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Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution

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arXiv 2024年

作者： Xiong, Jiaqi Yin, Nan Liang, Shiyang Li, Haoyang Wang, Yingxu Ai, Duo Pan, Fang Wang, Jingjie Department of Gastroenterology Tangdu Hospital Fourth Military Medical University Shaanxi 710038 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou528225 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Internal Medicine The No. 944 Hospital of Joint Logistic Support Force of PLA Xiongguan Road Jiu Quan735000 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Dermatology Xijing Hospital Fourth Military Medical University No 127 of West Changle Road Shaanxi Xi’an710032 China

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://***/kikixiong/XATGRN. © 2024, CC BY.

关键词： Graph embeddings

LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

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arXiv 2024年

作者： Li, Zhenshi Muhtar, Dilxat Gu, Feng Zhang, Xueliang Xiao, Pengfeng He, Guangjun Zhu, Xiaoxiang Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing210023 China Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources Nanjing210023 China School of Geography and Ocean Science Nanjing University Nanjing210023 China Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich Bavaria Munich80333 Germany Munich Center for Machine Learning Bavaria Munich80333 Germany State Key Laboratory of Space-Ground Integrated Information Technology Beijing100095 China

Automatically and rapidly understanding Earth’s surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth’s surface to address a wide range of human needs. The emergence of multimodal large language models (MLLMs) has great potential in boosting the efficiency and convenience of intelligent Earth observation. These models can engage in human-like conversations, serve as unified platforms for understanding images, follow diverse instructions, and provide insightful feedbacks. In this study, we introduce LHRS-Bot-Nova, an MLLM specialized in understanding remote sensing (RS) images, designed to expertly perform a wide range of RS understanding tasks aligned with human instructions. LHRS-Bot-Nova features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large-scale RS image-caption dataset, generated through feature-guided image recaptioning. Additionally, we introduce an instruction dataset specifically designed to improve spatial recognition abilities. Extensive experiments demonstrate superior performance of LHRS-Bot-Nova across various RS image understanding tasks. We also evaluate different MLLM performances in complex RS perception and instruction following using a complicated multi-choice question evaluation benchmark, providing a reliable guide for future model selection and improvement. data, code, and models will be available at https://***/NJU-LHRS/LHRS-Bot. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

The Alchemical Integral Transform revisited

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arXiv 2023年

作者： Krug, Simon León von Lilienfeld, O. Anatole Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada

We recently introduced the Alchemical Integral Transform (AIT) enabling the prediction of energy differences, and guessed an Ansatz to parametrize space r in some alchemical change λ. Here, we present a rigorous derivation of AIT’s kernel K and discuss the parametrization r(λ) in n dimensions, i.e. necessary conditions, mathematical freedoms and additional constraints when obtaining it. Analytical expressions for changes in energy spectra and densities are given for a number of systems. Examples include homogeneous potentials like the quantum harmonic oscillator, Hydrogen-like atom, and Dirac well, both for one- and multiparticle cases, and a multiparticle system beyond coordinate scaling for harmonic potentials. © 2023, CC BY.

关键词： Choquet integral

Exploration of Process Mining Opportunities In Educational Software Engineering - The GitLab Analyser 13

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Exploration of Process Mining Opportunities In Educational S...

13th International Conference on Educational data Mining, EDM 2020

作者： Dumbach, Philipp Aly, Alexander Zrenner, Markus Eskofier, Bjoern M. Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Carl-Thiersch-Str. 2b Erlangen91052 Germany

ISBN: (纸本)9781733673617

The increasing complexity in software development leads to the necessity for a detailed data analysis. Literature illustrates a stronger research focus on Educational Process Mining (EPM) being applied to the fields of e-learning and professional training. In this work, the opportunities of Process Mining (PM) are further examined by the evaluation of software engineering (SE) courses. The methodology follows the five stages of the L* life cycle model for PM projects using data from software repositories. The event log data was analyzed with the PM tool Disco to examine the students’ work following an agile development process. The new tool GitLab Analyser supports supervisors to visualize educational processes and still extracts event logs for the further analysis and application of PM techniques. © 2020 Proceedings of the 13th International Conference on Educational data Mining, EDM 2020. All rights reserved.

关键词： data mining

Is L2 physics-informed loss always suitable for training physics-informed neural network? 22

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Is L2 physics-informed loss always suitable for training phy...

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Chuwei Wang Shanda Li Di He Liwei Wang School of Mathematical Sciences Peking University Machine Learning Department School of Computer Science Carnegie Mellon University and Zhejiang Lab National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Center for Data Science Peking University

ISBN: (纸本)9781713871088

The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L2 Physics- Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero. With this concept, we study an important class of high-dimensional non-linear PDEs in optimal control, the Hamilton-Jacobi- Bellman (HJB) Equation, and prove that for general Lp Physics-Informed Loss, a wide class of HJB equation is stable only if p is sufficiently large. Therefore, the commonly used L2 loss is not suitable for training PINN on those equations, while L∞ loss is a better choice. Based on the theoretical insight, we develop a novel PINN training algorithm to minimize the L∞ loss for HJB equations which is in a similar spirit to adversarial training. The effectiveness of the proposed algorithm is empirically demonstrated through experiments.

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Modeling non-genetic information dynamics in cells using reservoir computing

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arXiv 2023年

作者： Niraula, Dipesh Naqa, Issam El Tuszynski, Jack Adam Gatenby, Robert A. Department of Machine Learning Moffitt Cancer Center TampaFL United States Departments of Physics and Oncology University of Alberta EdmontonAB Canada Department of Data Science and Engineering The Silesian University of Technology Gliwice44-100 Poland Department of Mechanical and Aerospace Engineering Politecnico di Torino TurinI-10129 Italy Departments of Radiology and Integrated Mathematical Oncology Moffitt Cancer Center TampaFL United States

Virtually all cells use energy and ion-specific membrane pumps to maintain large transmembrane gradients of Na+, K+, Cl−, Mg++, and Ca++. Although they consume up to 1/3 of a cell’s energy budget, the corresponding evolutionary benefit of transmembrane ion gradients remain unclear. Here, we propose that ion gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes of cytoplasmic ion concentration can generate a local response and orchestrate global or regional responses through wire-like ion fluxes along preexisting and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus. Here, we frame our hypothesis through a quasi-physical (Cell-Reservoir) model that treats intra-cellular ion-based information dynamics as a sub-cellular process permitting spatiotemporally resolved cellular response that is also capable of learning complex nonlinear dynamical cellular behavior. We demonstrate the proposed ion dynamics permits rapid dissemination of response to information extrinsic perturbations that is consistent with experimental observations. © 2023, CC BY.

关键词： Ions

Uncertainty quantification for sparse Fourier recovery

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arXiv 2022年

作者： Hoppe, Frederik Krahmer, Felix Verdun, Claudio Mayrink Menzel, Marion I. Rauhut, Holger Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Munich Germany Department of Mathematics Department of Electrical and Computer Engineering Technical University of Munich Munich Center for Machine Learning Munich Germany AImotion Bavaria Faculty of Electrical Engineering and Information Technology Technische Hochschule Ingolstadt Ingolstadt Department of Physics Technical University of Munich Garching and GE Healthcare Munich Germany Department of Mathematics LMU Munich Germany

One of the most prominent methods for uncertainty quantification in high-dimensional statistics is the desparsified LASSO that relies on unconstrained 1-minimization. The majority of initial works focused on real (sub-)Gaussian designs. However, in many applications, such as magnetic resonance imaging (MRI), the measurement process possesses a certain structure due to the nature of the problem. The measurement operator in MRI can be described by a subsampled Fourier matrix. The purpose of this work is to extend the uncertainty quantification process using the desparsified LASSO to design matrices originating from a bounded orthonormal system, which naturally generalizes the subsampled Fourier case and also allows for the treatment of the case where the sparsity basis is not the standard basis. In particular we construct honest confidence intervals for every pixel of an MR image that is sparse in the standard basis provided the number of measurements satisfies n Á maxts log2 s log p, s log2 pu or that is sparse with respect to the Haar wavelet basis provided a slightly larger number of measurements. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

HOW DOES THE BRAIN COMPUTE WITH PROBABILITIES?

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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

The effect of differential victim crime reporting on predictive policing systems

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arXiv 2021年

作者： Akpinar, Nil-Jana De-Arteaga, Maria Chouldechova, Alexandra Department of Statistics and Data Science & Machine Learning Department Carnegie Mellon University United States Information Risk and Operations Management Department McCombs School of Business University of Texas at Austin United States Heinz College Department of Statistics and Data Science Carnegie Mellon University United States

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation1 patterned after district-level victimization and crime reporting survey data for Bogotá, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing. © 2021, CC BY-NC-SA.

关键词： Crime