Ensuring reliable model performance across diverse domains is a critical challenge in computational pathology. A particular source of variability in Whole-Slide Images is introduced by differences in digital scanners, thus calling for better scanner generalization. This is critical for the real-world adoption of computational pathology, where the scanning devices may differ per institution or hospital, and the model should not be dependent on scanner-induced details, which can ultimately affect the patient's diagnosis and treatment planning. However, past efforts have primarily focused on standard domain generalization settings, evaluating on unseen scanners during training, without directly evaluating consistency across scanners for the same tissue. To overcome this limitation, we introduce SCORPION, a new dataset explicitly designed to evaluate model reliability under scanner variability. SCORPION includes 480 tissue samples, each scanned with 5 scanners, yielding 2,400 spatially aligned patches. This scanner-paired design allows for the isolation of scanner-induced variability, enabling a rigorous evaluation of model consistency while controlling for differences in tissue composition. Furthermore, we propose SimCons, a flexible framework that combines augmentation-based domain generalization techniques with a consistency loss to explicitly address scanner generalization. We empirically show that SimCons improves model consistency on varying scanners without compromising task-specific performance. By releasing the SCORPION dataset and proposing SimCons, we provide the research community with a crucial resource for evaluating and improving model consistency across diverse scanners, setting a new standard for reliability testing.

Axios is expanding its local newsletter presence from 30 to 34 cities. In its continued pretense of benefiting newsrooms, OpenAI has partnered with Axios in a three-year deal to cover Pittsburgh, Pennsylvania; Kansas City, Missouri; Boulder, Colorado; and Huntsville, Alabama. What does OpenAI get in exchange for its funding? Oh, just the ability to use Axios content to answer users' questions. Like the close to 20 newsrooms that OpenAI has already partnered with, Axios seems to have forgotten that the scorpion did end up stinging the frog.

We introduced the Scorpion Hunting Strategy (SHS), a novel population-based, nature-inspired optimisation algorithm. This algorithm draws inspiration from the hunting strategy of scorpions, which identify, locate, and capture their prey using the alpha and beta vibration operators. These operators control the SHS algorithm's exploitation and exploration abilities. To formulate an optimisation method, we mathematically simulate these dynamic events and behaviors. We evaluate the effectiveness of the SHS algorithm by employing 20 benchmark functions (including 10 conventional and 10 CEC2020 functions), using both qualitative and quantitative analyses. Through a comparative analysis with 12 state-of-the-art meta-heuristic algorithms, we demonstrate that the proposed SHS algorithm yields exceptionally promising results. These findings are further supported by statistically significant results obtained through the Wilcoxon rank sum test. Additionally, the ranking of SHS, as determined by the average rank derived from the Friedman test, positions it at the forefront when compared to other algorithms. Going beyond theoretical validation, we showcase the practical utility of the SHS algorithm by applying it to six distinct real-world optimisation tasks. These applications illustrate the algorithm's potential in addressing complex optimisation challenges. In summary, this work not only introduces the innovative SHS algorithm but also substantiates its effectiveness and versatility through rigorous benchmarking and real-world problem-solving scenarios.

Scorpions' tail joints both bend and twist at once Scorpion tails can simultaneously twist and bend thanks to unusual joints, which could inspire new kinds of robots. A detailed analysis of the scorpion tail reveals that its joints move simultaneously in ways similar to both a door hinge and a rotating wheel, providing for highly precise sting strikes, all the while allowing body tissues to run through its hollow structure. "Nobody has ever seen a joint like this before, so it's really fascinating," says Alice Günther at the University of Rostock in Germany. After investigating dozens of scorpions representing 16 species, Günther and her colleagues ran microscopic computed tomography (CT) scans of the five tail segments of a laboratory-bred adult female Mesobuthus gibbosus scorpion, a species that has tails typical of the vast majority of scorpions. They used the images to create 3D digital and print models that provided more practical views of the arachnid's tail joint, which evolved into its current form 400 million years ago.

In this paper, two novel automatic and real-time systems for the detection and classification of two genera of scorpions found in La Plata city (Argentina) were developed using computer vision and deep learning techniques. The object detection technique was implemented with two different methods, YOLO (You Only Look Once) and MobileNet, based on the shape features of the scorpions. High accuracy values of 88% and 91%, and high recall values of 90% and 97%, have been achieved for both models, respectively, which guarantees that they can successfully detect scorpions. In addition, the MobileNet method has been shown to have excellent performance to detect scorpions within an uncontrolled environment and to perform multiple detections. The MobileNet model was also used for image classification in order to successfully distinguish between dangerous scorpion (Tityus) and non-dangerous scorpion (Bothriurus) with the purpose of providing a health security tool. Applications for smartphones were developed, with the advantage of the portability of the systems, which can be used as a help tool for emergency services, or for biological research purposes. The developed systems can be easily scalable to other genera and species of scorpions to extend the region where these applications can be used. Keywords: computer vision, object detection, scorpion image classification, health security, deep learning.

It may look and move like a scorpion, but this $3,999 chair is the ultimate gaming and work station. A US firm unveiled the Scorpion Computer Cockpit that is powered with a push of a button, allowing the user to transform the design to fit their needs. The'tail' moves from the back of the chair overhead to become a screen mount that holds up to three displays and owners can choose to sit upright or lie down to take a break. Stretching nearly five and a half feet long, it is also equipped with a massage and heating feature so'can enjoy some quality time while you're making yourself look like the ultimate villain.' It may look and move like a scorpion, but this $3,999 chair is the ultimate gaming or work station.

A terrestrial robot that can maneuver rough terrain and scout places is very useful in mapping out unknown areas. It can also be used explore dangerous areas in place of humans. A terrestrial robot modeled after a scorpion will be able to traverse undetected and can be used for surveillance purposes. Therefore, this paper proposes modelling of a scorpion inspired robot and a reinforcement learning (RL) based controller for navigation. The robot scorpion uses serial four bar mechanisms for the legs movements. It also has an active tail and a movable claw. The controller is trained to navigate the robot scorpion to the target waypoint. The simulation results demonstrate efficient navigation of the robot scorpion.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. ESA astronaut Alexander Gerst welcomed a new face to the Columbus laboratory, thanks to the successful commissioning of technology demonstration Cimon. Short for Crew Interactive Mobile CompanioN, Cimon is a 3D-printed plastic sphere designed to test human-machine interaction in space.

Something I was really happy about accomplishing in 2017 was getting more practically involved with modern AI. I've studied a lot of math, which has certainly been fun, but haven't done any practical projects, and therefore have nothing to show for my efforts. To remedy this, in April, I applied for an AI Grant with the aim of building FastText skip-gram models for Kenyan speech. I became a finalist in the first round, but failed to win a grant. Then, this September, I applied to the international fellowship track of a now-complete class on Practical Deep Learning for Coders, Part 1 v2, taught by Jeremy Howard of fast.ai.

A'scorpion-milking' robot has been developed to extract venom from the arachnids faster and more safely for use in cancer research. Scorpion venom is normally milked by toxicologists manually, a dangerous procedure where one wrong move can prove deadly. The new machine allows researchers to strap scorpions into an extractor, reducing their contact time with the beasts and making venom extraction safer. A'scorpion-milking' robot (pictured) has been developed to extract venom from the arachnids faster and more safely for use in cancer research. Current scorpion-milking methods can be dangerous both for the animals, due to punctures made to the venom gland or damage to the abdomen, and to the researchers, due to electric shocks from the equipment.