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  1. Book ; Online: AutoKG

    Chen, Bohan / Bertozzi, Andrea L.

    Efficient Automated Knowledge Graph Generation for Language Models

    2023  

    Abstract: Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics. To address these limitations, we introduce AutoKG, a lightweight and efficient ... ...

    Abstract Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics. To address these limitations, we introduce AutoKG, a lightweight and efficient approach for automated knowledge graph (KG) construction. For a given knowledge base consisting of text blocks, AutoKG first extracts keywords using a LLM and then evaluates the relationship weight between each pair of keywords using graph Laplace learning. We employ a hybrid search scheme combining vector similarity and graph-based associations to enrich LLM responses. Preliminary experiments demonstrate that AutoKG offers a more comprehensive and interconnected knowledge retrieval mechanism compared to the semantic similarity search, thereby enhancing the capabilities of LLMs in generating more insightful and relevant outputs.

    Comment: 10 pages, accepted by IEEE BigData 2023 as a workshop paper in GTA3
    Keywords Computer Science - Computation and Language
    Subject code 004
    Publishing date 2023-11-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Designer shocks for carving out microscale surface morphologies.

    Bertozzi, Andrea L

    Proceedings of the National Academy of Sciences of the United States of America

    2016  Volume 113, Issue 41, Page(s) 11384–11386

    Language English
    Publishing date 2016-10-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1615158113
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Book ; Online: Model-Change Active Learning in Graph-Based Semi-Supervised Learning

    Miller, Kevin / Bertozzi, Andrea L.

    2021  

    Abstract: Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting ...

    Abstract Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. "Model-change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

    Comment: Submitted to SIAM Journal on Mathematics of Data Science (SIMODS)
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-10-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images

    Chen, Bohan / Miller, Kevin / Bertozzi, Andrea L. / Schwenk, Jon

    2023  

    Abstract: We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled ...

    Abstract We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.

    Comment: 4 pages, 2 figures, 1 table. Accepted by IGARSS 2023
    Keywords Electrical Engineering and Systems Science - Image and Video Processing
    Publishing date 2023-06-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Active Learning of Non-semantic Speech Tasks with Pretrained Models

    Lee, Harlin / Saeed, Aaqib / Bertozzi, Andrea L.

    2022  

    Abstract: Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach generally assumes that the downstream tasks have access to annotated data of ...

    Abstract Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach generally assumes that the downstream tasks have access to annotated data of sufficient size. In this work, we propose ALOE, a novel system for improving the data- and label-efficiency of non-semantic speech tasks with active learning. ALOE uses pretrained models in conjunction with active learning to label data incrementally and learn classifiers for downstream tasks, thereby mitigating the need to acquire labeled data beforehand. We demonstrate the effectiveness of ALOE on a wide range of tasks, uncertainty-based acquisition functions, and model architectures. Training a linear classifier on top of a frozen encoder with ALOE is shown to achieve performance similar to several baselines that utilize the entire labeled data.

    Comment: Accepted at: ICASSP'23, Code: https://github.com/HarlinLee/ALOE
    Keywords Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing
    Subject code 006 ; 004
    Publishing date 2022-10-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Fluid dynamics alters liquid-liquid phase separation in confined aqueous two-phase systems.

    Hester, Eric W / Carney, Sean / Shah, Vishwesh / Arnheim, Alyssa / Patel, Bena / Di Carlo, Dino / Bertozzi, Andrea L

    Proceedings of the National Academy of Sciences of the United States of America

    2023  Volume 120, Issue 49, Page(s) e2306467120

    Abstract: Liquid-liquid phase separation is key to understanding aqueous two-phase systems (ATPS) arising throughout cell biology, medical science, and the pharmaceutical industry. Controlling the detailed morphology of phase-separating compound droplets leads to ... ...

    Abstract Liquid-liquid phase separation is key to understanding aqueous two-phase systems (ATPS) arising throughout cell biology, medical science, and the pharmaceutical industry. Controlling the detailed morphology of phase-separating compound droplets leads to new technologies for efficient single-cell analysis, targeted drug delivery, and effective cell scaffolds for wound healing. We present a computational model of liquid-liquid phase separation relevant to recent laboratory experiments with gelatin-polyethylene glycol mixtures. We include buoyancy and surface-tension-driven finite viscosity fluid dynamics with thermally induced phase separation. We show that the fluid dynamics greatly alters the evolution and equilibria of the phase separation problem. Notably, buoyancy plays a critical role in driving the ATPS to energy-minimizing crescent-shaped morphologies, and shear flows can generate a tenfold speedup in particle formation. Neglecting fluid dynamics produces incorrect minimum-energy droplet shapes. The model allows for optimization of current manufacturing procedures for structured microparticles and improves understanding of ATPS evolution in confined and flowing settings important in biology and biotechnology.
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2306467120
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Online: Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets

    Chapman, James / Chen, Bohan / Tan, Zheng / Calder, Jeff / Miller, Kevin / Bertozzi, Andrea L.

    2023  

    Abstract: Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have ... ...

    Abstract Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data arXiv:2204.00005. In each iteration, sequential active learning selects a query set of size one while batch active learning selects a query set of multiple datapoints. While batch active learning methods exhibit greater efficiency, the challenge lies in maintaining model accuracy relative to sequential active learning methods. We developed a novel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set (DAC) for core-set generation and LocalMax for batch sampling. The batch active learning process that combines DAC and LocalMax achieves nearly identical accuracy as sequential active learning but is more efficient, proportional to the batch size. As an application, a pipeline is built based on transfer learning feature embedding, graph learning, DAC, and LocalMax to classify the FUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms the state-of-the-art CNN-based methods.

    Comment: 16 pages, 7 figures, Preprint
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Signal Processing ; I.2.6 ; I.2.10 ; I.4.0 ; I.4.9
    Subject code 004 ; 006
    Publishing date 2023-07-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  8. Article ; Online: Unusual suicide with an electric Jigsaw: A case report and literature review.

    Cioffi, Andrea / Cecannecchia, Camilla / Bertozzi, Giuseppe / Cipolloni, Luigi / Baldari, Benedetta

    Journal of forensic and legal medicine

    2022  Volume 89, Page(s) 102372

    Abstract: Fatal injuries caused by power saws are rare. In most cases, they are accidental and non-voluntary. Even rarer are suicides carried out using power saws, with no previously reported cases of suicide by electric jigsaw. We report a case of suicide by ... ...

    Abstract Fatal injuries caused by power saws are rare. In most cases, they are accidental and non-voluntary. Even rarer are suicides carried out using power saws, with no previously reported cases of suicide by electric jigsaw. We report a case of suicide by electric jigsaw of a young obese woman suffering from major depression with psychotic features. The peculiarity of our case - compared to those known in the scientific literature - concerns not only the means used for suicide, but also the results of psychological autopsy and the characteristics of the fatal injury. In fact, the autopsy showed a large wound on the anterior and lateral region of the neck with preservation of the integrity of the large vessels of the neck. The cause of death was attributed to haemorrhagic shock due to slow bleeding of small and medium calibre neck vessels, with blood aspiration.
    MeSH term(s) Autopsy ; Female ; Forensic Pathology/methods ; Humans ; Neck Injuries/etiology ; Shock, Hemorrhagic/etiology ; Suicide
    Language English
    Publishing date 2022-05-21
    Publishing country England
    Document type Case Reports ; Journal Article ; Review
    ZDB-ID 2268721-X
    ISSN 1878-7487 ; 1752-928X
    ISSN (online) 1878-7487
    ISSN 1752-928X
    DOI 10.1016/j.jflm.2022.102372
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4217: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization.

    Dröge, Hannah / Yuan, Baichuan / Llerena, Rafael / Yen, Jesse T / Moeller, Michael / Bertozzi, Andrea L

    Journal of imaging

    2021  Volume 7, Issue 10

    Abstract: Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image ... ...

    Abstract Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image quality make the automatic tracking and segmentation of the mitral valve in two-dimensional echocardiographic videos challenging. In this paper, we present a fully automatic and unsupervised method for segmentation of the mitral valve in two-dimensional echocardiographic videos, independently of the echocardiographic view. We propose a bias-free variant of the robust non-negative matrix factorization (RNMF) along with a window-based localization approach, that is able to identify the mitral valve in several challenging situations. We improve the average f1-score on our dataset of 10 echocardiographic videos by 0.18 to a f1-score of 0.56.
    Language English
    Publishing date 2021-10-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2824270-1
    ISSN 2313-433X ; 2313-433X
    ISSN (online) 2313-433X
    ISSN 2313-433X
    DOI 10.3390/jimaging7100213
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

    Ji, Hangjie / Lafata, Kyle / Mowery, Yvonne / Brizel, David / Bertozzi, Andrea L / Yin, Fang-Fang / Wang, Chunhao

    Frontiers in oncology

    2022  Volume 12, Page(s) 895544

    Abstract: Purpose: To develop a method of biologically guided deep learning for post-radiation : Methods: Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial ... ...

    Abstract Purpose: To develop a method of biologically guided deep learning for post-radiation
    Methods: Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation
    Results: The proposed method successfully generated post-20-Gy
    Conclusion: The developed biologically guided deep learning method achieved post-20-Gy
    Language English
    Publishing date 2022-05-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2022.895544
    Database MEDical Literature Analysis and Retrieval System OnLINE

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