The situation in the eurozone is much more fragile than in the U.S. Gopinath: It's true, high-energy prices are a particular burden on countries like Germany, whose economy is very dependent on energy imports. At least the Federal Republic has done better than expected; we had expected GDP growth to have slowed to 1.5 percent in 2022. Measured against that, it has done better, up 1.9 percent. Now, it seems that overall inflation may have already peaked. Gopinath: But core inflation – i.e., price increases excluding energy and food prices – is stubbornly high and will probably only start to fall toward the end of the year.

We may be a mere 23 years into the century but already it has been a doozy. In 2022, we saw impressive technological feats, including a fusion energy breakthrough, the first successful all-electric passenger plane test, and the release of bivalent Covid-19 booster vaccines. As we enter into 2023, what can we expect? At Inverse, we aren't in the business of fortune-telling, but the innovations we saw in the last 12 months can help us predict what might be in store for the next -- from driver-free transportation to commercial space exploration to (finally) clean energy for all This year will usher in more affordable EVs, allowing a bigger chunk of the population to drive sustainably. For example, GM is rolling out cheaper models that run for around $30,000, expanding the choices for drivers on a budget.

This may be the year when artificial intelligence transforms daily life. So said Brad Smith, president and vice chairman of Microsoft, at a Vatican-organised event on AI last week. But Smith's statement was less a prediction than a call to action: the event--attended by industry leaders and representatives of the three Abrahamic religions--sought to promote an ethical, human-centred approach to the development of AI. There is no doubt that AI poses a daunting set of operational, ethical and regulatory challenges. And addressing them will be far from straightforward.

Russia will slash its support for the development of AI technologies more than 10-fold after the fallout of the military invasion of Ukraine, Kommersant daily reported comparing the December 2022 government AI development roadmap to that approved in 2019. Reportedly, the government will only allocate RUB25bn for the development of artificial intelligence technologies in Russia by 2030, with another RUB100bn to be invested by state-controlled bank Sber (Sberbank), according to the 2030 roadmap approved by the Ministry of Economic Development at the end of December 2022. The main objective of the roadmap is to "accelerate the process of large-scale implementation of domestic AI solutions in the economy and the social sphere," the government officials commented to Kommersant. "After the imposition of sanctions [for the full-scale military invasion of Ukraine], we identified AI products that need to be developed in Russia, worked out a number of measures to enable their development and scaling, and included it all in the updated roadmap," the EconMin represetatvies added Ministry of Economy added. AI development is seen in four major directions: "natural language processing and speech synthesis", "computer vision", "advanced artificial intelligence methods" and "intelligent decision-making support".

This paper proposes a novel lexicon-based unsupervised sentimental analysis method to measure the $``\textit{hope}"$ and $``\textit{fear}"$ for the 2022 Ukrainian-Russian Conflict. $\textit{Reddit.com}$ is utilised as the main source of human reactions to daily events during nearly the first three months of the conflict. The top 50 $``hot"$ posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, UkraineWarReports) and their relative comments are scraped and a data set is created. On this corpus, multiple analyses such as (1) public interest, (2) hope/fear score, (3) stock price interaction are employed. We promote using a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modelling is also utilised to understand the main issues raised by users and what are the key talking points. Experimental analysis shows that the hope strongly decreases after the symbolic and strategic losses of Azovstal (Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and negatives, are present after important battles, but also some non-military events, such as Eurovision and football games.

It has been recently recognized that the surface of sub-km asteroids in contact with the space environment is not fine-grained regolith but consists of centimeter to meter-scale rocks. Here we aim to understand how the rocky morphology of minor bodies react to the well known space erosion agents on the Moon. We deploy a neural network and map a total of ~130,000 fragmented boulders scattered across the lunar surface and visually identify a dozen different desintegration morphologies corresponding to different failure modes. We find that several fragmented boulder morphologies are equivalent to morphologies observed on asteroid Bennu, suggesting that these morphologies on the Moon and on asteroids are likely not diagnostic of their formation mechanism. Our findings suggest that the boulder fragmentation process is characterized by an internal weakening period with limited morphological signs of damage at rock scale until a sudden highly efficient impact shattering event occurs. In addition, we identify new morphologies such as breccia boulders with an advection-like erosion style. We publicly release the produced fractured boulder catalog along with this paper.

U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets as finite-dimensional truncations of models on an infinite-dimensional function space. We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data. We then leverage our framework to identify state-of-the-art hierarchical VAEs (HVAEs), which have a U-Net architecture, as a type of two-step forward Euler discretisation of multi-resolution diffusion processes which flow from a point mass, introducing sampling instabilities. We also demonstrate that HVAEs learn a representation of time which allows for improved parameter efficiency through weight-sharing. We use this observation to achieve state-of-the-art HVAE performance with half the number of parameters of existing models, exploiting the properties of our continuous-time formulation.

With the recent surge in big data analytics for hyper-dimensional data there is a renewed interest in dimensionality reduction techniques for machine learning applications. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization, and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set, and Plane data set of Thankoor), where we achieve 0.77, 0.95, and 1.00 F1 score, respectively.

Robotic mobility in microgravity is necessary to expand human utilization and exploration of outer space. Bio-inspired multi-legged robots are a possible solution for safe and precise locomotion. However, a dynamic motion of a robot in microgravity can lead to failures due to gripper detachment caused by excessive motion reactions. We propose a novel Reaction-Aware Motion Planning (RAMP) to improve locomotion safety in microgravity, decreasing the risk of losing contact with the terrain surface by reducing the robot's momentum change. RAMP minimizes the swing momentum with a Low-Reaction Swing Trajectory (LRST) while distributing this momentum to the whole body, ensuring zero velocity for the supporting grippers and minimizing motion reactions. We verify the proposed approach with dynamic simulations indicating the capability of RAMP to generate a safe motion without detachment of the supporting grippers, resulting in the robot reaching its specified location. We further validate RAMP in experiments with an air-floating system, demonstrating a significant reduction in reaction forces and improved mobility in microgravity.

Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices. However, their training, architectural design, and hyperparameter tuning remain challenging as these involve multiple computationally expensive combinatorial optimization problems. Here we introduce quantum hypernetworks as a mechanism to train binary neural networks on quantum computers, which unify the search over parameters, hyperparameters, and architectures in a single optimization loop. Through classical simulations, we demonstrate that of our approach effectively finds optimal parameters, hyperparameters and architectural choices with high probability on classification problems including a two-dimensional Gaussian dataset and a scaled-down version of the MNIST handwritten digits. We represent our quantum hypernetworks as variational quantum circuits, and find that an optimal circuit depth maximizes the probability of finding performant binary neural networks. Our unified approach provides an immense scope for other applications in the field of machine learning.