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(Tee11/Shutterstock) How we store and serve data are critical factors in what we can do with data, and today we want to do oh-so much. That big data necessity is the mother of all invention, and over the past 20 years, it has spurred an immense amount of database creativity, from MapReduce and array databases to NoSQL and vector DBs. It all seems so promising…and then Mike Stonebraker enters the room. For half a century, Stonebraker has been churning out the database designs at a furious pace. The Turing Award winner made his early mark with Ingres and Postgres. However,…

arXiv:2407.03728v1 Announce Type: new Abstract: In unsupervised representation learning, models aim to distill essential features from high-dimensional data into lower-dimensional learned representations, guided by inductive biases. Understanding the characteristics that make a good representation remains a topic of ongoing research. Disentanglement of independent generative processes has long been credited with producing high-quality representations. However, focusing solely on representations that adhere to the stringent requirements of most disentanglement metrics, may result in overlooking many high-quality representations, well suited for various downstream tasks. These metrics often demand that generative factors be encoded in distinct, single dimensions aligned with the canonical basis of…

arXiv:2407.03535v1 Announce Type: new Abstract: Low-light videos often exhibit spatiotemporal incoherent noise, compromising visibility and performance in computer vision applications. One significant challenge in enhancing such content using deep learning is the scarcity of training data. This paper introduces a novel low-light video dataset, consisting of 40 scenes with various motion scenarios under two distinct low-lighting conditions, incorporating genuine noise and temporal artifacts. We provide fully registered ground truth data captured in normal light using a programmable motorized dolly and refine it via an image-based approach for pixel-wise frame alignment across different light levels. We provide benchmarks based on four…

arXiv:2407.03624v1 Announce Type: new Abstract: Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations, but this research explores the question: Does making the LLM analyze the question improve its performance? We propose a novel prompting strategy called Question Analysis Prompting (QAP), in which the model is prompted to explain the question in $n$ words before solving. The value of $n$ influences the length of response generated by the model. QAP is evaluated on GPT 3.5 Turbo and GPT 4 Turbo on arithmetic datasets GSM8K,…