As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up. An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy. However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors.

A look at 2022 cybersecurity takeaways and how to start the New Year right. A major lesson the private and public sectors can take away from 2022 is that as long as an organization or entity uses technology, it's at risk for a cyberattack. And the reality is that as the sun sets on 2022, an organization can't survive without technology. For that reason, cyber threats and cybersecurity are perhaps the most important topics to discuss with the dawn of the New Year--and perhaps every New Year. Trends in AI (artificial intelligence) and ML (machine learning), quantum computing, supply chain management, blockchain, and the IoT (Internet of Things) are all linked by this underlying concern about security. A down economy, lack of a skilled cybersecurity workforce, lagging government policy, a large hybrid workforce, and the use of AI by adversaries will all affect the cyber scene in 2023.

Tehran, Iran – Iran has suspended its morality police as the country continues to deal with two months of protests, the Iranian prosecutor general has suggested. The protests erupted shortly after the death of Mahsa Amini, a 22-year-old woman who was arrested by a unit of the morality police in Tehran for allegedly not adhering to the country's mandatory dress code for women. Speaking on Saturday at an event aimed at "outlining the hybrid war during recent riots", which is how Iranian officials describe alleged foreign influence in the unrest, prosecutor general Mohammad Jafar Montazeri was quoted as saying by local media the morality police operations are over. The morality police "has no connection with the judiciary and was shut down by the same place that it had been launched from in the past", he said, reportedly answering a question on why the morality police has been shut down. There were no other confirmations that work of the patrolling units – officially tasked with ensuring "moral security" in the society – has been terminated.

A Data Scientist extracts insights from raw data and uses them to solve a problem. Data scientists are in high demand as they can help companies make sense of the ever-growing amount of data available. A career in data science can be very rewarding as there are many opportunities for growth and development. The work is interesting, challenging, and intellectually stimulating. Here is a list of the top 10 career prospects in Data Science. Whether you're looking to start a career in data science or want to upgrade your existing skills, you'll need to be prepared to work with massive amounts of data. The demand for data analysts is growing rapidly. While the field has been around for a while, the latest trends and developments show that it is still very much alive and kicking.

The climax culminated with a drone light display of 500 Unmanned Ariel Vehicles (UAVs) illustrating the whimsical characters of the popular mobile game over the Hudson. Rather than applauding the decision, New York lawmakers ostracized the avionic wonders to Jersey. In the words of Democratic State Senator, Brad Hoylman, "Nobody owns New York City's skyline – it is a public good and to allow a private company to reap profits off it is in itself offensive." The complimentary event followed the model of Macy's New York fireworks that have illuminated the Hudson skies since 1958. Unlike the department store's pyrotechnics that release dangerous greenhouse gases into the atmosphere, drones are a quiet climate-friendly choice.

What the near future of artificial intelligence could be.

An ongoing project intends to enable military and other scientists to monitor and even enhance the ways in which a soldier's brain sleeps and, importantly, attains rest and repair. The effort – a collaboration between the U.S. Army Medical Research and Development Command (USAMRDC) Military Operational Medicine Research Program (MOMRP) and scientists and engineers at Rice University (Houston, Texas) – is only one of a group of sensor-driven military projects seeking to create wearable technology to track and improve soldier performance and outcomes. Scientists at the Houston-based university are developing a noninvasive "sleeping cap" that analyzes the glymphatic system, the flow of fluid that is thought to cleanse and rid the brain of common metabolic waste during sleep. The cap will be used to further understand how the human brain deals with that waste, and if that function actually prepares and refreshes people for the next day. A team at Rice University's NeuroEngineering Initiative – together with teams from Rice's Institute of Biosciences and Bioengineering (IBB) and physicians from Houston Methodist Hospital and Baylor College of Medicine in Houston – are developing a lightweight skullcap that can analyze the wearer's glymphatic function and stimulate proper flow to treat sleep disorders and improve wakefulness and day-to-day function.

Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial robustness may be that DNNs are only supervised by category labels and do not have part-based inductive bias like the recognition process of humans. Inspired by a well-known theory in cognitive psychology -- recognition-by-components, we propose a novel object recognition model ROCK (Recognizing Object by Components with human prior Knowledge). It first segments parts of objects from images, then scores part segmentation results with predefined human prior knowledge, and finally outputs prediction based on the scores. The first stage of ROCK corresponds to the process of decomposing objects into parts in human vision. The second stage corresponds to the decision process of the human brain. ROCK shows better robustness than classical recognition models across various attack settings. These results encourage researchers to rethink the rationality of currently widely-used DNN-based object recognition models and explore the potential of part-based models, once important but recently ignored, for improving robustness.

Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.

California's Central Valley is the national agricultural center, producing 1/4 of the nation's food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation temporal changes using machine learning techniques. We identified regions with different temporal characteristics of land displacement in that some areas (e.g., Helm) with coarser grain geologic compositions exhibited potentially reversible land deformation (elastic land compaction). We found a significant correlation between InSAR-based land deformation and geologic composition using random forest and deep neural network regression models. We also achieved significant accuracy with 1/4 sparse sampling to reduce any spatial correlations among data, suggesting that the model has the potential to be generalized to other regions for indirect estimation of geologic composition. Our results indicate that geologic composition can be estimated using InSAR-based land deformation data. In-situ measurements of geologic composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution geologic composition estimation utilizing existing measurements.