Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks. The BrainTeaser shared task tests lateral thinking and uses adversarial datasets to prevent memorization, resulting in poor performance for out-of-the-box models. We propose a system for iterative, chain-of-thought prompt engineering which optimizes prompts using human evaluation. Using this shared task, we demonstrate our system's ability to significantly improve model performance by optimizing prompts and evaluate the input dataset.

Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor. Our Beta Evidential Neural Network estimates multi-action uncertainty with Beta densities based on actor-context-object relation representations. An evidence debiasing constraint is added to the objective function for optimization to reduce the static bias of video representations, which can incorrectly correlate predictions and static cues. We develop a learning algorithm based on a primal-dual average scheme update to optimize the proposed problem. Theoretical analysis of the optimization algorithm demonstrates the convergence of the primal solution sequence and bounds for both the loss function and the debiasing constraint. Uncertainty and belief-based novelty estimation mechanisms are formulated to detect novel actions. Extensive experiments on two real-world video datasets show that our proposed approach achieves promising performance in single/multi-actor, single/multi-action settings.

Fox News national security correspondent Jennifer Griffin discusses a report alleging China is developing'brain control weapons' on'Fox Report.' Reports from India claim that China has started to deploy armed robotic vehicles to handle the altitude and terrain that has proven too difficult for its troops. China and India clashed in Sept. 2020 during a border dispute along the southern coast of Pangong Lake in an area known in China as Shenpaoshan and in India as Chushul, but the armies continued their standoff along the two nations' borders throughout 2021. China has now reportedly deployed unmanned ground vehicles (UGV) to the region of Tibet to strengthen its position. People's Liberation Army (PLA) soldiers march next to the entrance to the Forbidden City during the opening ceremony of the Chinese People's Political Consultative Conference (CPPCC) in Beijing on May 21, 2020.

Over the last decade, the accessibility of banking and financial services has grown by leaps and bounds. However, there has also been a growing incidence of financial frauds. One such scam that has been on the rise is the money mule scam. A money mule is essentially a person who is used as a conduit to transfer money illegally. Criminals target victims to get their money transferred using the latter's bank accounts.

A new approach to the sparse Canonical Correlation Analysis (sCCA)is proposed with the aim of discovering interpretable associations in very high-dimensional multi-view, i.e.observations of multiple sets of variables on the same subjects, problems. Inspired by the sparse PCA approach of Journee et al. (2010), we also show that the sparse CCA formulation, while non-convex, is equivalent to a maximization program of a convex objective over a compact set for which we propose a first-order gradient method. This result helps us reduce the search space drastically to the boundaries of the set. Consequently, we propose a two-step algorithm, where we first infer the sparsity pattern of the canonical directions using our fast algorithm, then we shrink each view, i.e. observations of a set of covariates, to contain observations on the sets of covariates selected in the previous step, and compute their canonical directions via any CCA algorithm. We also introduceDirected Sparse CCA, which is able to find associations which are aligned with a specified experiment design, andMulti-View sCCA which is used to discover associations between multiple sets of covariates. Our simulations establish the superior convergence properties and computational efficiency of our algorithm as well as accuracy in terms of the canonical correlation and its ability to recover the supports of the canonical directions. We study the associations between metabolomics, trasncriptomics and microbiomics in a multi-omic study usingMuLe, which is an R-package that implements our approach, in order to form hypotheses on mechanisms of adaptations of Drosophila Melanogaster to high doses of environmental toxicants, specifically Atrazine, which is a commonly used chemical fertilizer.

Money laundering accounts for up to 5% of global GDP - or $2tn (£1.5tn) - every year, says the United Nations Office on Drugs and Crime. So banks and law enforcement agencies are turning to artificial intelligence (AI) to help combat the growing problem. Money laundering, so-called after gangster Al Capone's practice of hiding criminal proceeds in cash-only laundromats in the 1920s, is a huge and growing problem. "Dirty" money is "cleaned" by passing it through layers of seemingly legitimate banks and businesses and using it to buy properties, businesses, expensive cars, works of art - anything that can be sold on for new cash. And one of the ways criminals do this is called "smurfing".

Fraud analytics is an endless game of cat and mouse, but machine learning just might be the tool to help fraud professionals win this game. In the financial services world, fraudsters must be faster and smarter than the slowest bank to be "quids in". And a bank must be better than the fraudster to avoid being a victim. Analytics and data science play a pivotal role in this. However, a troubling issue that banks often face is the bridge between the data scientist and the fraud analyst: one really understands statistics while the other understands fraud.

Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge of 2014. Methods: The main contribution of this work is a multi-label ensemble method that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms. Some secondary contributions include a study on the temporal aspects of the BioASQ corpus (observations apply also to the BioASQ's super-set, the PubMed articles collection) and the proper adaptation of the algorithms used to deal with this challenging classification task. Results: The ensemble method we developed is compared to other approaches in experimental scenarios with subsets of the BioASQ corpus giving positive results. During the BioASQ 2014 challenge we obtained the first place during the first batch and the third in the two following batches. Our success in the BioASQ challenge proved that a fully automated machine-learning approach, which does not implement any heuristics and rule-based approaches, can be highly competitive and outperform other approaches in similar challenging contexts.