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Papers and Code
PEAR: A Robust and Flexible Automation Framework for Ptychography Enabled by Multiple Large Language Model Agents
Oct 11, 2024
Ptychography is an advanced computational imaging technique in X-ray and electron microscopy. It has been widely adopted across scientific research fields, including physics, chemistry, biology, and materials science, as well as in industrial applications such as semiconductor characterization. In practice, obtaining high-quality ptychographic images requires simultaneous optimization of numerous experimental and algorithmic parameters. Traditionally, parameter selection often relies on trial and error, leading to low-throughput workflows and potential human bias. In this work, we develop the "Ptychographic Experiment and Analysis Robot" (PEAR), a framework that leverages large language models (LLMs) to automate data analysis in ptychography. To ensure high robustness and accuracy, PEAR employs multiple LLM agents for tasks including knowledge retrieval, code generation, parameter recommendation, and image reasoning. Our study demonstrates that PEAR's multi-agent design significantly improves the workflow success rate, even with smaller open-weight models such as LLaMA 3.1 8B. PEAR also supports various automation levels and is designed to work with customized local knowledge bases, ensuring flexibility and adaptability across different research environments.
Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
Oct 11, 2024
Foundation models (FMs) such as large language models (LLMs) have significantly impacted many fields, including software engineering (SE). The interaction between SE and FMs has led to the integration of FMs into SE practices (FM4SE) and the application of SE methodologies to FMs (SE4FM). While several literature surveys exist on academic contributions to these trends, we are the first to provide a practitioner's view. We analyze 155 FM4SE and 997 SE4FM blog posts from leading technology companies, leveraging an FM-powered surveying approach to systematically label and summarize the discussed activities and tasks. We observed that while code generation is the most prominent FM4SE task, FMs are leveraged for many other SE activities such as code understanding, summarization, and API recommendation. The majority of blog posts on SE4FM are about model deployment & operation, and system architecture & orchestration. Although the emphasis is on cloud deployments, there is a growing interest in compressing FMs and deploying them on smaller devices such as edge or mobile devices. We outline eight future research directions inspired by our gained insights, aiming to bridge the gap between academic findings and real-world applications. Our study not only enriches the body of knowledge on practical applications of FM4SE and SE4FM but also demonstrates the utility of FMs as a powerful and efficient approach in conducting literature surveys within technical and grey literature domains. Our dataset, results, code and used prompts can be found in our online replication package at https://github.com/SAILResearch/fmse-blogs.
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
Oct 11, 2024Empirical, benchmark-driven testing is a fundamental paradigm in the current RL community. While using off-the-shelf benchmarks in reinforcement learning (RL) research is a common practice, this choice is rarely discussed. Benchmark choices are often done based on intuitive ideas like "legged robots" or "visual observations". In this paper, we argue that benchmarking in RL needs to be treated as a scientific discipline itself. To illustrate our point, we present a case study on different variants of the Hopper environment to show that the selection of standard benchmarking suites can drastically change how we judge performance of algorithms. The field does not have a cohesive notion of what the different Hopper environments are representative - they do not even seem to be representative of each other. Our experimental results suggests a larger issue in the deep RL literature: benchmark choices are neither commonly justified, nor does there exist a language that could be used to justify the selection of certain environments. This paper concludes with a discussion of the requirements for proper discussion and evaluations of benchmarks and recommends steps to start a dialogue towards this goal.
Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
Oct 11, 2024In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatments within specific budget constraints is critical. Despite recent advancements in estimating counterfactual trajectories, a direct link to optimal treatment selection based on these estimates is missing. This paper introduces a novel method integrating counterfactual estimation techniques and uncertainty quantification to recommend personalized treatment plans adhering to predefined cost constraints. Our approach is distinctive in its handling of continuous treatment variables and its incorporation of uncertainty quantification to improve prediction reliability. We validate our method using two simulated datasets, one focused on the cardiovascular system and the other on COVID-19. Our findings indicate that our method has robust performance across different counterfactual estimation baselines, showing that introducing uncertainty quantification in these settings helps the current baselines in finding more reliable and accurate treatment selection. The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.
Data Processing for the OpenGPT-X Model Family
Oct 11, 2024This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.
Causal machine learning for predicting treatment outcomes
Oct 11, 2024Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
* Accepted version; not Version of Record
Intent-Enhanced Data Augmentation for Sequential Recommendation
Oct 11, 2024The research on intent-enhanced sequential recommendation algorithms focuses on how to better mine dynamic user intent based on user behavior data for sequential recommendation tasks. Various data augmentation methods are widely applied in current sequential recommendation algorithms, effectively enhancing the ability to capture user intent. However, these widely used data augmentation methods often rely on a large amount of random sampling, which can introduce excessive noise into the training data, blur user intent, and thus negatively affect recommendation performance. Additionally, these methods have limited approaches to utilizing augmented data, failing to fully leverage the augmented samples. We propose an intent-enhanced data augmentation method for sequential recommendation(\textbf{IESRec}), which constructs positive and negative samples based on user behavior sequences through intent-segment insertion. On one hand, the generated positive samples are mixed with the original training data, and they are trained together to improve recommendation performance. On the other hand, the generated positive and negative samples are used to build a contrastive loss function, enhancing recommendation performance through self-supervised training. Finally, the main recommendation task is jointly trained with the contrastive learning loss minimization task. Experiments on three real-world datasets validate the effectiveness of our IESRec model.
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities
Oct 11, 2024The advent of AI has influenced many aspects of human life, from self-driving cars and intelligent chatbots to text-based image and video generation models capable of creating realistic images and videos based on user prompts (text-to-image, image-to-image, and image-to-video). AI-based methods for image and video super resolution, video frame interpolation, denoising, and compression have already gathered significant attention and interest in the industry and some solutions are already being implemented in real-world products and services. However, to achieve widespread integration and acceptance, AI-generated and enhanced content must be visually accurate, adhere to intended use, and maintain high visual quality to avoid degrading the end user's quality of experience (QoE). One way to monitor and control the visual "quality" of AI-generated and -enhanced content is by deploying Image Quality Assessment (IQA) and Video Quality Assessment (VQA) models. However, most existing IQA and VQA models measure visual fidelity in terms of "reconstruction" quality against a pristine reference content and were not designed to assess the quality of "generative" artifacts. To address this, newer metrics and models have recently been proposed, but their performance evaluation and overall efficacy have been limited by datasets that were too small or otherwise lack representative content and/or distortion capacity; and by performance measures that can accurately report the success of an IQA/VQA model for "GenAI". This paper examines the current shortcomings and possibilities presented by AI-generated and enhanced image and video content, with a particular focus on end-user perceived quality. Finally, we discuss open questions and make recommendations for future work on the "GenAI" quality assessment problems, towards further progressing on this interesting and relevant field of research.
Personalized Item Embeddings in Federated Multimodal Recommendation
Oct 11, 2024Federated recommendation systems play a crucial role in protecting user privacy. However, existing methods primarily rely on ID-based item embeddings, overlooking the rich multimodal information of items. To address this limitation, we propose a novel Federated Multimodal Recommendation System called FedMR. FedMR leverages a foundation model on the server side to encode multimodal data, such as images and text, associated with items. To tackle the challenge of data heterogeneity caused by varying user preferences, FedMR introduces a Mixing Feature Fusion Module on the client. This module dynamically adjusts the weights of different fusion strategies based on user interaction history, generating personalized item embeddings that capture fine-grained user preferences. FedMR is compatible with existing ID-based federated recommendation systems, improving their performances without modifying the original framework. Our experiments on four real-world multimodal recommendation datasets demonstrate the effectiveness of FedMR. Our code is available at https://anonymous.4open.science/r/FedMR.
Deconstructing equivariant representations in molecular systems
Oct 10, 2024Recent equivariant models have shown significant progress in not just chemical property prediction, but as surrogates for dynamical simulations of molecules and materials. Many of the top performing models in this category are built within the framework of tensor products, which preserves equivariance by restricting interactions and transformations to those that are allowed by symmetry selection rules. Despite being a core part of the modeling process, there has not yet been much attention into understanding what information persists in these equivariant representations, and their general behavior outside of benchmark metrics. In this work, we report on a set of experiments using a simple equivariant graph convolution model on the QM9 dataset, focusing on correlating quantitative performance with the resulting molecular graph embeddings. Our key finding is that, for a scalar prediction task, many of the irreducible representations are simply ignored during training -- specifically those pertaining to vector ($l=1$) and tensor quantities ($l=2$) -- an issue that does not necessarily make itself evident in the test metric. We empirically show that removing some unused orders of spherical harmonics improves model performance, correlating with improved latent space structure. We provide a number of recommendations for future experiments to try and improve efficiency and utilization of equivariant features based on these observations.