—: The size and complexity of deep neural networks used in AI applications continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track the energy consumption and equivalent CO ₂ emissions of their models in a straightforward way. In eco2AI we focus on accurate tracking of energy consumption and regional CO ₂ emissions accounting. We encourage the research for community to search for new optimal Artificial Intelligence (AI) architectures with lower computational cost. The motivation also comes from the concept of AI-based greenhouse gases sequestrating cycle with both Sustainable AI and Green AI pathways. The code and documentation are hosted on Github under the Apache 2.0 license https://github.com/sb-ai-lab/Eco2AI .

The atomic structure and energy spectrum of Ga(As,P)/GaP heterostructures were studied. It was shown that the deposition of GaAs of the same nominal thickness leads to the formation of pseudomorphic GaAs/GaP quantum wells (QW), fully relaxed GaAs/GaP self-assembled quantum dots (SAQDs), or pseudomorphic GaAsP/GaP SAQDs depending on the growth temperature. We demonstrate that the atomic structure of Ga(As,P)/GaP heterostructures is ruled by the temperature dependence of adatom diffusion rate and GaAs-GaP intermixing. The band alignment of pseudomorphic GaAs/GaP QW and GaAsP/GaP SAQDs is shown to be of type II, in contrast to that of fully relaxed GaAs/GaP SAQDs, which have the band alignment of type I with the lowest electronic states at the indirect L valley of the GaAs conduction band.

Introduction: Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting cardiac abnormalities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early improves the quality and efficiency of medical care.Methods: The paper presents various modern approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincaré representation of ECG signal and deep-learning-based image classifiers. Additionally, the raw signals were processed with the one-dimensional convolutional model while the XGBoost model was facilitated to predict based on the time-series features.Results: The Poincaré-based methods showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost model gave an acceptable performance in long-term data but had a long inference time due to highly-consuming calculations within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and they were superior to the first-ranking solution of each challenge. The 1D models also presented high specificity. Additionally, our paper investigated efficiency metrics including power consumption and equivalent CO₂ emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1D ResNet assessed the AF patterns in raw ECG signals.Discussion: Despite the under-performed results, the Poincaré diagrams are still worth studying further because of the accessibility and inexpensive procedure. In the 1D convolutional models, the residual connections are useful to keep the model simple but not decrease the performance. Our approach in power measurement and model interpretation helped understand the numerical complexity and mechanism behind the model decision.

Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018–2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.