SDNist are intended as tools to encourage investigation and discussion of deiden-tification algorithms, and they are not intended or suitable for product evaluation. The National Institute of Standards and Technology does not endorse any algorithm included in these resources.

Deidentification algorithms are vulnerable to the same bias and privacy issues that impact other data analytics and machine learning applications, and it can even amplify those issues by contaminating downstream applications.

SDNist are intended as tools to encourage investigation and discussion of deiden-tification algorithms, and they are not intended or suitable for product evaluation. The National Institute of Standards and Technology does not endorse any algorithm included in these resources.

Attention is the critical component of a transformer. Yet the quadratic computational complexity of vanilla full attention in the input size and the inability of its linear attention variant to focus have been challenges for computer vision tasks. We provide a mathematical definition of generalized attention and formulate both vanilla softmax attention and linear attention within the general framework. We prove that generalized attention disperses, that is, as the number of keys tends to infinity, the query assigns equal weights to all keys. Motivated by the dispersion property and recent development of Mamba form of attention, we design Scalable and Efficient Mamba like Attention (SEMA) which utilizes token localization to avoid dispersion and maintain focusing, complemented by theoretically consistent arithmetic averaging to capture global aspect of attention. We support our approach on Imagenet-1k where classification results show that SEMA is a scalable and effective alternative beyond linear attention, outperforming recent vision Mamba models on increasingly larger scales of images at similar model parameter sizes.

Fox News' Jonathan Hunt reports the latest on the anti-ICE riots in Los Angeles. Correspondent Rich Edson details Dems' response to Trump deploying the National Guard and'Outnumbered' co-host Kayleigh McEnany weighs in on the escalation. Dozens of protesters have been arrested following a weekend of violence across Los Angeles as tensions hit a boiling point over immigration raids throughout the city. On Sunday, law enforcement officials from multiple agencies arrested 41 protesters as anti-Immigration and Customs Enforcement (ICE) demonstrations spiraled out of control. Of the nearly four-dozen arrests, 21 were made by the Los Angeles Police Department (LAPD), 19 by California Highway Patrol and one by the Los Angeles Sheriff's Department.

Researchers at the University of Washington developed small robotic devices that can change how they move through the air by "snapping" into a folded position during their descent. Shown here is a timelapse photo of the "microflier" falling in its unfolded state, which makes it tumble chaotically and spread outward in the wind. On a cool afternoon at the heart of the University of Washington's campus, autumn, for a few fleeting moments, appears to have arrived early. Tiny golden squares resembling leaves flutter then fall, switching from a frenzied tumble to a graceful descent with a snap. Aptly named "microfliers" and inspired by Miura-fold origami, these small robotic devices can fold closed during their descent after being dropped from a drone.

Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points which are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm's performance is demonstrated in comparison to the state-of-the-art approaches, such as Markov Chain, LLPD, and Disperse, on several synthetic and real astronomical datasets stemming from an N-body simulation of a cosmological volume.

Robots have become key players during the coronavirus pandemic and United Airlines is using one to disinfect planes with the hopes of encouraging people to take flight again. The firm has designed a 100-pound robot to disperse an antimicrobial spray that forms a long-lasting bond on surfaces and hinders the growth of microbes. Called NovaRover, the system is equipped with an omnidirectional nozzle atop a poll that sprays the fine mist in six directions as it rolls down the aisle. United explains that it only takes one and a half minutes to disinfect a majority of planes and is set to release these robots at 10 airports across the US that help during a craft's'deepest cleaning' overnight. Robots are being used across the US and worldwide to help limit the spread of the lingering coronavirus.

Once considered futuristic, artificial intelligence has been made more accessible and integral to contractors in recent years by several burgeoning startups, through programs such as OpenSpace and Smartvid.io. Alice, an AI-based simulation platform for construction, announced yesterday that it has closed $8 million in Series A funding round led by Merus Capital with participation from Foundamental, Blackhorn Ventures and Lightspeed Venture Partners. Founded by Stanford scholar and inventor Rene Morkos in 2018, Alice uses AI to help analyze the flows of labor and equipment throughout a jobsite. In use by firms such as DPR Construction, Mortenson and Parsons, the technology allows users to explore a variety of building plans in real-time and understand the impact of key construction decisions on project cost and duration. The firm says that its customers have cut project duration by 16% and labor costs by nearly 15%.