Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error planning. Purpose: Develop a stochastic policy-based DRL agent for automatic treatment planning with efficient training, broad applicability, and robustness against adversarial attacks using Fast Gradient Sign Method (FGSM). Methods: Using the Actor-Critic with Experience Replay (ACER) architecture, the agent tunes treatment planning parameters (TPPs) in inverse planning. Training is based on prostate cancer IMRT cases, using dose-volume histograms (DVHs) as input. The model is trained on a single patient case, validated on two independent cases, and tested on 300+ plans across three datasets. Plan quality is assessed using ProKnow scores, and robustness is tested against adversarial attacks. Results: Despite training on a single case, the model generalizes well. Before ACER-based planning, the mean plan score was 6.20$\pm$1.84; after, 93.09% of cases achieved a perfect score of 9, with a mean of 8.93$\pm$0.27. The agent effectively prioritizes optimal TPP tuning and remains robust against adversarial attacks. Conclusions: The ACER-based DRL agent enables efficient, high-quality treatment planning in prostate cancer IMRT, demonstrating strong generalizability and robustness.

When a series of potholes appeared in the city, the town of Oviedo, Florida sent a robotic camera into a storm water pipe to investigate "anomalies" under the roadway. They were shocked to find a 5ft (1.7m) alligator. "At first, they thought it was a toad and in the video, you see two little glowing eyes until you get closer - but when it turned around, they saw the long tail of the alligator and followed it through the pipes," Oviedo city officials said. "Thank goodness our crews have a robot", officials added, warning locals not to wander down into the pipes.

Crews in Oviedo, Florida, were investigating underground pipes for anomalies when their robotic camera ran into a 5-foot alligator. A city crew in Florida spotted a 5-foot alligator lurking in a stormwater pipe while investigating the pipes with a robotic camera last week, officials said Tuesday. The stormwater crew in the city of Oviedo, located about 20 miles northeast of Orlando, was on Lockwood Boulevard to check on a series of potholes that appeared in the roadway on Friday, the city said in a Facebook post. The crew used a four-wheel robotic camera to go into the pipes below the road and investigate any anomalies such as leaking pipes, cracks or other defects underground, officials said. However, crews instead found a different kind of anomaly while searching the underground pipes.

Automunge is a tabular preprocessing library that encodes dataframes for supervised learning. When selecting a default feature encoding strategy for gradient boosted learning, one may consider metrics of training duration and achieved predictive performance associated with the feature representations. Automunge offers a default of binarization for categoric features and z-score normalization for numeric. The presented study sought to validate those defaults by way of benchmarking on a series of diverse data sets by encoding variations with tuned gradient boosted learning. We found that on average our chosen defaults were top performers both from a tuning duration and a model performance standpoint. Another key finding was that one hot encoding did not perform in a manner consistent with suitability to serve as a categoric default in comparison to categoric binarization.

A semi-autonomous prosthesis control based on computer vision can be used to improve performance while decreasing the cognitive burden, especially when using advanced systems with multiple functions. However, a drawback of this approach is that it relies on the complex processing of a significant amount of data (e.g., a point cloud provided by a depth sensor), which can be a challenge when deploying such a system onto an embedded prosthesis controller. In the present study, therefore, we propose a novel method to reconstruct the shape of the target object using minimal data. Specifically, four concurrent laser scanner lines provide partial contours of the object cross-section. Simple geometry is then used to reconstruct the dimensions and orientation of spherical, cylindrical and cuboid objects. The prototype system was implemented using depth sensor to simulate the scan lines and vibrotactile feedback to aid the user during aiming of the laser towards the target object. The prototype was tested on ten able-bodied volunteers who used the semi-autonomous prosthesis to grasp a set of ten objects of different shape, size and orientation. The novel prototype was compared against the benchmark system, which used the full depth data. The results showed that novel system could be used to successfully handle all the objects, and that the performance improved with training, although it was still somewhat worse compared to the benchmark. The present study is therefore an important step towards building a compact system for embedded depth sensing specialized for prosthesis grasping.

The volume of the distribution of weight sets associated with a loss value may be the source of implicit regularization from overparameterization due to the phenomenon of contracting volume with increasing dimensions for geometric figures demonstrated by hyperspheres. We introduce the geometric regularization conjecture and extract to an explanation for the double descent phenomenon by considering a similar property resulting from shrinking intrinsic dimensionality of the distribution of potential weight set updates available along training path, where if that distribution retracts across a volume verses dimensionality curve peak when approaching the global minima we could expect geometric regularization to re-emerge. We illustrate how data fidelity representational complexity may influence model capacity double descent interpolation thresholds. The existence of epoch and model capacity double descent curves originating from different geometric forms may imply universality of closed n-manifolds having dimensionally adjusted n-sphere volumetric correspondence.

Is it possible to learn how to feel? A research study to validate artificial creativity will discover it. Launching in Spanish, Falta Una Palabra- translated as In Need of a Word is a novel written by Dr. Ángel García-Crespo with the help of AI. It tells the love story of Beatriz and Benito, two people looking for a word to describe the nature of their relationship. Beatriz and Benito share a dilemma that feeds their passion, they can't be together or separated.

The first quarter delivered home-sale profits that were more than twice as high as anticipated, the company said. Zillow expected to make money primarily from transaction fees and from services such as title insurance--not from making a killing on the flip. The company's algorithm, which was supposed to predict housing prices, didn't seem to understand the market. Zillow was also behind on its target for home purchases. By the summer, it had the opposite problem, the company later acknowledged.

A report reveals the controversial face recognition software Clearview AI was developed with help from far-right extremists. The Huffington Post claims founder Hoan Ton-That has close ties with several extremists, attended dinners organized by alt-right groups and had employees famous for hate speech. It includes Charles Johnson, owner of a crowdfunding platform for white supremacists and Jack Posobiec, who led the Pizzagate campaign, along with Peter Thiel and his associate Jeff Giesa who is said to donate to alt-right causes, the Huffington Post claims. The report claims to have videos, messages and emails linking the Clearview founder to these individuals as well as evidence suggesting the technology was designed specifically to'identify every illegal alien in the country.' Ton-That has commented on these allegations in an email to DailyMail.com,

Omega Medical Imaging, manufactures of Artificial Intelligence Fluoroscopy/Cine (AIF/C) Imaging systems, just announced the Food and Drug Administration 510 (k) clearance of FluoroShield with their 2020 Cardiac Flat Panel Detector. The unique FluoroShield system allows for auto collimation during interventional fluoro or cine cases while maintaining a perspective of surrounding anatomy. The blended image incorporates a lower frequency refresh of the peripheral image area. This combined image (live fluoroscopy or cine of ROI background refreshed at a rate of once or twice per second) increases the quality of information presented during interventional procedures. Brian Fleming, President of Omega Medical Imaging states, "Until now products on the market have only been able to manage radiation to patients and staff. FluoroShield is the only system in the world that provides an actual reduction in dose. The impact of this groundbreaking solution for patients and healthcare providers is substantial. I am very grateful to be a part of a team that pushes the envelope in the development of safer healthcare solutions."