Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning.

Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aberrations. However, developing an AI model are hindered by the overwhelming complexity and diversity of chromosomal abnormalities, requiring extensive annotation efforts, while automated methods remain task-specific and lack generalizability due to the scarcity of comprehensive datasets spanning diverse resource conditions. Here, we introduce CHROMA, a foundation model for cytogenomics, designed to overcome these challenges by learning generalizable representations of chromosomal abnormalities. Pre -trained on over 84,000 specimens (~4 million chromosomal images) via self -supervised learning, CHROMA outperforms other methods across all types of abnormalities, even when trained on fewer labelled data and more imbalanced datasets. By facilitating comprehensive mapping of instability and clonal leisons across various aberration types, CHROMA offers a scalable and generalizable solution for reliable and automated clinical analysis, reducing the annotation workload for experts and advancing precision oncology through the early detection of rare genomic abnormalities, enabling broad clinical AI applications and making advanced genomic analysis more accessible.

This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach involves extensive pre-processing, segmenting audio signals into 30-second windows, and extracting high-dimensional feature representations through Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Temporal characteristics. Leveraging these features, we train regression models to predict sentiment scores on a 0-100 scale, achieving root mean square error (RMSE) values of 3.420, 5.482, 2.783, and 4.212, respectively. Improvements over a baseline model based on absolute difference metrics are observed. These results demonstrate the potential of machine learning to capture sentiment and similarity in audio, offering an adaptable framework for AI applications in media analysis.

Deep Neural Networks (DNNs) require large amounts of annotated training data for a good performance. Often this data is generated using manual labeling (error-prone and time-consuming) or rendering (requiring geometry and material information). Both approaches make it difficult or uneconomic to apply them to many small-scale applications. A fast and straightforward approach of acquiring the necessary training data would allow the adoption of deep learning to even the smallest of applications. Chroma keying is the process of replacing a color (usually blue or green) with another background. Instead of chroma keying, we propose luminance keying for fast and straightforward training image acquisition. We deploy a black screen with high light absorption (99.99\%) to record roughly 1-minute long videos of our target objects, circumventing typical problems of chroma keying, such as color bleeding or color overlap between background color and object color. Next we automatically mask our objects using simple brightness thresholding, saving the need for manual annotation. Finally, we automatically place the objects on random backgrounds and train a 2D object detector. We do extensive evaluation of the performance on the widely-used YCB-V object set and compare favourably to other conventional techniques such as rendering, without needing 3D meshes, materials or any other information of our target objects and in a fraction of the time needed for other approaches. Our work demonstrates highly accurate training data acquisition allowing to start training state-of-the-art networks within minutes.

OpenAI's DALL.E 2 has been making it big with text-to-image models that easily generate pictures from textual descriptions. Earlier this week, two biotech labs--Generate Biomedicines and David Baker's Group--relied on generative AI, particularly diffusion models, to come up with new protein structures and, eventually, better drugs. Boston-based therapeutics company Generate Biomedicines announced a programme called Chroma which, according to the company, is the "DALL-E 2 of biology". Similarly, biologist David Baker's team from the University of Washington has also come up with RoseTTAFoldDiffusion. The model can build accurate designs for new proteins that can be brought to life in the lab.

With artificial intelligence (AI) spreading rapidly across various industries, worldwide revenue from AI-based systems is predicted to reach 47 billion US dollars in 2020, according to International Data Corporation (IDC). Chroma ATE Inc., one of the world's leading suppliers of precision test and measurement instrumentation, automated test systems, intelligent manufacturing systems, and turnkey solutions, is aggressively taking part in AI by investing in Touch Cloud Inc. pioneered in AI solutions, as its largest corporate shareholder. Implementing AI into Chroma's core technologies will make their instrumentation and turnkey solutions smarter and a higher value to their customers. Touch Cloud Inc. specializes in cloud computing architecture, machine learning, and deep learning using its unique algorithms to provide big data analysis and prediction. Its AI engine can satisfy an automated factory's demands for high-volume and high-dimensional numerical data analysis, as well as defect detection and automated classification by optical inspection.