In this work, we address the design of tracking controllers that drive a mechanical system's state asymptotically towards a reference trajectory. Motivated by aerospace and robotics applications, we consider fully-actuated systems evolving on the broad class of homogeneous spaces (encompassing all vector spaces, Lie groups, and spheres of any finite dimension). In this setting, the transitive action of a Lie group on the configuration manifold enables an intrinsic description of the tracking error as an element of the state space, even in the absence of a group structure on the configuration manifold itself (e.g., for $\mathbb{S}^2$). Such an error state facilitates the design of a generalized control policy depending smoothly on state and time, which drives the geometric tracking error to a designated origin from almost every initial condition, thereby guaranteeing almost global convergence to the reference trajectory. Moreover, the proposed controller simplifies elegantly when specialized to a Lie group or the n-sphere. In summary, we propose a unified, intrinsic controller guaranteeing almost global asymptotic trajectory tracking for fully-actuated mechanical systems evolving on a broad class of manifolds. We apply the method to an axisymmetric satellite and an omnidirectional aerial robot.

Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs. We further implement a tool, LogConfigLocalizer, aligned with the design of the aforementioned strategy, hoping to assist end-users in coping with configuration errors through log analysis. To the best of our knowledge, this is the first work to localize the root-cause configuration properties for end-users based on Large Language Models~(LLMs) and logs. We evaluate the proposed strategy on Hadoop by LogConfigLocalizer and prove its efficiency with an average accuracy as high as 99.91%. Additionally, we also demonstrate the effectiveness and necessity of different phases of the methodology by comparing it with two other variants and a baseline tool. Moreover, we validate the proposed methodology through a practical case study to demonstrate its effectiveness and feasibility.

While ransomware attempts to exploit your data for hackers' financial gain, for many businesses, the worst-case scenario is that their financial data becomes compromised. A financial data breach can cause your audience to distrust you or blame you for exposing their sensitive data to hackers. There are material and reputational damages to your business model. Fortunately, AI can bolster your cybersecurity efforts by keeping financial data safe. Sophisticated hackers have a variety of tools in their arsenal when it comes to obtaining your financial data.

The group, led by Guillaume Cabanac at the University of Toulouse in France, could not understand why researchers would use the terms'counterfeit consciousness', 'profound neural organization' and'colossal information' in place of the more widely recognized terms'artificial intelligence', 'deep neural network' and'big data'. Further investigation revealed that these strange terms -- which they dub "tortured phrases" -- are probably the result of automated translation or software that attempts to disguise plagiarism. And they seem to be rife in computer-science papers. Research-integrity sleuths say that Cabanac and his colleagues have uncovered a new type of fabricated research paper, and that their work, posted in a preprint on arXiv on 12 July1, might expose only the tip of the iceberg when it comes to the literature affected. To get a sense of how many papers are affected, the researchers ran a search for 30 tortured phrases in journal articles indexed in the citation database Dimensions.