Optical monitoring techniques can be used to identify defects based on layer-wise imaging, but these methods are difficult to scale to high resolutions due to cost and memory constraints. Therefore, we implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate, enabling cost-efficient process monitoring. To do so, a conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images, recovering the distribution of small-scale features and surface roughness. We first evaluate the performance of the model by analyzing the reconstruction quality of the generated images using peak-signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) and wavelet covariance metrics that describe the preservation of high-frequency information. Additionally, we design a framework based upon the Segment Anything foundation model to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples. Finally, we explore the zero-shot generalization capabilities of the implemented framework to other part geometries by creating synthetic low-resolution data.

The emergence of modular satellites marks a significant transformation in spacecraft engineering, introducing a new paradigm of flexibility, resilience, and scalability in space exploration endeavors. In addressing complex challenges such as attitude control, both the satellite's morphological architecture and the controller are crucial for optimizing performance. Despite substantial research on optimal control, there remains a significant gap in developing optimized and practical assembly strategies for modular satellites tailored to specific mission constraints. This research gap primarily arises from the inherently complex nature of co-optimizing design and control, a process known for its notorious bi-level optimization loop. Conventionally tackled through artificial evolution, this issue involves optimizing the morphology based on the fitness of individual controllers, which is sample-inefficient and computationally expensive. In this paper, we introduce a novel gradient-based approach to simultaneously optimize both morphology and control for modular satellites, enhancing their performance and efficiency in attitude control missions. Our Monte Carlo simulations demonstrate that this co-optimization approach results in modular satellites with better mission performance compared to those designed by evolution-based approaches. Furthermore, this study discusses potential avenues for future research.

Federated learning (FL) has emerged as a promising framework for distributed machine learning. It enables collaborative learning among multiple clients, utilizing distributed data and computing resources. However, FL faces challenges in balancing privacy guarantees, communication efficiency, and overall model accuracy. In this work, we introduce CorBin-FL, a privacy mechanism that uses correlated binary stochastic quantization to achieve differential privacy while maintaining overall model accuracy. The approach uses secure multi-party computation techniques to enable clients to perform correlated quantization of their local model updates without compromising individual privacy. We provide theoretical analysis showing that CorBin-FL achieves parameter-level local differential privacy (PLDP), and that it asymptotically optimizes the privacy-utility trade-off between the mean square error utility measure and the PLDP privacy measure. We further propose AugCorBin-FL, an extension that, in addition to PLDP, achieves user-level and sample-level central differential privacy guarantees. For both mechanisms, we derive bounds on privacy parameters and mean squared error performance measures. Extensive experiments on MNIST and CIFAR10 datasets demonstrate that our mechanisms outperform existing differentially private FL mechanisms, including Gaussian and Laplacian mechanisms, in terms of model accuracy under equal PLDP privacy budgets.

Othering, the act of portraying outgroups as fundamentally different from the ingroup, often escalates into framing them as existential threats--fueling intergroup conflict and justifying exclusion and violence. These dynamics are alarmingly pervasive, spanning from the extreme historical examples of genocides against minorities in Germany and Rwanda to the ongoing violence and rhetoric targeting migrants in the US and Europe. While concepts like hate speech and fear speech have been explored in existing literature, they capture only part of this broader and more nuanced dynamic which can often be harder to detect, particularly in online speech and propaganda. To address this challenge, we introduce a novel computational framework that leverages large language models (LLMs) to quantify othering across diverse contexts, extending beyond traditional linguistic indicators of hostility. Applying the model to real-world data from Telegram war bloggers and political discussions on Gab reveals how othering escalates during conflicts, interacts with moral language, and garners significant attention, particularly during periods of crisis. Our framework, designed to offer deeper insights into othering dynamics, combines with a rapid adaptation process to provide essential tools for mitigating othering's adverse impacts on social cohesion.

This paper investigates the vulnerability of bilateral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup enables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly undetectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan.

In an Intelligence Committee hearing with representatives from Google, Apple, and Meta on Wednesday, senators stressed that foreign influence is far from a solved problem. Top officials from Google, Apple, and Meta testified Wednesday before the United States Senate Intelligence Committee about each of their company's ongoing efforts to identify and disrupt foreign influence campaigns ahead of the country's November elections . The hearing, chaired by Senator Mark Warner of Virginia, served largely to impress upon the companies the need for more extensive safeguards against the disinformation campaigns being funded by foreign entities with an eye on influencing US politics. "This is really our effort to try to urge you guys to do more. To alert the public that this has not gone away," Warner said.

AI-generated content doesn't seem to have swayed recent European elections But there's still a risk it could in the future, say researchers. AI-generated falsehoods and deepfakes seem to have had no effect on election results in the UK, France, and the European Parliament this year, according to new research. Since the beginning of the generative-AI boom, there has been widespread fear that AI tools could boost bad actors' ability to spread fake content with the potential to interfere with elections or even sway the results. Such worries were particularly heightened this year, when billions of people were expected to vote in over 70 countries. Those fears seem to have been unwarranted, says Sam Stockwell, the researcher at the Alan Turing Institute who conducted the study . He focused on three elections over a four-month period from May to August 2024, collecting data on public reports and news articles on AI misuse.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Two likely finalists spotted at mayor's mansion From left: Former LAPD Deputy Chief Robert Arcos, LAPD Deputy Chief Emada Tingirides and former Los Angeles County Sheriff Jim McDonnell. Published Sept. 18, 2024 Updated Sept. 19, 2024 10:46 AM PT Mayor Karen Bass said she would conduct a nationwide search for the next chief of the Los Angeles Police Department, but in the end it seems she found three finalists close to home. Deputy Chief Emada Tingirides and Robert "Bobby" Arcos, a former LAPD assistant chief who works in the L.A. County district attorney's office, were seen arriving at Getty House, the mayor's residence, for their candidate interviews over the span of a few hours Tuesday. The third candidate is said to be former Los Angeles County Sheriff Jim McDonnell, who also served in the LAPD, leaving as first assistant chief.

Neuralink says the Food and Drug Administration has designated its experimental Blindsight implant as a "breakthrough device." The company is developing the technology in an attempt to restore blind people's sight. Manufacturers who apply to the FDA's voluntary breakthrough devices program and receive the designation from the agency are granted "an opportunity to interact with FDA experts through several different program options to efficiently address topics as they arise during the premarket review phase." Ultimately, a breakthrough device designation can accelerate development of a technology. Last year, the FDA gave the designation to 145 medical devices.

A Ukrainian drone strike launched at an arms depot in Russia's Tver region caused a massive explosion picked up by NASA space satellites. Ukrainian drones allegedly attacked a massive arms depot some 240 miles west of Moscow on Wednesday, causing an earthquake-sized blast and forcing the evacuation of thousands from the area. Ukrainian officials have not yet claimed responsibility for the attack, though sources in the Security Service of Ukraine (SBU) reportedly confirmed the attack to the Kyiv Independent and other reports pointed to military bloggers and local officials who said Ukrainian drones had been shot down over the Tver region in Russia, where a known military arsenal was located. Video footage obtained by Fox News Digital depicted a huge blast erupting during the early morning hours on Wednesday, though the cause of the explosions or the targets could not be independently verified. A cloud rises after an explosion in Toropets, Tver region, Russia, in this screen grab obtained from a social media video released on Sept. 18, 2024.