One chilly day in November 2025, community worker Mike Prado drove through Abilene, Tex., handing out blankets, socks, and jackets to unhoused individuals across the city. People sat on curbs, alleyway after alleyway, their meager belongings soaked by the previous night's hard rain. Prado has worked in this community for a decade, and was once homeless in Abilene himself. Prado has witnessed difficult years--but the current situation was the worst he'd ever seen, he told TIME. One man with a walker approached Prado outside of the Hope Haven offices--an Abilene nonprofit where Prado works, which operates a shelter and helps people with vouchers find housing--and accepted a jacket from him.

The study of canvas fabrics in works of art is a crucial tool for authentication, attribution and conservation. Traditional methods are based on thread density map matching, which cannot be applied when canvases do not come from contiguous positions on a roll. This paper presents a novel approach based on deep learning to assess the similarity of textiles. We introduce an automatic tool that evaluates the similarity between canvases without relying on thread density maps. A Siamese deep learning model is designed and trained to compare pairs of images by exploiting the feature representations learned from the scans. In addition, a similarity estimation method is proposed, aggregating predictions from multiple pairs of cloth samples to provide a robust similarity score. Our approach is applied to canvases from the Museo Nacional del Prado, corroborating the hypothesis that plain weave canvases, widely used in painting, can be effectively compared even when their thread densities are similar. The results demonstrate the feasibility and accuracy of the proposed method, opening new avenues for the analysis of masterpieces.

In this work, the authors develop regression approaches based on deep learning to perform thread density estimation for plain weave canvas analysis. Previous approaches were based on Fourier analysis, which is quite robust for some scenarios but fails in some others, in machine learning tools, that involve pre-labeling of the painting at hand, or the segmentation of thread crossing points, that provides good estimations in all scenarios with no need of pre-labeling. The segmentation approach is time-consuming as the estimation of the densities is performed after locating the crossing points. In this novel proposal, we avoid this step by computing the density of threads directly from the image with a regression deep learning model. We also incorporate some improvements in the initial preprocessing of the input image with an impact on the final error. Several models are proposed and analyzed to retain the best one. Furthermore, we further reduce the density estimation error by introducing a semi-supervised approach. The performance of our novel algorithm is analyzed with works by Ribera, Vel\'azquez, and Poussin where we compare our results to the ones of previous approaches. Finally, the method is put into practice to support the change of authorship or a masterpiece at the Museo del Prado.

Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud) has made quant trading accessible to a larger audience. Each of the above steps involve lot of research and trial and error to get right. Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

The leaps AI made last year are expected to boost the digital transformation of businesses, while disrupting various sectors such as cybersecurity and autotech. Artificial intelligence (AI) surged in popularity last year, as both businesses and the public saw first-hand examples of its potential applications. Companies such as OpenAI released a wave of public demos, including the advanced chatbot ChatGPT which has drawn the attention of Microsoft. Text-to-image generators such as Dall-E 2, Midjourney and Stable Diffusion took the limelight as millions of users began to create their own AI-generated art, much to the anger of artists and companies such as Getty Images. In its tech predictions for 2023, Dell Technologies Ireland said AI could become the "main engine of innovation" for the year, as more organisations adopt the technology to harness the full potential of data and support teams across a business.

This week, craft beer company Bira landed new funding to expand its geographic reach while Torr FoodTech grabbed $12 million for its unusual and tech-centric approach to snack bars. In agtech, Clarifruit also raised $12 million while more layoffs struck the food delivery sector. Craft beer maker Bira 91 lands $70 million round led by beer company Kirin. Bira will use the funding to build more breweries and expand geographical reach of the Bira line of beverages. Sustainable grocery startup Modern Milkman raises £50 million ($60 million) after Series C close.

Marcos López de Prado, whom we have featured in previous Math Scholar articles (see Article A, Article B and Article C), has been invited to present a keynote presentation at the ACM Conference on Artificial Intelligence in Finance, to be conducted virtually October 14-16, 2020. López de Prado is a faculty member of Cornell University and also CEO of True Positive Technologies, LP, a private firm that provides machine learning techniques techniques for finance applications. He is also the author of two books in the field: Advances in Financial Machine Learning, published by Wiley (2018) and Machine Learning for Asset Managers, published by Cambridge University Press (2020). López de Prado has graciously provided the viewgraph file for the talk he is scheduled to present at the ACM Conference on AI in Finance: Viewgraph file. For full details, see López de Prado's viewgraphs at the above link.

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. Other Cornell co-authors are: David Easley, the Henry Scarborough Professor of Social Science in the College of Arts and Sciences and professor of information science in Computing and Information Science; and Marcos Lopez de Prado, professor of practice in Operations Research and Information Engineering in the College of Engineering and chief information officer of True Positive Technologies.

Robots have replaced thousands of routine jobs on Wall Street. That's the contention of Marcos Lopez de Prado, a Cornell University professor and the former head of machine learning at AQR Capital Management LLC, who testified in Washington on Friday about the impact of artificial intelligence on capital markets and jobs. The use of algorithms in electronic markets has automated the jobs of tens of thousands of execution traders worldwide, and it's also displaced people who model prices and risk or build investment portfolios, he said. "Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs -- not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms," Lopez de Prado told the U.S. House Committee on Financial Services. During the almost two-hour hearing, lawmakers asked experts about racial and gender bias in AI, competition for highly skilled technology workers, and the challenges of regulating increasingly complex, data-driven financial markets.

We've been told that there is nothing to worry about artificial intelligence, robots and technology. New technologies will only replace mundane, repetitive jobs and free up workers to do more meaningful work, claims the media and top management consulting firms. Last week, the House Financial Services Committee's Task Force on Artificial Intelligence conducted a meeting with university academics and Wall Street financial services professionals to discuss the impact of AI on trading, robo-advisory, market surveillance and other activities within the financial services sector. To set the tone, the report by Wells Fargo predicting 200,000 banking jobs in the U.S. will be lost over the next decade--due to the introduction of new technologies--was cited by the chairman of the AI Task Force, Rep. Bill Foster (D-Ill). According to Marcos Lopez de Prado, the former head of machine learning at AQR Capital Management, algorithms in electronic markets have already automated the jobs once dominated by thousands of traders.