Russian shelling has killed seven people, including a 23-day-old infant, and wounded 20 others in Ukraine's southern region of Kherson, prompting local officials to declare a day of mourning. Kyiv reclaimed part of Kherson from Russian occupation last November, but Kremlin troops have continued shelling the regional capital and areas around it from across the Dnipro River. A couple, their 23-day-old child and another man were killed in the village of Shyroka Balka, Interior Minister Ihor Klymenko said on Sunday. The couple's 12-year-old son was critically wounded and died in hospital. "The terrorists will never willingly stop killing civilians," Klymenko wrote in a Telegram post.

Fox News senior national security correspondent Jennifer Griffin has the latest on why the Army is expected to fall short of its 2023 goal on'America Reports.' Since the first brave Americans took up arms to claim their freedom in the War of Independence, our proud military tradition has sustained our nation and kept us safe. Today, by some accounts, this tradition is in danger of dying out. Some of the loudest alarm bells are coming from the armed forces themselves. The Wall Street Journal reports that most branches expect to miss their recruitment targets this year by significant margins – 15,000 for the Army, 10,000 for the Navy and 3,000 for the Air Force. The Marine Corps is on track to meet its quota, but Marine officials still described a "challenging" recruiting climate.

We introduce generative interpretation, a new approach to estimating contractual meaning using large language models. As AI triumphalism is the order of the day, we proceed by way of grounded case studies, each illustrating the capabilities of these novel tools in distinct ways. Taking well-known contracts opinions, and sourcing the actual agreements that they adjudicated, we show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties' agreements. We also illustrate how models can calculate the probative value of individual pieces of extrinsic evidence. After offering best practices for the use of these models given their limitations, we consider their implications for judicial practice and contract theory. Using LLMs permits courts to estimate what the parties intended cheaply and accurately, and as such generative interpretation unsettles the current interpretative stalemate. Their use responds to efficiency-minded textualists and justice-oriented contextualists, who argue about whether parties will prefer cost and certainty or accuracy and fairness. Parties--and courts--would prefer a middle path, in which adjudicators strive to predict what the contract really meant, admitting just enough context to approximate reality while avoiding unguided and biased assimilation of evidence. As generative interpretation offers this possibility, we argue it can become the new workhorse of contractual interpretation.

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.

The Michigan Robotics Undergraduate Program owes a tremendous debt of gratitude to many people across our Robotics Institute and Robotics Department, the University of Michigan, the College of Engineering, the State of Michigan, and the greater national and global robotics community. Creating a first-of-a-kind robotics program is an incredibly bold and daring undertaking that would not be possible without the support, contributions, empathy, and insights from all corners of our amazing university (Go Blue!). While it would be impossible to recognize everyone who played important roles in realizing the Robotics Major, we would like to acknowledge some individuals who were especially critical to the formation of the program. We must first thank Dean Alec Gallimore and the College of Engineering for their visionary leadership throughout our evolution. Under the guidance and stewardship of Dean Gallimore, the Robotics Institute was able to grow, thrive, and prove it has the right stuff to become a viable academic department and undergraduate program. None of this would be possible without your confidence in us and willingness to innovate for the Common Good. The Robotics Institute owes its origins to Dawn Tilbury - the founding Director of the Robotics Institute (in 2014 under Dean David Munson) and now the inaugural Chair of the Robotics Department - and her foresight to envision what has become the home of Michigan Robotics - the Ford Motor Company Robotics Building. Nadine Sarter, Associate Dean Michael Wellman, and the Robotics Future Committee did tremendous work between 2018-20 to explore the potential and opportunities for Michigan to establish a department and undergraduate program in robotics. Their work identified the path for Michigan to earn distinguished leadership in robotics.

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.

Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite exhibiting superb performance over many problems, their black-box nature still poses a significant challenge with respect to explainability. Indeed, explainable artificial intelligence (XAI) is crucial in several fields, wherein the answer alone -- sans a reasoning of how said answer was derived -- is of little value. This paper uncovers a troubling property of explanation methods for image-based DNNs: by making small visual changes to the input image -- hardly influencing the network's output -- we demonstrate how explanations may be arbitrarily manipulated through the use of evolution strategies. Our novel algorithm, AttaXAI, a model-agnostic, adversarial attack on XAI algorithms, only requires access to the output logits of a classifier and to the explanation map; these weak assumptions render our approach highly useful where real-world models and data are concerned. We compare our method's performance on two benchmark datasets -- CIFAR100 and ImageNet -- using four different pretrained deep-learning models: VGG16-CIFAR100, VGG16-ImageNet, MobileNet-CIFAR100, and Inception-v3-ImageNet. We find that the XAI methods can be manipulated without the use of gradients or other model internals. Our novel algorithm is successfully able to manipulate an image in a manner imperceptible to the human eye, such that the XAI method outputs a specific explanation map. To our knowledge, this is the first such method in a black-box setting, and we believe it has significant value where explainability is desired, required, or legally mandatory.

While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of SmartGD, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Hackers are competing to be the first to crack into a U.S. government satellite in a contest administrated by the Pentagon. Officials with the Air Force and Space Force organized the Las Vegas competition to hack into a functioning satellite currently orbiting the globe, which will pay out a $50,000 first prize. Five teams are participating in the "Hack-a-Sat" competition, which will provide national defense agencies with insight into cybersecurity weak points and advanced infiltration tactics.

It looks like 2024 will be a pivotal year for democracy. There are elections taking place all over the free world – in South Africa, Ghana, Tunisia, Mexico, India, Austria, Belgium, Lithuania, Moldova and Slovakia, to name just a few. Of these, the last may be the most pivotal because: Donald Trump is a racing certainty to be the Republican candidate; a significant segment of the voting population seems to believe that the 2020 election was "stolen"; and the Democrats are, well… underwhelming. The consequences of a Trump victory would be epochal. It would mean the end (for the time being, at least) of the US experiment with democracy, because the people behind Trump have been assiduously making what the normally sober Economist describes as "meticulous, ruthless preparations" for his second, vengeful term.