Is the Rat War Over? In New York, a rat czar and new methods have brought down complaints. We may even be ready to appreciate the creatures. Rats were leaving Manhattan, hurrying across the bridges in single-file lines. Some went to Westchester, some to Brooklyn. It was the pandemic, and the rats, which had been living off the nourishing trash of New York's densest borough for generations, were as panicked about the closure of restaurants as we were. People were eating three meals a day at home, and the rats were hungry. At least that was the story going around.

In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion for Data Mining, integrating embeddings and the Cross-Industry Standard Process for Data Mining with the existing Data Fusion Information Group model. Our model aims to decrease computational costs, complexity, and bias while improving efficiency and reliability. We also propose "disentangled dense fusion", a novel embedding fusion method designed to optimize mutual information and facilitate dense inter-modality feature interaction, thereby minimizing redundant information. We demonstrate the model's efficacy through three use cases: predicting diabetic retinopathy using retinal images and patient metadata, domestic violence prediction employing satellite imagery, internet, and census data, and identifying clinical and demographic features from radiography images and clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction and sex classification, respectively, in radiological analysis. These results underscore the Data Fusion for Data Mining model's potential to significantly impact multimodal data processing, promoting its adoption in diverse, resource-constrained settings.

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauer-based time Graph Neural Network (GegenGNN) architecture, which adopts an encoder-decoder structure. Likewise, our approach also utilizes a dedicated loss function that incorporates a mean squared error component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals.

Financial technology services are increasingly large and diverse, not only representing a change for users, but also for banks that have had to adapt as new developments allow greater knowledge of the market and customers. Faced with this situation, they have launched in Colombia a platform that will use advanced artificial intelligence functions to generate a credit score for each person and allow financial institutions to identify potential clients. The new system is developed by the fintech Yabx which specializes in enabling credit for unbanked sectors, so thanks to an alliance it will base its data on Telecom's Telecommunications system in association with Claro, therefore It will allow the identification of new clients not recognized by the criteria of traditional banking. The platform will use machine-learning algorithms (artificial intelligence machine learning) to provide a credit score and other products that can be offered to banks or other fintech companies that want to improve their abilities to acquire and qualify customers whose applications to banks traditional are rejected. Thanks to the association with Claro, one of the largest telecommunications networks in the country, the new system will be able to cover around 67% of Colombian adults, in addition, it will allow credit institutions to reduce their rejection rates by up to 40% by take into account factors that are not normally observed.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.