In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings. Improving the calibration of classifiers is crucial for enhancing the trustworthiness and interpretability of machine learning models, especially in sensitive decision-making scenarios. Although various calibration (error) estimators exist in the current literature, there is a lack of guidance on selecting the appropriate estimator and tuning its hyperparameters. By leveraging the bilinear structure of squared calibration errors, we reformulate calibration estimation as a regression problem with independent and identically distributed (i.i.d.) input pairs. This reformulation allows us to quantify the performance of different estimators even for the most challenging calibration criterion, known as canonical calibration. Our approach advocates for a training-validation-testing pipeline when estimating a calibration error on an evaluation dataset. We demonstrate the effectiveness of our pipeline by optimizing existing calibration estimators and comparing them with novel kernel ridge regression-based estimators on standard image classification tasks.

The increasing need for competent computing graduates proficient in programming, software development, and related technical competencies [Ca17] is one of the factors exacerbating pressure on higher education institutions to offer high quality, competency-based education [Ra21]. However, the latter requires extensive resources, mentoring, and, for example, formative feedback for learners, especially in introductory programming classes [Je22; Lo24]. This is due to the fact that novices experience a number of challenges in the process, which have been subject to extensive research in the past decades [Du86; Lu18; SS86]. Among them are cognitively demanding competencies [Ki20; Ki24], such as problem understanding, designing and writing algorithms, debugging, and understanding error messages [Du86; ER16; Ki20; Lu18; SS86]). Educators' expectations towards novice learners and what they can achieve in their first semester(s) seem to be too high and unrealistic [Lu16; Lu18; WCL07]. Moreover, the student-educator ratio in introductory programming classes keeps increasing in German higher education institutions, thereby limiting resources to provide feedback and hints, and adequately address heterogeneous prior knowledge and diverse educational biographies [Pe16; SB22].

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [-1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented dataset while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost effective models for black hole image generation and reliably augment training datasets for other parameterization algorithms.

Murat Durmus is CEO and founder of AISOMA. A Frankfurt am Main (Germany) based company specialized in AI-based technology development and consulting. Besides being an entrepreneur, he devotes most of his time to philosophy and AI's impacts on society.

For more than a decade, molecular biologist Martin Beck and his colleagues have been trying to piece together one of the world's hardest jigsaw puzzles: a detailed model of the largest molecular machine in human cells. This behemoth, called the nuclear pore complex, controls the flow of molecules in and out of the nucleus of the cell, where the genome sits. Hundreds of these complexes exist in every cell. Each is made up of more than 1,000 proteins that together form rings around a hole through the nuclear membrane. These 1,000 puzzle pieces are drawn from more than 30 protein building blocks that interlace in myriad ways. Making the puzzle even harder, the experimentally determined 3D shapes of these building blocks are a potpourri of structures gathered from many species, so don't always mesh together well. And the picture on the puzzle's box -- a low-resolution 3D view of the nuclear pore complex -- lacks sufficient detail to know how many of the pieces precisely fit together. In 2016, a team led by Beck, who is based at the Max Planck Institute of Biophysics (MPIBP) in Frankfurt, Germany, reported a model1 that covered about 30% of the nuclear pore complex and around half of the 30 building blocks, called Nup proteins.

About a year ago, Frankfurt's Lord Mayor Peter Feldmann had the impulse to launch an AI initiative. Stefan Jäger, a speaker in the Lord Mayor's office and honorary board member of the association, let us know what Feldmann had in mind: "As always with new technologies, citizens have difficulty imagining what artificial intelligence actually is in this case. The association wants to acquire and share knowledge. Only transparency will make people curious." And Dr Thorsten Pötter wants to help him do so.

Robots have been a mainstay in factories for decades, but their use has been expanding everywhere else, from warehouses and hospitals to retail. That trend continued last year, and the novel coronavirus pandemic has accelerated service robot demand for automated logistics, disinfection, and delivery, according to the International Federation of Robotics. The Frankfurt, Germany-based IFR said that the sales value of professional service robots increased by 32% to $11.2 billion (U.S.) worldwide between 2018 and 2019. The organization published its full research in the "World Robotics 2020 – Service Robots" report, which is available for download. Sales of medical robotics accounted for 47% of the total service robot value turnover in 2019, according to the IFR.

Fintech Europe, Plug and Play's fintech-focused innovation platform based out of Frankfurt, Germany, announced today the eight startups selected for its sixth batch. The platform has grown its partner base to 13 Financial Institutions since its inception in May 2018. Together with Deutsche Bank, TechQuartier, BNP Paribas, Nets Group, Nexi, UniCredit, Aareal Bank, Abanca, Danske Bank, DZ Bank, Elo, UBI Banca, and Raiffeisen Bank International, the program seeks to support innovation in the world of Financial Services. After screening applications from all over the world and intensive weeks of reviewing preselected startups with the partners, the final group of eight companies have been accepted into Fintech Europe. The program aims at facilitating pilots, POCs, and business development opportunities for the participating startups and financial institutions.

A journalist rode in a driverless vehicle last month. The tricked-out minivan navigated busy city streets, made an unprotected left-hand turn and even reached speeds of 45 mph. According to a TechCrunch report, it was a completely uneventful, yet remarkable ride in a Waymo fully autonomous vehicle. FRANKFURT AM MAIN, GERMANY - SEPTEMBER 12: John Krafcik, CEO of Waymo, speaks at the opening event ... [ ] of the IAA 2019 Frankfurt Auto Show on September 12, 2019 in Frankfurt am Main, Germany. The IAA will be open to the public from September 12 through 22. (Photo by Sean Gallup/Getty Images) Self-driving vehicles reached peak hype a year ago.

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