们获得了学习神经差异的第一种方法，并继承了布雷格曼分歧的许多不错的数学特性，为更好地发展和研究不对称距离学习提供了基础和工具。

学习了解连接自然语言的基础语言，是一个关键的研究区域。在接地语言习得中的事先工作主要集中在文本输入上。在这项工作中，我们展示了对配对的视觉感知和原始语音输入进行接地语言习得的可行性。这将允许从最终用户学习新的任务和环境的语言，从而减少对文本输入的依赖性，并且可能减轻广泛可用语音识别系统中发现的人口统计偏差的影响。我们利用最近在自我监督的语言表演模型中的工作，并表明学习的言论表示可以使语言接地系统更加包容，同时保持甚至增加一般性。

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.

Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation. Methods: We present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level. Results: We validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below 1{\deg}. We further illustrate how TAToo may be used in a surgical navigation setting. Conclusion: We present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.

As the number of distributed services (or microservices) of cloud-native applications grows, resource management becomes a challenging task. These applications tend to be user-facing and latency-sensitive, and our goal is to continuously minimize the amount of CPU resources allocated while still satisfying the application latency SLO. Although previous efforts have proposed simple heuristics and sophisticated ML-based techniques, we believe that a practical resource manager should accurately scale CPU resources for diverse applications, with minimum human efforts and operation overheads. To this end, we ask: can we systematically break resource management down to subproblems solvable by practical policies? Based on the notion of CPU-throttle-based performance target, we decouple the mechanisms of SLO feedback and resource control, and implement a two-level framework -- Autothrottle. It combines a lightweight learned controller at the global level, and agile per-microservice controllers at the local level. We evaluate Autothrottle on three microservice applications, with both short-term and 21-day production workload traces. Empirical results show Autothrottle's superior CPU core savings up to 26.21% over the best-performing baselines across applications, while maintaining the latency SLO.

Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation should include both generalising to unseen indoor visual scenes as well as generalising to unheard sounding objects. However, previous SAVi task definitions do not include evaluation conditions on truly novel sounding objects, resorting instead to evaluating agents on unheard sound clips of known objects; meanwhile, previous SAVi methods do not include explicit mechanisms for incorporating domain knowledge about object and region semantics. These weaknesses limit the development and assessment of models' abilities to generalise their learned experience. In this work, we introduce the use of knowledge-driven scene priors in the semantic audio-visual embodied navigation task: we combine semantic information from our novel knowledge graph that encodes object-region relations, spatial knowledge from dual Graph Encoder Networks, and background knowledge from a series of pre-training tasks -- all within a reinforcement learning framework for audio-visual navigation. We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects. We show improvements over strong baselines in generalisation to unseen regions and novel sounding objects, within the Habitat-Matterport3D simulation environment, under the SoundSpaces task.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.