To ensure the security and confidentiality of various data types (including text, images, audio, and video), this paper proposes a multi-wavelet figure-and-text hiding algorithm (MWFTHA) and its corresponding multi-wavelet figure-and-text restoration algorithm (MWFTRA). These algorithms facilitate the encoding and embedding of text and color images into a one-dimensional signal through multi-wavelet transforms. Text data is encoded using a character dataset, while color images are processed via a linear transformation before being integrated into the signal. Subsequently, the original […]

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arXiv:2601.21455v1 Announce Type: new Abstract: Conformal prediction (CP) has become a cornerstone of distribution-free uncertainty quantification, conventionally evaluated by its coverage and interval length. This work critically examines the sufficiency of these standard metrics. We demonstrate that the interval length might be deceptively improved through a counter-intuitive approach termed Prejudicial Trick (PT), while the coverage remains valid. Specifically, for any given test sample, PT probabilistically returns an interval, which is either null or constructed using an adjusted confidence […]

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I have an episodic problem which always takes 30 days to complete, and each time step takes 1 day. Also, at any given time, there are around 1000 episodes simultaneously running (although start dates might be different). That means each day around 33 new episodes start and another 33 end. The action space is discrete (5 different actions). Which kind of algorithms would be good for this type problem? submitted by /u/diepala [link] [comments]

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Discussions and questions about methods, approaches, and tools for estimating (relative) binding free energies of protein-ligand complexes are quite popular, and even the simplest tools can be quite tricky to use. Here, I want to briefly summarize the idea of molecular docking, and give a short overview about how we can use AutoDock 4.2’s hybrid approach for evaluating binding affinities.

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Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with verifiable ground truths. Extending GRPO to open-domain settings remains a critical challenge, as unconstrained generation entails multi-faceted and often conflicting objectives – such as creativity versus factuality – where rigid, static reward scalarization is inherently suboptimal. To address this, we propose MAESTRO (Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization), which introduces […]

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arXiv:2601.05259v1 Announce Type: new Abstract: In this work, we address the challenge of multilingual category relevance judgment in e-commerce search, where traditional ensemble-based systems improve accuracy but at the cost of heavy training, inference, and maintenance complexity. To overcome this limitation, we propose a simplified yet effective framework that leverages prompt engineering with Chain-of-Thought task decomposition to guide reasoning within a single large language model. Specifically, our approach decomposes the relevance judgment process into four interpretable subtasks: translation, […]

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arXiv:2601.20080v1 Announce Type: new Abstract: Finding a particular object in a display is important for viewers in many visualizations, for example, when reacting to brushing or to a highlighted object. This can be enabled by making the target object different in one of the visual variables that determine the object’s appearance; for example, by changing its color or size. Certain interpretations of the visual search literature have promoted the view that using visual variables such as hue-often labeled […]

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Here, I want to present a simple and conservative approach of implementing a weighted majority rule ensemble classifier in scikit-learn that yielded remarkably good results when I tried it in a kaggle competition. For me personally, kaggle competitions are just a nice way to try out and compare different approaches and ideas — basically an opportunity to learn in a controlled environment with nice datasets.

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