Monocular 3D object detection continues to attract attention due to the cost benefits and wider availability of RGB cameras. Despite the recent advances and the ability to acquire data at scale, annotation cost and complexity still limit the size of 3D object detection datasets in the supervised settings. Self-supervised methods, on the other hand, aim at training deep networks relying on pretext tasks or various consistency constraints. Moreover, other 3D perception tasks (such as depth estimation) have shown the benefits of temporal priors as a self-supervision signal. In this work, we argue that the temporal consistency on the level of object poses, provides an important supervision signal given the strong prior on physical motion. Specifically, we propose a self-supervised loss which uses this consistency, in addition to render-and-compare losses, to refine noisy pose predictions and derive high-quality pseudo labels. To assess the effectiveness of the proposed method, we finetune a synthetically trained monocular 3D object detection model using the pseudo-labels that we generated on real data. Evaluation on the standard KITTI3D benchmark demonstrates that our method reaches competitive performance compared to other monocular self-supervised and supervised methods.

That same month, researchers at Baylor College of Medicine and India's Amity University announced that they had developed an AI platform that can target not only Covid-19 but Chagas disease, an infectious ailment common in South America that results in damage to the heart and central nervous system. AI is capable of analyzing data from various sources--electronic health records, images, therapies, etc.--and developing models that will predict the best possible approach to any given patient's care journey, thereby streamlining operations and ensuring the most favorable outcomes. IBM researchers, for example, have partnered with scientists from two healthcare systems to use AI to examine EHRs for clues about the warning signs of heart failure, which has long been the leading cause of death in the U.S. As a result, the team was able to develop a model that predicted this malady as much as two years earlier than previous methods.

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We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training data or refining the model by reverting the model optimization on the marked data points. Unfortunately, aside from their computational inefficiency, those approaches inevitably hurt the resulting model's generalization ability since they remove not only unique characteristics but also discard shared (and possibly contributive) information. To address the performance degradation problem, this paper presents a novel approach called Performance Unchanged Model Augmentation~(PUMA). The proposed PUMA framework explicitly models the influence of each training data point on the model's generalization ability with respect to various performance criteria. It then complements the negative impact of removing marked data by reweighting the remaining data optimally. To demonstrate the effectiveness of the PUMA framework, we compared it with multiple state-of-the-art data removal techniques in the experiments, where we show the PUMA can effectively and efficiently remove the unique characteristics of marked training data without retraining the model that can 1) fool a membership attack, and 2) resist performance degradation. In addition, as PUMA estimates the data importance during its operation, we show it could serve to debug mislabelled data points more efficiently than existing approaches.

Hydropower has been stirring up controversies since the early 2000s. Despite being promoted as a solution to mitigate climate change, the hydropower bubble burst when researchers discovered in 2005 that hydropower dams are responsible for huge amounts of greenhouse gas emissions. Hydropower dams' walls restrict the flow of rivers and turn them into pools of stagnant water. Reservoir surfaces and turbines then release methane into the atmosphere. Methane makes up approximately 80 percent of the greenhouse gases emitted from hydropower dams, peaking in the first decade of the dams lifecycle.

Listed below are the key technology trends impacting the smart grid in power theme, as identified by GlobalData. By clicking the Download Free Report button, you accept the terms and conditions and acknowledge that your data will be used as described in the GlobalData Privacy Policy By downloading this Report, you acknowledge that we may share your information with our white paper partners/sponsors who may contact you directly with information on their products and services. Visit our privacy policy for more information about our services, how we may use, process and share your personal data, including information on your rights in respect of your personal data and how you can unsubscribe from future marketing communications. Our services are intended for corporate subscribers and you warrant that the email address submitted is your corporate email address. AI has an important role to play in understanding demand, and generating predictions from non-dispatchable resources such as wind and solar and from wholesale prices.

When the pandemic forced millions of people into working and collaborating remotely, it not only caused an explosion in the use and development of new technologies for productive and effective collaboration, it also made many of us more aware than ever of how technologies can enhance our thinking and creativity. At Nesta's Centre for Collective Intelligence Design, our work rests upon the premise that human intelligence combined with machine intelligence is more powerful than either in isolation. When these are successfully combined, it is known as collective intelligence. Our Grants Programme awarded funding to 15 different teams around the world that designed experiments to explore and test this idea in new ways to help tackle pressing social and environmental challenges. Each experiment fell under one of four themes: exploring artificial intelligence (AI)-crowd interaction; making better collective decisions; understanding the dynamics of collective behaviour; and gathering better data.

A new research project has found that the discretionary decisions made by human bank managers can be replicated by machine learning systems to an accuracy of more than 95%. Using the same data available to bank managers in a privileged dataset, the best-performing algorithm in the test was a Random Forest implementation – a fairly simple approach that's twenty years old, but which still outperformed a neural network when attempting to mimic the behavior of human bank managers formulating final decisions about loans. The Random Forest algorithm, one of four put through their paces for the project, achieves high human-equivalent scoring vs. performance of bank managers, despite the relative simplicity of the algorithm. The researchers, who had access to a proprietary dataset of 37,449 loan ratings across 4,414 unique customers at'a large commercial bank', suggest at various points in the preprint paper that the automated data analysis that managers are given to make their decision has now become so accurate that bank managers rarely deviate from it, potentially signifying that bank managers' part in the loan approval process chiefly consists of retaining someone to fire in the event of a loan default. 'From a practical perspective it is worth noting that our results may indicate that the bank could process loans faster and cheaper in the absence of human loan managers with very comparable results.

With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action transitions within an input video are challenging but necessary tasks for applications that require real-time human-machine interaction. By reviewing a large body of recent related work in the literature, we thoroughly analyse, explain and compare action segmentation methods and provide details on the feature extraction and learning strategies that are used on most state-of-the-art methods. We cover the impact of the performance of object detection and tracking techniques on human action segmentation methodologies. We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation and deployment.