Russian drones struck port and grain storage facilities in and around the Danube River port of Izmail. Ukraine's infrastructure minister Oleksandr Kubrakov said the attacks damaged about 40,000 tonnes of grains expected by countries in Africa. Ukrainian President Volodymyr Zelenskyy accused Russia of waging an attack on "global food security". The Ukrainian governor of Kherson said two people were injured following a Russian attack on a residential area. More than 10 Russian drones were downed during an attack on Kyiv at about 3am (00:00 GMT), Ukrainian officials said.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a common pitfall when applying machine learning to this problem: an inflated estimate of the effect of interventions, due to survivors bias - where the magnitude of inflation may be conditional on heterogeneous confounders in the population. This bias arises simply because in order to receive an intervention after discharge, a person must not have been readmitted in the intervening period. After deriving an expression for the phantom effect due to survivors bias, we controlled for this and other biases within an inherently interpretable model that quilts together linear models using Bayesian multilevel modeling. We identified case management services as being the most impactful for reducing readmissions overall.

Machine learning models are known to be vulnerable to adversarial evasion attacks as illustrated by image classification models. Thoroughly understanding such attacks is critical in order to ensure the safety and robustness of critical AI tasks. However, most evasion attacks are difficult to deploy against a majority of AI systems because they have focused on image domain with only few constraints. An image is composed of homogeneous, numerical, continuous, and independent features, unlike many other input types to AI systems used in practice. Furthermore, some input types include additional semantic and functional constraints that must be observed to generate realistic adversarial inputs. In this work, we propose a new framework to enable the generation of adversarial inputs irrespective of the input type and task domain. Given an input and a set of pre-defined input transformations, our framework discovers a sequence of transformations that result in a semantically correct and functional adversarial input. We demonstrate the generality of our approach on several diverse machine learning tasks with various input representations. We also show the importance of generating adversarial examples as they enable the deployment of mitigation techniques.

This is part II of a two-part paper. Part I presented a universal Birkhoff theory for fast and accurate trajectory optimization. The theory rested on two main hypotheses. In this paper, it is shown that if the computational grid is selected from any one of the Legendre and Chebyshev family of node points, be it Lobatto, Radau or Gauss, then, the resulting collection of trajectory optimization methods satisfy the hypotheses required for the universal Birkhoff theory to hold. All of these grid points can be generated at an $\mathcal{O}(1)$ computational speed. Furthermore, all Birkhoff-generated solutions can be tested for optimality by a joint application of Pontryagin's- and Covector-Mapping Principles, where the latter was developed in Part~I. More importantly, the optimality checks can be performed without resorting to an indirect method or even explicitly producing the full differential-algebraic boundary value problem that results from an application of Pontryagin's Principle. Numerical problems are solved to illustrate all these ideas. The examples are chosen to particularly highlight three practically useful features of Birkhoff methods: (1) bang-bang optimal controls can be produced without suffering any Gibbs phenomenon, (2) discontinuous and even Dirac delta covector trajectories can be well approximated, and (3) extremal solutions over dense grids can be computed in a stable and efficient manner.

Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in this domain, the ability of even the most state-of-the-art methods to defend against adversarial examples remains elusive. While several adversarial defense strategies have been proposed, they typically suffer from constrained practicability due to inevitable trade-offs between universality, effectiveness, and efficiency. To overcome these challenges, we thoroughly delve into the coupled relationship between adversarial detection and face anti-spoofing. Based on this, we propose a robust face anti-spoofing framework, namely AdvFAS, that leverages two coupled scores to accurately distinguish between correctly detected and wrongly detected face images. Extensive experiments demonstrate the effectiveness of our framework in a variety of settings, including different attacks, datasets, and backbones, meanwhile enjoying high accuracy on clean examples. Moreover, we successfully apply the proposed method to detect real-world adversarial examples.

This report outlines the concepts, mechanisms and inner dynamics of the BEAM (Behavior, Energy, Autonomy, and Mobility) modeling framework. BEAM is an open-source large-scale high-resolution transportation model that harnesses the principles of the actor model of computation to build a powerful and efficient agent-based model of travel behavior. It allows a detailed microscopic view of how people make travel choices and interact with the transportation system, enabling more accurate simulations of human mobility and urban transport networks. It also allows the analysis of numerous spatially defined but interacting layers, and integrates them into a cohesive representation of a regional transportation system. This integrated picture provides invaluable insights to policy makers and other stakeholders about how changes to the transportation system result in changes to traffic congestion, mode share, energy use, and emissions throughout a modeled region. These capabilities are demonstrated with a case study of New York City that showcase BEAM's application in a very large and intricate urban transportation system, without relying on existing travel demand models. The unique ability of BEAM to simulate individual behaviors, integrate with other models, and adapt to different real-world scenarios underscores its importance in the rapidly evolving field of transportation and emphasizes its potential as a valuable proof-of-concept tool to contribute to more informed and effective policy and planning decisions.

Unfortunately, the world remains off track in meeting Development Goals (SDGs), especially goal 13, which focuses the Paris Agreement's target of limiting the temperature rise to on combating climate change and its impacts. To mitigate the effects 1.5 C above pre-industrial levels and reaching net-zero emissions of climate change, reducing enterprise Scope 3 (supply chain by 2050 [14], with a projected temperature rise of around 2.7 C emissions) is vital, as it accounts for more than 90% of total emission above pre-industrial levels by 2100 [22]. To achieve these targets, inventories. However, tracking Scope 3 emissions proves challenging, it is critical to engage non-state actors like enterprises, who have as data must be collected from thousands of upstream and pledged to reduce their GHG emissions, and have significant potential downstream suppliers. To address the above mentioned challenges, to drive more ambitious actions towards climate targets than we propose a first-of-a-kind framework that uses domain-adapted governments [9]. However, a lack of high-quality data and insights NLP foundation models to estimate Scope 3 emissions, by utilizing about an enterprise's operational performance can create barriers to financial transactions as a proxy for purchased goods and services.

This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories. Our research is typically inspired by real-world problems and often conducted in interdisciplinary collaborations with research and industry partners. We summarize past experiments and results comprising topics like various ways of knowledge graph construction in corporate and personal settings, Managed Forgetting and (Self-organizing) Context Spaces as a novel approach to Personal Information Management (PIM) and knowledge work support. Past results are complemented by an overview of related work and some of our latest findings not published so far. Last, we give an overview of our related industry use cases including a detailed look into CoMem, a Corporate Memory based on our presented research already in productive use and providing challenges for further research. Many contributions are only first steps in new directions with still a lot of untapped potential, especially with regard to further increasing the automation in PIM and knowledge work support.

Like other extreme quantities (for example, a distance of 34 parsecs), unfamiliar dollar amounts can be hard to fathom without comparison to something else [7, 18]. To address this issue, it can be useful to employ perspectives: re-phrasings of measurements that make them easier to understand, via a change of units to express the focal number on a different scale, or a comparison to a reference object. For instance, $330 billion can be re-expressed using perspectives of "about $1,000 per person in the United States" or "about 5% of the United States Federal Budget". In addition to being intuitively appealing and perceived as helpful [6, 10, 13], perspectives have been shown to aid numerical comprehension by boosting recall, estimation, error detection, and prediction [3, 12, 27], which could find relevance in a wide variety of downstream applications. These demonstrations of the benefits of perspectives have led to questions around what makes some analogies better than others, and if and how one can generate high-quality perspectives at scale for naturally occurring mentions of measurements. Approaches to automated perspective generation have varied, but they generally rely on first constructing a database of reference objects to compare measurements to and then prioritizing analogies to these reference objects that are both familiar and helpful to the reader [10, 27]. Prioritizing reference objects is complicated by the fact that what is most helpful for understanding a measurement can be difficult to quantify and can depend on the context in which the measurement occurs.