Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this paper, we present Axolotl (Automated cross Location-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer, (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the U.S., Japan, and Germany, using 3 as source regions and 9 of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.
Despite increasing focus on data publication and discovery in materials science and related fields, the global view of materials data is highly sparse. This sparsity encourages training models on the union of multiple datasets, but simple unions can prove problematic as (ostensibly) equivalent properties may be measured or computed differently depending on the data source. These hidden contextual differences introduce irreducible errors into analyses, fundamentally limiting their accuracy. Transfer learning, where information from one dataset is used to inform a model on another, can be an eff…
This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity and imbalance in the few-shot setting. Our analysis further suggests that to prevent catastrophic forgetting, actions need to be taken in the primitive stage -- the training of base classes instead of later few-shot learning sessions. Therefore, we propose to search for flat loc…
Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation
Department of Computer Science, University of Northern British Columbia（北坎伯兰大学计算机科学系）；Department of Computer Science, Western University（西部大学计算机科学系）；Juniper Networks
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computa…
Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model
Department of Nuclear Engineering, North Carolina State University Burlington Engineering Laboratories（核工程系，北卡罗来纳州立大学伯灵顿工程实验室）；Laboratory for Reactor Physics（反应堆物理实验室）；Thermal-Hydraulics, Center for Nuclear Engineering（热工实验室，核工程中心）；Sciences, Paul Scherrer Institute（科学，保罗·施特格研究所）
VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science
UCL School of Pharmacy, University College London（伦敦大学学院药学院）；UCL Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；UCL Cancer Institute, University College London（伦敦大学学院癌症研究所）
This paper proposes multiscale convolutional neural network (CNN)-based deep metric learning for bioacoustic classification, under low training data conditions. The proposed CNN is characterized by the utilization of four different filter sizes at each level to analyze input feature maps. This multiscale nature helps in describing different bioacoustic events effectively: smaller filters help in learning the finer details of bioacoustic events, whereas, larger filters help in analyzing a larger context leading to global details. A dynamic triplet loss is employed in the proposed CNN architectu…
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；Institute of Image Analysis and Computer Vision, Faculty of Informatics and Data Science, University of Regensburg（雷根斯堡大学信息学与数据科学学院图像分析与计算机视觉研究所）；Institute for Pathology, Hannover Medical School（汉诺威医学院病理学研究所）；Institute for Neuropathology, University Clinic Freiburg（弗莱堡大学医院神经病理学研究所）；Institute for Experimental Molecular Imaging, RWTH Aachen University（亚琛工业大学实验分子影像学研究所）
Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervised models are further handicapped by domain shifts, when the labeled dataset, despite being large enough, fails to cover different protocols or ethnicities. In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student sem…
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；The State Key Laboratory of Integrated Services Networks（综合业务网络国家重点实验室）；School of Network and Information Security, Xidian University（西安电子科技大学网络与信息安全学院）
Synthetic Series-Symbol Data Generation for Time Series Foundation Models
School of Telecommunications Engineering Xidian University（电子科技大学电信工程学院）；School of Artificial Intelligence Xidian University（电子科技大学人工智能学院）；School of Cyber Engineering Xidian University（电子科技大学网络安全工程学院）
Laboratory of Biological Modeling, NIDDK（NIDDK生物建模实验室）
Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various applications, computational requirements have limited applications to small data sets of a few thousand points. We present Dory, an efficient and scalable algorithm that can compute the persistent homology of large data sets. Dory uses significantly less memory than published algorithms and also provides significant reductions in the computation time compare…
The exponential growth of Location-based Social Networks (LBSNs) has greatly stimulated the demand for precise location-based recommendation services. Next Point-of-Interest (POI) recommendation, which aims to provide personalised POI suggestions for users based on their visiting histories, has become a prominent component in location-based e-commerce. Recent POI recommenders mainly employ self-attention mechanism or graph neural networks to model complex high-order POI-wise interactions. However, most of them are merely trained on the historical check-in data in a standard supervised learning…
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；RWTH Aachen University（亚琛工业大学）
The automated analysis of medical images is currently limited by technical and biological noise and bias. The same source tissue can be represented by vastly different images if the image acquisition or processing protocols vary. For an image analysis pipeline, it is crucial to compensate such biases to avoid misinterpretations. Here, we evaluate, compare, and improve existing generative model architectures to overcome domain shifts for immunofluorescence (IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine the performance of the generative models, the original and tran…
Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Hubei Luojia Laboratory, Wuhan University（湖北珞珈实验室）；Chinese Antarctic Center of Surveying and Mapping, Wuhan University（中国极地测绘南极科考中心）
