pwc-archive/papers-with-abstracts huggingface dataset:
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
Authors: Alexander Strehl, Joydeep Ghosh
Date: 2002-12-01 00:00:00
Proceeding: JMLR 2002 12
Tasks: Clustering, Combinatorial Optimization, hypergraph partitioning

This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.
HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations
Authors: Erich Schubert
Date: 2021-09-01 00:00:00
Proceeding: Lernen, Wissen, Daten, Analysen 2021 9
Tasks: Clustering

Partitioning Around Medoids (PAM) is a popular and flexible clustering method. Also known by the name 𝑘-Medoids clustering, and originally conceived for the 𝐿1-norm, it can be used to cluster data into 𝑘 partitions with respect to an arbitrary distance or similarity measure. The ability to work with any distance makes this method more widely applicable than, for example, 𝑘-means clustering. Similar to 𝑘-means, a challenge when using PAM is the need to choose the number of clusters, 𝑘, before running the algorithm. In many cases, the “optimal” 𝑘 will not be known beforehand, and the user may need to run PAM several times with different 𝑘 and rely on additional heuristics to pick the “best” result. We introduce the algorithm Hierarchical Agglomerative Clustering Around Medoids (HACAM), a combination of ideas from classic hierarchical agglomerative clustering (HAC), but where points are clustered around medoids. In our approach, each subtree of the dendrogram has a representative point, which is the medoid: the point with the smallest average distance to all others. In contrast to the arithmetic mean, this does not make assumptions on the data representation or distance function used. Unfortunately, we also show that the requirement to produce a hierarchical result is a limiting factor to the cluster quality, as the optimum result for a particular number of clusters 𝑘 does not have to be consistent with the optimum result with 𝑘+1 clusters. Hence, if a range of interesting values of 𝑘 is known beforehand, existing algorithms such as FasterPAM remain favorable.
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
Authors: Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, Xin Wang
Date: 2022-08-14 00:00:00
Proceeding: SIGKDD 2022 8
Tasks: Few-Shot Learning, Node Classification, Node Property Prediction, Representation Learning, Transfer Learning

Despite the promising representation learning of graph neural networks (GNNs), the supervised training of GNNs notoriously requires large amounts of labeled data from each application. An effective solution is to apply the transfer learning in graph: using easily accessible information to pre-train GNNs, and fine-tuning them to optimize the downstream task with only a few labels. Recently, many efforts have been paid to design the self-supervised pretext tasks, and encode the universal graph knowledge among the various applications. However, they rarely notice the inherent training objective gap between the pretext and downstream tasks. This significant gap often requires costly fine-tuning for adapting the pre-trained model to downstream problem, which prevents the efficient elicitation of pre-trained knowledge and then results in poor results. Even worse, the naive pre-training strategy usually deteriorates the downstream task, and damages the reliability of transfer learning in graph data. To bridge the task gap, we propose a novel transfer learning paradigm to generalize GNNs, namely graph pre-training and prompt tuning (GPPT). Specifically, we first adopt the masked edge prediction, the most simplest and popular pretext task, to pre-train GNNs. Based on the pre-trained model, we propose the graph prompting function to modify the standalone node into a token pair, and reformulate the downstream node classification looking the same as edge prediction. The token pair is consisted of candidate label class and node entity. Therefore, the pre-trained GNNs could be applied without tedious fine-tuning to evaluate the linking probability of token pair, and produce the node classification decision. The extensive experiments on eight benchmark datasets demonstrate the superiority of GPPT, delivering an average improvement of 4.29% in few-shot graph analysis and accelerating the model convergence up to 4.32X. The code is available in: https://github.com/MingChen-Sun/GPPT.
Improving the Cluster Structure Extracted from OPTICS Plots
Authors: Erich Schubert, Michael Gertz
Date: 2018-08-01 00:00:00
Proceeding: Lernen, Wissen, Daten, Analysen 2018 8

Density-based clustering is closely associated with the two algorithms DBSCAN and OPTICS. While the first finds clusters connected at a single density threshold, the latter allows the extraction of a cluster hierarchy based on different densities. Extraction methods for clusters from OPTICS rely on an intermediate representation, known as the OPTICS plot. In this plot, which can be seen as a density profile of the data set, valleys (areas of higher density) are associated with clusters. Multiple methods for automatic detecting such valleys have been proposed, but all of them tend to produce a particular artifact, where some point is included in the cluster that may be far away from the remainder. In this article, we will discuss this commonly seen artifact, and propose a simple but sound way of removing the artifacts. At the same time, this change is minimally invasive, and tries to keep the existing algorithms largely intact for future study.
A Framework for Clustering Uncertain Data
Authors: Erich Schubert, Alexander Koos, Tobias Emrich, Andreas Zufle, Klaus Arthur Schmid, Arthur Zimek
Date: 2015-08-01 00:00:00
Proceeding: VLDB 2015 8
Tasks: Clustering, Outlier Detection

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty—using different uncertainty models—on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.
On the Soundness of XAI in Prognostics and Health Management (PHM)
Authors: David Solis-Martin, Juan Galan-Paez, Joaquin Borrego-Diaz
Date: 2023-04-24 00:00:00
Proceeding: Information (MDPI) 2023 4
Tasks: Explainable Artificial Intelligence (XAI), Management, regression, Time Series, Time Series Classification, Time Series Regression

The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing.
Motion-robust free-running volumetric cardiovascular MRI
Authors: Syed M. Arshad, Lee C. Potter, Chong Chen, Yingmin Liu, Preethi Chandrasekaran, Christopher Crabtree, Matthew S. Tong, Orlando P. Simonetti, Yuchi Han, Rizwan Ahmad
Date: 2023-08-04 00:00:00
Tasks: compressed sensing, SSIM

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications. CODE & DATA: Implementation code and datasets are available on GitHub at http://github.com/OSU-MR/motion-robust-CMR
GEMRec: Towards Generative Model Recommendation
Authors: Yuanhe Guo, Haoming Liu, Hongyi Wen
Date: 2023-08-04 00:00:00
Tasks: Articles, Image Generation, model, Recommendation Systems, Retrieval

Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent advances in Generative AI such as GPT and Diffusion models, a new form of recommendation task is yet to be explored where items are to be created by generative models with personalized prompts. Taking image generation as an example, with a single prompt from the user and access to a generative model, it is possible to generate hundreds of new images in a few minutes. How shall we attain personalization in the presence of "infinite" items? In this preliminary study, we propose a two-stage framework, namely Prompt-Model Retrieval and Generated Item Ranking, to approach this new task formulation. We release GEMRec-18K, a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. Our findings demonstrate the promise of generative model recommendation as a novel personalization problem and the limitations of existing evaluation metrics. We highlight future directions for the RecSys community to advance towards generative recommender systems. Our code and dataset are available at https://github.com/MAPS-research/GEMRec.
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
Authors: Haoyi Xiu; Xin Liu; Weimin Wang; Kyoung-Sook Kim; Masashi Matsuoka
Date: 2023-08-04 00:00:00
Tasks: Edge Detection, Surface Normals Estimation, Surface Reconstruction

Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation perform well in regions where normals change slowly, they tend to fail where normals vary rapidly. To address this issue, we propose a novel approach called MSECNet, which improves estimation in normal varying regions by treating normal variation modeling as an edge detection problem. MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream. The MSEC stream achieves robust edge detection through multi-scale feature fusion and adaptive edge detection. The detected edges are then combined with the output of the backbone network using the edge conditioning module to produce edge-aware representations. Extensive experiments show that MSECNet outperforms existing methods on both synthetic (PCPNet) and real-world (SceneNN) datasets while running significantly faster. We also conduct various analyses to investigate the contribution of each component in the MSEC stream. Finally, we demonstrate the effectiveness of our approach in surface reconstruction.
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
Authors: Weihao Yu, Zhengyuan Yang, Linjie Li, JianFeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, Lijuan Wang
Date: 2023-08-04 00:00:00
Tasks: Math, MM-Vet, Zero-Shot Visual Question Answring

We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models.
Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration
Authors: Juan Del Aguila Ferrandis, João Moura, Sethu Vijayakumar
Date: 2023-08-04 00:00:00
Tasks: Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging. The underactuated and hybrid-dynamics nature of the problem, further complicated by the uncertainty resulting from the frictional interactions, requires sophisticated control behaviors. Reinforcement Learning (RL) is a powerful framework for developing such robot controllers. However, previous RL literature addressing the nonprehensile pushing task achieves low accuracy, non-smooth trajectories, and only simple motions, i.e. without rotation of the manipulated object. We conjecture that previously used unimodal exploration strategies fail to capture the inherent hybrid-dynamics of the task, arising from the different possible contact interaction modes between the robot and the object, such as sticking, sliding, and separation. In this work, we propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies for arbitrary starting and target object poses, i.e. positions and orientations, and with improved accuracy. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers. Furthermore, we validate the transferability of the learned policies, trained entirely in simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our supplemental video: https://youtu.be/vTdva1mgrk4.
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Authors: Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao
Date: 2023-08-04 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Missing Elements, Temporal Knowledge Graph Completion

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
Authors: David Oniani, Jordan Hilsman, Yifan Peng, COL, Ronald K. Poropatich, COL Jeremy C. Pamplin, LTC Gary L. Legault, Yanshan Wang
Date: 2023-08-04 00:00:00
Tasks: Decision Making

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare.
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Authors: Alberto Archetti, Francesca Ieva, Matteo Matteucci
Date: 2023-08-04 00:00:00
Tasks: Federated Learning, Survival Analysis

Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are often incomplete, censored, distributed, and confidential, especially in healthcare settings where privacy is critical. The scarcity of data can severely limit the scalability of survival models to distributed applications that rely on large data pools. Federated learning is a promising technique that enables machine learning models to be trained on multiple datasets without compromising user privacy, making it particularly well-suited for addressing the challenges of survival data and large-scale survival applications. Despite significant developments in federated learning for classification and regression, many directions remain unexplored in the context of survival analysis. In this work, we propose an extension of the Federated Survival Forest algorithm, called FedSurF++. This federated ensemble method constructs random survival forests in heterogeneous federations. Specifically, we investigate several new tree sampling methods from client forests and compare the results with state-of-the-art survival models based on neural networks. The key advantage of FedSurF++ is its ability to achieve comparable performance to existing methods while requiring only a single communication round to complete. The extensive empirical investigation results in a significant improvement from the algorithmic and privacy preservation perspectives, making the original FedSurF algorithm more efficient, robust, and private. We also present results on two real-world datasets demonstrating the success of FedSurF++ in real-world healthcare studies. Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy.