In the 21st century, the concept of a twin need not be confined to fraternal or identical--a twin can be digital too. Digital twins have caught the eyes of some of the biggest companies in the world--Amazon and Nvidia, for instance, both made announcements about new digital-twin initiatives within the last month--as well as those of specialists like infrastructure engineering software company Bentley Systems. The concept started gaining traction at the beginning of the century, and picked up steam in the early 2010s when the rise of IoT made digital twins more feasible. As of 2020, it was estimated to be a $3.1 billion market, per Markets and Markets, and projected to grow into a $48.2 billion industry by 2026. So...what is a digital twin?

The savvy investor keeps their eyes forward, toward the horizon. Right now, the sea of tech is the one to watch, and the ships coming into view are flying AI's flag. This is not a new development, it's been on course for several years – but as an investment sector, it's heating up. AI is the tech that will power our digital systems for years to come, everything from our smartphones to our cars to Elon Musk's Mars rockets. AI isn't just one technology, rather, it's a range of techs – and approaches to tech – including the data collection and analysis that feeds machine learning.

Over the past 20 years, artificial intelligence (AI) has significantly transformed industry, taking an organisation's ability to optimise processes and proactively detect and solve problems to a whole new level. As a result of the increasing adoption of digital transformation, AI continues to provide benefits across a range of industrial processes. This has resulted in the extensive use of digital twins – virtual representations of physical objects, systems or factories that are created through data gathered from Internet of Things (IoT) devices, advanced computer systems and digital processes. AI is the brain behind the digital twin. By applying various forms of AI – such as neural networks, computer vision, and machine learning – in different ways, it can create targeted solutions presented in the form of analytics.

Deloitte released a report which highlights how trends in Technology, Media & Telecommunications (TMT) may affect businesses and consumers worldwide in 2022. The report underscores how many of these trends are being driven by the global pandemic's economic and societal shifts, resulting in an increasingly connected and multi-device world, fueling the world's need for more chips, growth in connectivity, and entertainment options. "The pandemic increased the need to maintain connections, improve productivity and experience entertainment, with accelerated adoption from both consumers and businesses alike," said Kevin Westcott, vice chair, Deloitte, U.S. TMT and global Telecommunications, Media and Entertainment (TME) practice leader. "In 2022, we foresee these behaviors continuing to grow, but amid a backdrop of challenges. Supply chain woes, increasing regulatory issues and changing media habits will be at the forefront of business leaders' minds as these challenges impact their ability to meet market demands." Many types of chips will still be in short supply during 2022, but it will be less severe than it was for most of 2021, and it will not affect all chips.

The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with an antisymmetric layer to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to measure the expressiveness of the antisymmetric layer. This work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers as a diagnostic tool. We first introduce a generic antisymmetric (GA) layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small systems. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks. Interestingly, the resulting FermiNet-FA architecture does not outperform the FermiNet. This suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet called the full determinant mode, which replaces each product of determinants with a single combined determinant. The full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can significantly outperform the standard 64-determinant FermiNet, yielding an energy within 0.4 kcal/mol of the best available computational benchmark.

It is important to forecast dam inflow for flood damage mitigation. The hydrograph provides critical information such as the start time, peak level, and volume. Particularly, dam management requires a 6-h lead time of the dam inflow forecast based on a future hydrograph. The authors propose novel target inflow weights to create an ocean feature vector extracted from the analyzed images of the sea surface. We extracted 4,096 elements of the dimension vector in the fc6 layer of the pre-trained VGG16 network. Subsequently, we reduced it to three dimensions of t-SNE. Furthermore, we created the principal component of the sea temperature weights using PCA. We found that these weights contribute to the stability of predictor importance by numerical experiments. As base regression models, we calibrate the least squares with kernel expansion, the quantile random forest minimized out-of bag error, and the support vector regression with a polynomial kernel. When we compute the predictor importance, we visualize the stability of each variable importance introduced by our proposed weights, compared with other results without weights. We apply our method to a dam at Kanto region in Japan and focus on the trained term from 2007 to 2018, with a limited flood term from June to October. We test the accuracy over the 2019 flood term. Finally, we present the applied results and further statistical learning for unknown flood forecast.

Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks. However, most existing advances of deep learning methods in the RSI field heavily rely on the features extracted by the manually designed backbone network, which severely hinders the potential of deep learning models due the complexity of RSI and the limitation of prior knowledge. In this paper, we research a new design paradigm for the backbone architecture in RSI recognition tasks, including scene classification, land-cover classification and object detection. A novel one-shot architecture search framework based on weight-sharing strategy and evolutionary algorithm is proposed, called RSBNet, which consists of three stages: Firstly, a supernet constructed in a layer-wise search space is pretrained on a self-assembled large-scale RSI dataset based on an ensemble single-path training strategy. Next, the pre-trained supernet is equipped with different recognition heads through the switchable recognition module and respectively fine-tuned on the target dataset to obtain task-specific supernet. Finally, we search the optimal backbone architecture for different recognition tasks based on the evolutionary algorithm without any network training. Extensive experiments have been conducted on five benchmark datasets for different recognition tasks, the results show the effectiveness of the proposed search paradigm and demonstrate that the searched backbone is able to flexibly adapt different RSI recognition tasks and achieve impressive performance.

Object detection is a canonical task in computer vision, as well as in remote sensing. Object detection in remote sensing imagery deals with detecting instances of visual objects of certain classes, most of which are man-made, buildings, airplanes, ships, vehicles, to name a few. This technology has been widely used in many civilian and military fields, such as port and airport flow monitoring, traffic diversion, urban planning, lost ship search and rescue. Traditional machine learning (ML) schemes based on the encoding of handcrafted features (e.g., textures, color histogram, or more complex HOG Dalal and Triggs (2005), SIFT Lowe (2004), Haar Viola and Jones (2001),ACF Dollár, Appel, Belongie and Perona (2014), etc.) can only generate shallow to middle features with limited representativity. Recently, with the rapid development of deep learning (DL), convolutional neural networks (CNNs) have became a new and powerful approach for feature extraction and greatly improved the performance of object detection. Current CNN-based object detection methods could be roughly divided into two streams: two-stage schemes and one-stage schemes. The two-stage detector, such as R-CNN Girshick, Donahue, Darrell and Malik (2014), Fast R-CNN Girshick (2015), Faster R-CNN Ren, He, Girshick and Sun (2017) and other detectors Cai and Vasconcelos (2018); Pang, Chen, Shi, Feng, Ouyang and Lin (2019); Li, Chen, Wang and Zhang (2019b), divide the detection into localization and recognition stages, having one more region-proposal step than single-stage detectors.

Due to their high energy intensity, buildings play a major role in the current worldwide energy transition. Building models are ubiquitous since they are needed at each stage of the life of buildings, i.e. for design, retrofitting, and control operations. Classical white-box models, based on physical equations, are bound to follow the laws of physics but the specific design of their underlying structure might hinder their expressiveness and hence their accuracy. On the other hand, black-box models are better suited to capture nonlinear building dynamics and thus can often achieve better accuracy, but they require a lot of data and might not follow the laws of physics, a problem that is particularly common for neural network (NN) models. To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues. In this work, we present a novel physics-informed NN architecture, dubbed Physically Consistent NN (PCNN), which only requires past operational data and no engineering overhead, including prior knowledge in a linear module running in parallel to a classical NN. We formally prove that such networks are physically consistent -- by design and even on unseen data -- with respect to different control inputs and temperatures outside and in neighboring zones. We demonstrate their performance on a case study, where the PCNN attains an accuracy up to $50\%$ better than a classical physics-based resistance-capacitance model on $3$-day long prediction horizons. Furthermore, despite their constrained structure, PCNNs attain similar performance to classical NNs on the validation data, overfitting the training data less and retaining high expressiveness to tackle the generalization issue.

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches, so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets and thus free up otherwise occupied resources. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner. In this way, fully controllable experiment environments are set up, which support insights in the model training. Thus, SyntEO makes the learning process approachable and model behaviour interpretable, an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.