Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for estimating the importance of variables have been developed during the last decade. In this paper, we proposed a computational and theoretical exploration of the emerging methods of variable importance estimation, namely: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), the Predictive Error Function (PERF), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST) that were tested on different kinds of real-life and simulated data. All these methods can handle both regression and classification tasks seamlessly but all fail when it comes to dealing with data containing missing values. The implementation has shown that PERF has the best performance in the case of highly correlated data closely followed by RF. PERF and XGBOOST are "data-hungry" methods, they had the worst performance on small data sizes but they are the fastest when it comes to the execution time. SVM is the most appropriate when many redundant features are in the dataset. A surplus with the PERF is its natural cut-off at zero helping to separate positive and negative scores with all positive scores indicating essential and significant features while the negatives score indicates useless features. RF and LASSO are very versatile in a way that they can be used in almost all situations despite they are not giving the best results.

Professor Kyunghyun Cho (Computer Science - Courant and Center for Data Science) is one of five recipients of the Inaugural Samsung AI Researcher of the Year Award. Professor Cho is donating the $30,000 prize money to MILA, an AI research institute in Quebec, for the support of incoming female students from Latin America, Africa, South Asia, South East Asia, and Korea. (November, 2020)

Overbond, the leading API-based credit trading automation and execution service in the global capital markets, has secured funding from Fitch Ventures, the equity investment arm of Fitch Group, which is a global leader in financial information services. Overbond will use the capital to grow its sales and marketing division with plans to open an office in London, U.K., and double its headcount over the coming year. In addition, through new cloud-based data redistribution channels, Overbond will grow its global presence, integrate new data sources to expand its AI models' coverage and provide enhanced AI trade automation solutions for clients. With the addition of neutrino8 wireless access to the vendor-agnostic .connect Joseph Hospital, along with Perimeter Medical Imaging AI, Inc.("Perimeter" or the "Company") – a medical technology company driven to transform cancer surgery with ultra-high-resolution, real-time, advanced imaging tools to address high unmet medical needs – today jointly announced the first commercial placement of the Perimeter S-Series OCT system in the state of California at Pavilion Surgery Center in Orange, CA.

With world's population continuing to increase and climate change drastically affecting our environment, many metropolises are struggling to grow, develop and even support citizens within current and traditional urban designs. Governments, entrepreneurs and technology companies are employing some of the world's leading architects and designers to rethink the idea of cities, how people can interact and how to live within them. From reclaimed land, groundbreaking skyscrapers in the desert and cities rising in the metaverse, here are 12 incredible futuristic cities redefining the urban spaces we live in. The $500 billion Neom project in Saudi Arabia is set to be home to a record-setting 170-kilometre-long skyscraper called the Mirror Line. It will be the world's largest structure, comprising of two buildings up to 490 metres tall, running parallel to each other.

Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the learnable parameter and optimize the weights on the meta set, entailing expensive bilevel optimization. In this paper, we propose a novel re-weighting method based on optimal transport (OT) from a distributional point of view. Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set. The weights of the training samples are the probability mass of the imbalanced distribution and learned by minimizing the OT distance between the two distributions. Compared with existing methods, our proposed one disengages the dependence of the weight learning on the concerned classifier at each iteration. Experiments on image, text and point cloud datasets demonstrate that our proposed re-weighting method has excellent performance, achieving state-of-the-art results in many cases and providing a promising tool for addressing the imbalanced classification issue.

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.

Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks. Simple fine-tuning of PLMs, on the other hand, might be suboptimal for domain-specific tasks because they cannot possibly cover knowledge from all domains. While adaptive pre-training of PLMs can help them obtain domain-specific knowledge, it requires a large training cost. Moreover, adaptive pre-training can harm the PLM's performance on the downstream task by causing catastrophic forgetting of its general knowledge. To overcome such limitations of adaptive pre-training for PLM adaption, we propose a novel domain adaption framework for PLMs coined as Knowledge-Augmented Language model Adaptation (KALA), which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. We validate the performance of our KALA on question answering and named entity recognition tasks on multiple datasets across various domains. The results show that, despite being computationally efficient, our KALA largely outperforms adaptive pre-training. Code is available at: https://github.com/Nardien/KALA/.

The arrests were made after someone complained that he had been blackmailed - police say the fraudsters threatened to publish nude photos of him on social media unless he paid them some money.

Detect Technologies announces a global agreement with Vedanta for deployment of T-Pulse, their internationally deployed AI-based workplace safety software. Vedanta Resources Limited is a globally diversified natural resources company and is among the top producers of major commodities, including zinc-lead-silver, iron ore, steel, copper, aluminium, oil and gas. The group engages more than 65,000 employees and contractors, primarily in India, Africa, Ireland and Australia. Managing EHS for such a diverse and spread-out organisation is a massive challenge. Driven by its commitment to GOAL ZERO, Vedanta started exploring AI–based solutions, which can infuse efficiency in this process.

Melbourne: Everyone sleeps, but we have few tools for measuring the sleep the world is getting at scale. AI and sleep could help us study global shocks in near-real time. It's not unusual to hear people complaining about tiredness multiple times a day, but why? Sleep is fundamental to human health, but because it's so private, there are few tools for measuring how much sleep everyone is getting at scale. Existing methods use time diaries, sleep surveys, sleep laboratories or, more recently, wearable technology to measure sleep. But none of these approaches is ready to tackle a global sleep-loss pandemic.