Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map generation. In this paper, we try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. The purpose of this is to allow the feature extraction process to potentially stimulate the density map generation process to overcome the annotation noise. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our work points a new direction for follow-up research, which should investigate how to properly relax the overly strict pixel-level spatial invariance for object counting. We evaluate our methods on 4 mainstream object counting networks (i.e., MCNN, CSRNet, SANet, and ResNet-50). Extensive experiments were conducted on 7 popular benchmarks for 3 applications (i.e., crowd, vehicle, and plant counting). Experimental results show that our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects.

Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data from the Environmental Protection Agency and historical wildfire data from 2012 to 2018 have been compiled to establish a comprehensive wildfire database, the largest of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural network was designed with a special architecture for extracting key spatial and temporal features from contiguous environmental parameters indicative of impending wildfires. Environmental and meteorological factors were incorporated into the database and classified as leading indicators and trailing indicators, correlated to risks of wildfire conception and propagation respectively. Additionally, geological data was used to provide better wildfire risk assessment. This novel spatio-temporal neural network achieved >97% accuracy vs. around 76% using traditional convolutional neural networks, successfully predicting 2018's five most devastating wildfires 5-14 days in advance. Finally, a personalized early warning system, tailored to individuals with sensory disabilities or respiratory exacerbation conditions, was proposed. This technique would enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives and reducing economic damages.

Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult as well. For these systems, often computationally inexpensive, but in general inaccurate, models, known as the low-fidelity models, are available. In this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network (DeepONet), a neural network architecture suitable for approximating nonlinear operators. We apply the approach to model systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown complex physical systems.

A Russian spacewalker had to rush back inside the International Space Station on Wednesday when the battery voltage in his spacesuit suddenly dropped. Russian Mission Control ordered Oleg Artemyev, the station commander, to quickly return to the airlock so he could hook his suit to station power. The hatch remained open as his spacewalking partner, Denis Matveev, tidied up outside. NASA said neither man was ever in any danger. Matveev, in fact, remained outside for another hour or so, before he, too, was ordered to wrap it up. Matveev's suit was fine, but Russian Mission Control cut the spacewalk short since flight rules insist on the buddy system.

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defense strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users' trust in AI are emerging on a global scale. However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double-edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats.

"Today, we live in that quadrant of things humans can do and humans are supervising," Dreyer explained. "That is all the [U.S. Food and Drug Administration (FDA)] approved AI stuff that we see today." He said the next step is for AI to move into the realm of superhuman work, such as measuring 1,000 lymph nodes at once, or to make a risk prediction about future events in the next two years based on the patient's prior 40 images, because it looks like a million other patients' scans. Dreyer said the FDA is in discussions with vendors on fully autonomous AI for radiology applications, but the agency wants to see controls built into the software.

A team of three autonomous patrol robots are now providing security at the Tokyo metropolitan government building. The robots are SQ-2 models produced by Seqsense in Tokyo and they're equipped with multiple cameras that can broadcast video directly to human security personnel at a central location. These robots look decidedly more R2-D2 than Robocop with spinning cameras on their heads that constantly whirl around and can make 3-D maps of their surroundings. The robots are SQ-2 models (seen above) produced by Seqsense in Tokyo and they're equipped with cameras that can broadcast video directly to human security personnel They do still have artificial intelligence capabilities that can detect people and other obstacles to avoid any collisions on the predetermined patrol routes. They even have hand sensors so that people in the space can request help from human guards, but they are not meant to fulfill all the duties of a regular human security guard.

A Russian cosmonaut just dealt with a rare spacesuit problem. As CNN's Jackie Wattles observed, mission control ordered Oleg Artemyev back to the International Space Station's airlock after encountering a suit issue. While the exact nature of the trouble wasn't clear as of this writing, NASA commentators noted a "slight fluctuation" in the suit's battery power. Artemyev returned safely, plugged into the station's power supply and resumed operations. We've asked NASA for comment.

This article reports the results of a study that follows a multi-year, pragmatic clinical trial in a real world, community based primary care. What started as a quality project evolved to include the development and deployment of Artificial Intelligence (AI) decision support to guide medication choices when treating hypertension (HTN). Results show that primary care physicians significantly improved HTN outcomes as compared to the national average of success. All patients with a hypertension diagnosis were tracked across three years–including the COVID pandemic period. Of the 13, 441 HTN patients 94% had a blood pressure "at goal" (i.e. less than 140/90)–as of their last clinician visit. The last published study of US blood pressure control which occurred prior to the pandemic was 44%. Because the use of AI in primary care is novel as of this writing, the concept of AI is often unfamiliar to many practicing clinicians and medical group leaders.

Powered by Solidus AI Tech, our utility token AITECH is the World's First Artificial Intelligence utility token to be used by Governmental Authorities, Corporations, SMEs, Metaverse & Play2Earn projects for Artificial Intelligence services and high- performance computing power. We are now entering the Metaverse and the Play2Earn gaming space which, according to a 2021 Bloomberg report, is valued at $500bn with huge growth predictions. Meta Battlebots is a collection of 10,000 NFTS with rarity based their attributes and US Military rankings. Your battlebots will give you early access to the Metaverse Play2Earn game that we are currently building on Unreal Engine 5. Battlebots holders will also receive additional tokens for winning battles and discounts on upgrades in the game. We anticipate that our game will go live in 2023.