We present the first comparative study of two fundamentally distinct feature extraction techniques: Histogram of Oriented Gradients (HOG) and Topological Data Analysis (TDA), for medical image classification using retinal fundus images. HOG captures local texture and edge patterns through gradient orientation histograms, while TDA, using cubical persistent homology, extracts high-level topological signatures that reflect the global structure of pixel intensities. We evaluate both methods on the large APTOS dataset for two classification tasks: binary detection (normal versus diabetic retinopathy) and five-class diabetic retinopathy severity grading. From each image, we extract 26244 HOG features and 800 TDA features, using them independently to train seven classical machine learning models with 10-fold cross-validation. XGBoost achieved the best performance in both cases: 94.29 percent accuracy (HOG) and 94.18 percent (TDA) on the binary task; 74.41 percent (HOG) and 74.69 percent (TDA) on the multi-class task. Our results show that both methods offer competitive performance but encode different structural aspects of the images. This is the first work to benchmark gradient-based and topological features on retinal imagery. The techniques are interpretable, applicable to other medical imaging domains, and suitable for integration into deep learning pipelines.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can easily identify if an arm is pointing in some general direction. The Sawyer robotic arm is used for developing and training this intelligent algorithm. Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to cover the entire joint configuration space. In this work, orthogonal arrays are used, similar to the Taguchi method, to efficiently span the joint space with the minimal number of training images. This ``optimally'' generated database is used to train the custom ANN and its degree of accuracy is on average equal to twice the smallest joint displacement step used for database generation. A pre-trained ANN will be useful for estimating the postures of robotic manipulators used on space stations, spacecraft, and rovers as an auxiliary tool or for contingency plans.

Sign up to receive the Future Tense newsletter every other Saturday. I showed up for my first "Flow sesh" feeling sluggish. It was 6 p.m. on a Monday, and I had promised myself I was going to use the time to try to make headway on a writing project I had been putting off all day. But I was also pretty skeptical. Flow Club, a platform for virtual coworking sessions, promises to allow members to "Feel good getting work done."

The safety driver behind the wheel of a self-driving Uber that struck and killed a woman in 2018 has been charged with a crime. Prosecutors in Maricopa County, Arizona, Tuesday said the driver, Rafaela Vasquez, has been indicted for criminal negligence. But Uber, her employer and the company that built the automated system involved in the fatal collision, won't face charges. The attorney for neighboring Yavapai County declined to prosecute Uber last year, writing in a letter that the office found "no basis for criminal liability." Yavapai County attorney Sheila Polk declined to elaborate on her decision.

The US National Highway Traffic Safety Administration is investigating a crash involving a speeding Tesla that killed two people in a Los Angeles suburb, the agency said on Tuesday. Spokesman Sean Rushton would not say whether the Tesla Model S was on Autopilot when it crashed on 29 December in Gardena. That system is designed to automatically change lanes and keep a safe distance from other vehicles. The black Tesla had left a freeway and was moving at a high rate of speed when it ran a red light and slammed into a Honda Civic at an intersection, police said. A man and woman in the Civic died at the scene.

Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs' benefits also introduce a serious potential challenge, in the form of complex interactions with human-driven vehicles (HVs). The emergence of AVs introduces uncertainty in the behavior of human actors and in the impact of the AV manufacturer on autonomous driving design. This paper thus aims to investigate how AVs affect road safety and to design socially optimal liability rules for AVs and human drivers. A unified game is developed, including a Nash game between human drivers, a Stackelberg game between the AV manufacturer and HVs, and a Stackelberg game between the law maker and other users. We also establish the existence and uniqueness of the equilibrium of the game. The game is then simulated with numerical examples to investigate the emergence of human drivers' moral hazard, the AV manufacturer's role in traffic safety, and the law maker's role in liability design. Our findings demonstrate that human drivers could develop moral hazard if they perceive their road environment has become safer and an optimal liability rule design is crucial to improve social welfare with advanced transportation technologies. More generally, the game-theoretic model developed in this paper provides an analytical tool to assist policy-makers in AV policymaking and hopefully mitigate uncertainty in the existing regulation landscape about AV technologies.

A year after the first fatality caused by a fully self-driving car, questions about liability in the event of a death involving the cars are still completely up in the air. Officials announced earlier this week that Uber won't face criminal charges in the death of a pedestrian struck and killed by one of its self-driving cars nearly a year ago in Tempe, Arizona. The Yavapai County Attorney's Office said it conducted a thorough review of the evidence and determined there was no basis for criminal liability against Uber. It did not detail how the decision was made and has declined to answer any questions on the case. The pedestrian was walking a bicycle across a road at night.

Mr. Douma said prosecutors' announcement Tuesday tracked with how typically people, and not car manufacturers, are held responsible for crimes they commit behind the wheel. But, as autonomous vehicles become more sophisticated, he said, such cases raise questions about that way of thinking. "Is this driver, or was this driver, behaving in any way different than what most drivers are going to be behaving like when the car is doing this much driving?" he said. "It's a very conventional way of thinking to say we can expect and we should expect people to sit and monitor technology that is otherwise doing all the decision-making." The Yavapai County Attorney's Office did its review at the request of the Maricopa County Attorney's Office, which had a potential conflict of interest in the case because of an earlier partnership with Uber in a safety campaign.

The Yavapai County Attorney said in a letter made public that there was "no basis for criminal liability" for Uber, but that the back-up driver, Rafaela Vasquez, should be referred to the Tempe police for additional investigation. Prosecutors' decision not to pursue criminal charges removes one potential headache for the ride-hailing company as the company's executives try to resolve a long list of federal investigations, lawsuits and other legal risks ahead of a hotly anticipated initial public offering this year. The crash involved a Volvo XC90 sport utility vehicle that Uber was using to test self-driving technology. The fatal accident was a setback from which the company has yet to recover; its autonomous vehicle testing remains dramatically reduced. The accident was also a blow to the entire autonomous vehicle industry and led other companies to temporarily halt their testing.