The Delta variant of the coronavirus spread to more countries in recent weeks, and the total number of cases officially logged soared past half a million per day. The global number of deaths is now about two-thirds as high as it was at the peak of the previous wave, in April of this year. As the virus spreads, the potential rises for a vaccine-resistant strain to emerge. Meanwhile, in poorer countries, vaccines are scarce, and most populations are little protected (exhibit).

A study of bird songs conducted in the Sierra Nevada mountain range in California generated a million hours of audio, which AI researchers are working to decode to gain insights into how birds responded to wildfires in the region, and to learn which measures helped the birds to rebound more quickly. Scientists can also use the soundscape to help track shifts in migration timing and population ranges, according to a recent account in Scientific American. More audio data is coming in from other research as well, with sound-based projects to count insects and study the effects of light and noise pollution on bird communities underway. "Audio data is a real treasure trove because it contains vast amounts of information," stated ecologist Connor Wood, a Cornell University postdoctoral researcher, who is leading the Sierra Nevada project. "We just need to think creatively about how to share and access that information."

3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding. Previous works in this field either require high-quality 3D human scans or sequential point clouds, which cannot be easily applied to low-quality 3D scans captured by consumer-level depth sensors. In this paper, we make the first attempt to reconstruct reliable 3D human shapes from single-frame partial point clouds.To achieve this, we propose an end-to-end learnable method, named VoteHMR. The core of VoteHMR is a novel occlusion-aware voting network that can first reliably produce visible joint-level features from the input partial point clouds, and then complete the joint-level features through the kinematic tree of the human skeleton. Compared with holistic features used by previous works, the joint-level features can not only effectively encode the human geometry information but also be robust to noisy inputs with self-occlusions and missing areas. By exploiting the rich complementary clues from the joint-level features and global features from the input point clouds, the proposed method encourages reliable and disentangled parameter predictions for statistical 3D human models, such as SMPL. The proposed method achieves state-of-the-art performances on two large-scale datasets, namely SURREAL and DFAUST. Furthermore, VoteHMR also demonstrates superior generalization ability on real-world datasets, such as Berkeley MHAD.

Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

The nanotechnology is the science, engineering and technology that are developed to nano-scale, around 1 to 100 nanometers. One of nanotechnology main applications is the nanobots, machines that can construct and handle objects at an atomic level and that are capable of moving through the circulatory system.1 The nanotechnology has become a billionaire industry and since it has multiple potential applications in human beings, there is a great interest in human experimentation. However, the nanotechnology acts at atomic level and for that reason the experimentation in humans is high risk, which causes an evident lack of volunteers. Therefore, the transnational nanotechnology companies would be resorting to criminal methods to get human experimentation subjects; thus, they would be using violence, swindle, extortion and organized crime.2–4 Recent researches reveal evidences that the technological transnational companies, in illicit association with USA, European Community and China governments and the corrupt Latin American governments, have created an organization that is developing mainly in Latin America a secret, forced and illicit neuroscientific human experimentation with invasive neurotechnology, brain nanobots, microchips and implants to execute neuroscientific projects,2–5 which can have even led scientists to win Medicine Nobel Prizes6 based on this illicit human experimentation at the expense of Latin Americans' health.

AI that understands the world from a first-person point of view could unlock a new era of immersive experiences, as devices like augmented reality (AR) glasses and virtual reality (VR) headsets become as useful in everyday life as smartphones. Imagine your AR device displaying exactly how to hold the sticks during a drum lesson, guiding you through a recipe, helping you find your lost keys, or recalling memories as holograms that come to life in front of you. To build these new technologies, we need to teach AI to understand and interact with the world like we do, from a first-person perspective -- commonly referred to in the research community as egocentric perception. Today's computer vision (CV) systems, however, typically learn from millions of photos and videos that are captured in third-person perspective, where the camera is just a spectator to the action. "Next-generation AI systems will need to learn from an entirely different kind of data -- videos that show the world from the center of the action, rather than the sidelines," says Kristen Grauman, lead research scientist at Facebook.

Eighty-five percent of the world's population lives in areas impacted by human-induced climate change, according to an international team of researchers. They used a new machine learning approach to identify more than 100,000 scientific studies on the effects of climate change across every continent. This massive literature review created a global map of impacts, which the team then compared to changing trends of surface temperature and rain caused by humans. In the age of big data, using AI is an important tool for climate scientists, the researchers say. While it can't substitute for expert assessments like the Intergovernmental Panel on Climate Change (IPPC), using machine learning to sort through climate studies is invaluable to helping map evidence in a systematic way.

Farmers Insurance (Woodland Hills, Calif.) has announced plans to use a digitally controlled mobile robot to assist with in-field catastrophe claims handling and non-catastrophe property inspections, with the aim of helping to improve the safety and efficiency of both while becoming one of the first national property/casualty insurers to deploy a robotic quadruped. Created by Boston Dynamics (Waltham, Mass.) and customized for Farmers, the robot--named "Spot"--will be used by Farmers claims personnel as early as fall 2021 to help assess damage from catastrophes such as hurricanes, tornadoes, earthquakes, and wildfires. Farmers reports that Spot will be equipped with a variety of sensors and cameras, including a 360-degree camera and site documentation software to help reduce the time required to capture data and augment the in-field claims review process. The robot may also help Farmers claims adjusters collect critical data and assist with claims handling optimization to serve impacted customers more efficiently, according to a statement from the insurer. In addition to utilizing Spot for in-field claims use, Farmers says it will explore applications that could help first-responder organizations during scenarios such as post-event search and rescue operations, accessing areas to assess danger for first responders or others, and/or pre-inspections to assess safety for anyone in the general vicinity.

Due to the complicated procedure and costly hardware, Simultaneous Localization and Mapping (SLAM) has been heavily dependent on public datasets for drill and evaluation, leading to many impressive demos and good benchmark scores. However, with a huge contrast, SLAM is still struggling on the way towards mature deployment, which sounds a warning: some of the datasets are overexposed, causing biased usage and evaluation. This raises the problem on how to comprehensively access the existing datasets and correctly select them. Moreover, limitations do exist in current datasets, then how to build new ones and which directions to go? Nevertheless, a comprehensive survey which can tackle the above issues does not exist yet, while urgently demanded by the community. To fill the gap, this paper strives to cover a range of cohesive topics about SLAM related datasets, including general collection methodology and fundamental characteristic dimensions, SLAM related tasks taxonomy and datasets categorization, introduction of state-of-the-arts, overview and comparison of existing datasets, review of evaluation criteria, and analyses and discussions about current limitations and future directions, looking forward to not only guiding the dataset selection, but also promoting the dataset research.

Autonomous driving is a complex task, which has been tackled since the first self-driving car ALVINN in 1989, with a supervised learning approach, or behavioral cloning (BC). In BC, a neural network is trained with state-action pairs that constitute the training set made by an expert, i.e., a human driver. However, this type of imitation learning does not take into account the temporal dependencies that might exist between actions taken in different moments of a navigation trajectory. These type of tasks are better handled by reinforcement learning (RL) algorithms, which need to define a reward function. On the other hand, more recent approaches to imitation learning, such as Generative Adversarial Imitation Learning (GAIL), can train policies without explicitly requiring to define a reward function, allowing an agent to learn by trial and error directly on a training set of expert trajectories. In this work, we propose two variations of GAIL for autonomous navigation of a vehicle in the realistic CARLA simulation environment for urban scenarios. Both of them use the same network architecture, which process high dimensional image input from three frontal cameras, and other nine continuous inputs representing the velocity, the next point from the sparse trajectory and a high-level driving command. We show that both of them are capable of imitating the expert trajectory from start to end after training ends, but the GAIL loss function that is augmented with BC outperforms the former in terms of convergence time and training stability.