We study nonparametric regression and classification for path-valued data. We introduce a functional Nadaraya-Watson estimator that combines the signature transform from rough path theory with local kernel regression. The signature transform provides a principled way to encode sequential data through iterated integrals, enabling direct comparison of paths in a natural metric space. Our approach leverages signature-induced distances within the classical kernel regression framework, achieving computational efficiency while avoiding the scalability bottlenecks of large-scale kernel matrix operations. We establish finite-sample convergence bounds demonstrating favorable statistical properties of signature-based distances compared to traditional metrics in infinite-dimensional settings. We propose robust signature variants that provide stability against outliers, enhancing practical performance. Applications to both synthetic and real-world data - including stochastic differential equation learning and time series classification - demonstrate competitive accuracy while offering significant computational advantages over existing methods.

"It's always easier to do business if you already know what's going to happen." This playful phrase recently became an inspiration for the consumer health marketing team at the global life science company Bayer in the creation of a forecasting model to, in essence, try to predict the future. Specifically, the team wanted to predict cold and flu search trends around the world to help reach people with the right products to alleviate their symptoms. Eric Gregoire, SVP and global head of digital and media at Bayer, said the project started in Australia ahead of the nation's cold and flu season this year. And the prediction model was so successful in improving digital marketing performance that the team intends to exapand the project globally.

With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted in the RAW domain, addressing lack of available RAW data while reaping from the benefits of performing tasks directly on sensor readings. This paper's proposed methodology is a state-of-the-art solution to the task of RAW reconstruction, and the multi-step refinement process integrating an overexposure mask is novel in three ways: instead of from RGB to bayer, the pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss functions; the multi-step processes has greatly enhanced the performance of the baseline U-Net from start to end; the pipeline is a generalizable process of refinement that can enhance other high performance methodologies that support end-to-end learning.

Date Posted: Sept. 16, 2022 Domestic relocation as well as Visa sponsorship is available. Required qualifications: Minimum of a Bachelor's degree with five plus years of experience or Masters degree with two plus years' experience or PhD; Educational preparation or applied experience in at least one of the following areas: Machine Learning, Computational Biology, Applied Mathematics, Bioinformatics, Genomics, Computer Science, Statistics, Biostatistics, or other related quantitative discipline; Demonstrate proficiency applying deep learning, machine learning algorithms, concepts to image classification, object detection, and object segmentation; Skilled in computational skills and level of experience building models using Python and building analysis pipelines; Understanding of software development best practices (version control, code documentation & review, cloud-based sequence analysis, database management); Intermediate proficiency in predictive modeling including comprehension of theory, identification strategies, limitations, and pitfalls; Experience in successful delivery of valuable analysis through application of domain knowledge; Possess strong business acumen; Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner. Entirely remote jobs that could be performed in Colorado: employees can expect to be paid a salary of approximately $120,000 (or between $110,000 to $135,000). Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc..

"We've developed a proprietary AI technology that analyzes certain popular songs of a specified genre and generates recommendations for the various elements of song construction: lyrical content, chords, melody, tempo, sounds, etc. We then combine these elements to create the song," Martini said in the interview. That said, FN Meka currently boasts 10 million TikTok followers. So have a listen to "Florida Water" and judge for yourself as you read the rest of this week's A.I. news. Today's edition was curated and written by Jeremy Kahn.

It's no secret the medtech industry has embraced artificial intelligence (AI) and machine learning (ML). Pharmaceutical companies are leaping into the AI/ML space, too. There have been about 100 partnerships that have been established between pharmaceutical companies and AI vendors, according to a report from clinicaltrialsarena.com citing GlobalData Healthcare data. Earlier today (Wednesday), Merck announced its plan to dive deeper into the space. The pharma powerhouse said it was launching the Merck Digital Sciences Studio (MDSS), which will help early-stage biomedical startups with direct investment, access to powerful Azure Cloud computing, and opportunities to pilot their technologies in collaboration with discovery and clinical scientists at Merck.

From search engines to social media, algorithms are quickly becoming a part of everyday life, and companies and academic institutions like Bayer, the Massachusetts Institute of Technology (MIT) and others are using them to their advantage. In artificial intelligence (AI) and machine learning (ML), algorithms are used to solve complex problems - including in preventative healthcare, diagnostics and drug discovery. Continue reading to learn how these technologies are being utilized in the life sciences. One of the most time-consuming diagnostic tools available is radiology. First, the patient sits tight for the required timeframe, before a radiologist sits down to analyze the captured images.

The US Supreme Court on Tuesday declined an appeal from Bayer-owned Monsanto that aimed to challenge thousands of lawsuits claiming its weedkiller Roundup causes cancer -- a potentially costly ruling. The high court did not explain its decision not to take the case, which left intact a $25 million ruling in favor of a California man who alleged he developed cancer after using the chemical for years. The decision marks a major blow to the German conglomerate's legal fight against some 31,000 Roundup-related cases. "Bayer respectfully disagrees with the Supreme Court's decision," the company said in a statement. "The company believes that the decision undermines the ability of companies to rely on official actions taken by expert regulatory agencies," it added, referring to a 2020 federal finding that Roundup's active ingredient is not risky.

Over the past seven years, Bayer's investment arm has infused 50-plus companies with more than €1.3 billion. Leaps by Bayer is accelerating its dealmaking pace and Bayer is committing another €1.3 billion, which the multinational corporation estimates will fuel its investment vehicle for two more years. Bayer announced the capital commitment on Friday during the company's Breakthrough Innovation Forum, an event that covered the corporation's plans in healthcare and agriculture. Those two fields were the core focus areas when Bayer formed Leaps in 2015, aiming to invest in companies developing breakthrough solutions to big challenges facing humanity, challenges that the corporation termed "leaps." At the start, Bayer identified 10 leaps.

So far, the pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately its business model has been under significant pressure since the return on R&D investment has dropped to its lowest level in decades (lack of innovation amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility) and its public reputation in US and around the world (anti vaccine movement in Europe) is worse than ever. This worrisome mix of little growth potential and low reputation is the main reason why investors are increasingly worried, not to mention that the current drug development process needs a big dose of digital innovation to deal with its messy data. As a matter of fact, Stefan Oelrich member of the Board Management of Bayer AG, President Pharmaceuticals, wrote in an article -- that the title perfectly summarises the AI pharma situation "Artificial Intelligence - When we Suddenly Know What we Don't Know" -- the following: "As we open the first doors in this unknown land we start to discover how much more is out there for our entire pharmaceutical value chain spanning from research to product supply. I expect AI to help us know what we have not known so far. Artificial Intelligence will become instrumental in our search for new medicines to better serve patients around the world as we leverage Science For A Better Life".