Icefin the robot is designed to go where no human can, swimming off the coast of Antarctica under 2,000 feet of ice. Lowered through a borehole drilled with hot water, the torpedo-shaped machine takes readings and--most strikingly--video of Thwaites Glacier's vulnerable underbelly. This Florida-sized chunk of ice is also known as the Doomsday Glacier, and for good reason: It's rapidly deteriorating, and if it collapses, global sea levels could rise over a foot. It could also tug on surrounding glaciers as it dies, which would add another 10 feet to rising seas. In a pair of papers published today in the journal Nature, scientists describe what Icefin and other instruments have discovered underneath all that ice.

Earth's changing climate poses an increased risk of drought due to global warming. Since 1880, the global temperature has increased 1.01 C. Since 1993, sea levels have risen 102.5 millimeters. Since 2002, the land ice sheets in Antarctica have been losing mass at a rate of 151.0 billion metric tons per year. In 2022, the Earth's atmosphere contains more than 400 parts per million of carbon dioxide, which is 50% more than it had in 1750. While these numbers might seem removed from our daily lives, the Earth has been warming at an unprecedented rate over the past 10,000 years [1].

The detection of anomalies, or observations that significantly deviate from what is considered normal [1], in time series data is essential in various fields, including healthcare [2], cybersecurity [3, 4], industry [5], and robotics [6]. Anomaly detection is a notoriously challenging task, as the definition of what is considered anomalous can vary based on the context or application [7]. Moreover, the absence of labeled training data for non-academic problems often precludes the use of supervised machine learning techniques. Anomaly detection in data streams, which requires rapid results while aiming to detect anomalies accurately and efficiently, is frequently necessary. It is important to minimize false positive detections to prevent alarm fatigue, which can result in a serious problem being overlooked due to excessive false alarms [7]. It is also necessary to choose the appropriate method based on the application and, often, domain knowledge, as the existence of a universal anomaly detection method is a myth [8]. Choosing the appropriate method from the plethora of available options can be a challenge in itself, as different methods have different strengths in detecting certain types of anomalies. The numerous available methods can be categorized using various criteria, such as the underlying probabilistic, classification, or reconstruction-based model [1], the type of input data (univariate or multivariate), the need for labeled training data, or the ability to process data streams. In this work, we compare six unsupervised anomaly detection methods with varying complexities.

Orcinus orca (killer whales) exhibit complex calls. They last about a second. In a call, an orca typically uses multiple frequencies simultaneously, varies the frequencies, and varies their volumes. Behavior data is hard to obtain because orcas live under water and travel quickly. Sound data is relatively easy to capture. As a science goal, we would like to know whether orca vocalizations constitute a semantic language. We do this by studying whether machine learning can predict behavior from vocalizations. Such prediction would also help scientific research and safety applications because one would like to predict behavior while only having to capture sound. A significant challenge in this process is lack of labeled data. We work with recent recordings of McMurdo Sound orcas [Wellard et al. 2020] where each recording is labeled with the behaviors observed during the recording. This yields a dataset where sound segments - continuous vocalizations that can be thought of as call sequences or more general structures - within the recordings are labeled with superfluous behaviors. Despite that, with a careful combination of recent machine learning techniques, we achieve 96.4% classification accuracy. This suggests that orcas do use a semantic language. It is also promising for research and applications.

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work, we develop a hybrid approach to approximate existing ice sheet computational models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while retaining a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier, and can therefore be used for generating samples for uncertainty quantification. We study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.

From a pig heart being successfully transplanted into a human, to being able to redirect an asteroid on a collision course with Earth, there have been all manner of weird and wonderful scientific discoveries in 2022. They include the human genome finally been mapped after two decades, the unearthing of Africa's oldest known dinosaur, and the release of the first ever image of a supermassive black hole at the heart of our Milky Way galaxy. There was also the alarming discovery that microplastics are everywhere – including in us – and the hugely-anticipated first images from the world's most powerful space telescope James Webb, which will peer back to the dawn of the universe. Here, MailOnline looks at 10 of the most interesting advances this year. The year began with a bang scientifically when just a week into it a dying man became the first patient in the world to get a heart transplant from a genetically-modified pig.

A quick reminder, every link you need for our materials and articles is right here on our website. Please share this article, so we can keep spreading the word. Join Bill's Twitter here, his Gab here, and our official We Are Sovereign Twitter here. Check the bottom of this post for important notes. The following was collated from a few wide-ranging conversations covering many topics. Bill: My feeling is this is a big year, and that 2022 was this transition or preparation year, and 2023 is going to be a year of implementing plans. Gideon: "The groundwork laid in 2022 will come to fruition in 2023. If a person is not prepared, they are going to become ensnared in the cabal's plans. We also believe CSRQ will come online in 2023." Bill: I hear all the time that 2025 is their big date, it's all going to happen then, all leading up to that, but you've maintained it's coming sooner than that. What will be going on then? Gideon: "By that time, CSRQ will be fully operational. The mass die-off of the vaccinated will be evident, but generally covered-up and dismissed as the effects of Covid or other things. The cabal's main focus at that point will be implementing the new vaccination agenda, which will tie into a new pandemic. Their focus will be the non-vaccinated. They know the hold-outs, or those who resisted, will not comply easily, so this is where we see a lot of focus on using the gatekeeping operations to bring them into a compliance, and possibly a point they may even be unaware they are complying. I also suspect we will see a greater popularization of Alt Media, alternative truths, where that is brought into the light more, which will lead to the deception that we are somehow winning and the cabal is losing. Meanwhile, it will be the cabal doing it in the first place, a sort of false sense of victory and security. This is already happening, on a small scale, and it's cabal-controlled in nature." Bill: That's a very interesting point and you've brought it up more and more with me, in terms of compliance. I guess I see how it's possible. Like how we just accepted debit and credit cards have these RFID chips in them, which is now ubiquitous.

War in Europe, a momentous volcanic eruption and a surprise finding that could rewrite our understanding of reality – 2022 really has been a busy year for science, technology, health and environment news, and all that happened in just the first few months. From stunning space imagery to pig heart transplants, here are the New Scientist news editors' picks of the biggest scientific developments, discoveries and events of the year. Russia's invasion of Ukraine in February has sparked devastation across the country and affected many areas of life around the world, as both nations play a key role in the global supply chains for energy, food and more. It has also raised the spectre of nuclear weapons, with Russian president Vladimir Putin making not-so veiled threats about deploying his atomic arsenal. Thankfully, Armageddon has been avoided, but Russia's offensive has sparked discussion of a new kind of nuclear war, as Ukraine's nuclear power plants became a battleground this year.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.