An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and complexity of graph data. The graph learning models suffer from the inability to efficiently learn graph information. In order to indemnify this inefficacy, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which has enormous benefits. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models followed by examining existing PIGL methods in relation to the unified framework. We also discuss several future challenges for PIGL. This survey paper is expected to stimulate innovative research and development activities pertaining to PIGL.

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurement of the Mn L2,3 edges is straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with a three-element oxidation state composition label. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/{\lambda} = 1). We further validated the model against spectral data that were not used in training.

Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand complex language descriptions. In this work, we propose a unified framework for composing ensembles of different pre-trained models - combining the strengths of each individual model to solve various multimodal problems in a zero-shot manner. We use pre-trained models as "generators" or "scorers" and compose them via closed-loop iterative consensus optimization. The generator constructs proposals and the scorers iteratively provide feedback to refine the generated result. Such closed-loop communication enables models to correct errors caused by other models, significantly boosting performance on downstream tasks, e.g. We demonstrate that consensus achieved by an ensemble of scorers outperforms the feedback of a single scorer, by leveraging the strengths of each expert model. Results show that the proposed method can be used as a general purpose framework for a wide range of zero-shot multimodal tasks, such as image generation, video question answering, mathematical reasoning, and robotic manipulation. Large pre-trained models have shown remarkable zero-shot generalization abilities, ranging from zero-shot image generation and natural language processing to machine reasoning and action planning. Such models are trained on large datasets scoured from the internet, often consisting of billions of datapoints. Individual pre-trained models capture different aspects of knowledge on the internet, with language models (LMs) capturing textual information in news, articles, and Wikipedia pages, and visual-language models (VLMs) modeling the alignments between visual and textual information. While it is desirable to have a single sizable pre-trained model capturing all possible modalities of data on the internet, such a comprehensive model is challenging to obtain and maintain, requiring intensive memory, an enormous amount of energy, months of training time, and millions of dollars. A more scalable alternative approach is to compose different pre-trained models together, leveraging the knowledge from different expert models to solve complex multimodal tasks. Building a unified framework for composing multiple models is challenging.

Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree. To account for the influence of battery aging on the SOC estimation, the battery degradation-related state is incorporated in the SOC estimation for capacity calibration. The DIICAN method is validated on the Oxford battery dataset. The experimental results show that the proposed method can achieve SOH and RUL co-estimation with high accuracy and effectively improve SOC estimation accuracy for the whole lifespan.

For those that have not been following the journey of EPIC Oil Machine Trading Software the last 6 years, this will provide some prospective on the WHY tokenize oil trading. It is well documented that crude oil futures are traded in large part by sophisticated algorithms (easily over 80% of trade and likely near 95%). This is the casino of futures markets (all futures markets). See the CFTC study related AI Oil Trading Article here. What the studies don't tell you is that the entities (the machine learning programs) doing all that trading and more specifically the ones that are reaping the vast majority of the win-side profits are doing it with size. Size of account is critical to win rates and ROI.

Pro-Kremlin officials were pulling out of the key southern Ukraine city of Kherson on Wednesday, as Kyiv's forces advanced on territory in Russian hands since the war's earliest days. Kherson was the first major city to fall to Moscow's troops after the February invasion and retaking it would be a major prize in Ukraine's ongoing counter-offensive. Kyiv's recapturing of swathes of its territory in the east and parts of the south has however been followed by punishing missile and drone strikes that have demolished large parts of Ukraine's power grid ahead of winter. "The entire administration is already moving today," to the left bank of the Dnieper river, the region's Moscow-installed head Vladimir Saldo, said on Russian state television. The city is located on the western bank of the Dnieper, the same side where Ukrainian troops have been moving forward in a counter-offensive that began in August.

Nina Wiedemann, Valentin Wüest et al present an efficient and accurate control policy that is trained with the Analytic Policy Gradient method, and experiment with complex aerial robots such as a quadrotor (left). Their controller can track a reference trajectory accurately (right), within a fraction of the runtime required by online-optimisation methods such as MPC (bottom left). In their recent paper, Training Efficient Controllers via Analytic Policy Gradient, Nina Wiedemann, Valentin Wüest, Antonio Loquercio, Matthias Müller, Dario Floreano, and Davide Scaramuzza propose a gradient-based method for control of robotic systems. First authors Nina Wiedemann and Valentin Wüest told us more about their approach, the motivation for the work, and what they are planning next. Our paper is about control for robotic systems, with a focus on aerial vehicles.

A study published in Remote Sensing proposed a multilevel stacked context network (MSCNet) to enhance target detection accuracy and the feature pyramid network (FPN) representation by aggregating the logical relationships between different contexts and objects in remote sensing images. The remote sensing data's acquisition costs have been steadily decreasing, data sources have been steadily expanding, and image resolution and quality have improved due to the exponential growth of remote sensing technology. As a result, remote sensing imagery is increasingly adopted across various sectors. Convolutional neural networks have recently emerged as a powerful technique for analyzing remote sensing data. It generates feature representations directly from the original image pixels, while its deep stacked structure aids in extracting more abstract semantic features.

The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures long-range global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals from binary neutron star coalescences, built from five distinct, cold equations of state (EOS) of nuclear matter. From the analysis of the mass dependence of the tidal deformability parameter for each EOS class it is shown that the AST model achieves a promising performance in correctly classifying the EOS purely from the gravitational wave signals, especially when the component masses of the binary system are in the range [1,1.5]M_ . Furthermore, the generalization ability of the model is investigated by using gravitational-wave signals from a new EOS not used during the training of the model, achieving fairly satisfactory results.

Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the original dataset, and (ii) predictions towards the extreme values of the properties, important regions for new materials discovery, are not very reliable due to the sparse datapoints in this region. To address these challenges, here we present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (~50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards accelerated discovery of novel glass compositions.