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  1. AU="Xu, Winnie"
  2. AU="Seah, Annabel"
  3. AU="Qv, Zong-Yang"
  4. AU="Lederer, Franziska L"
  5. AU="Funder, John W"
  6. AU="Boyang Zhou"
  7. AU="Karsten Niehaus"
  8. AU=Sakurada Tsutomu
  9. AU="Ravinovich"
  10. AU="Le Corroller, Thomas"
  11. AU=Wang Lihua
  12. AU="Balducci, Ivan"
  13. AU="Kamble, Nitish" AU="Kamble, Nitish"
  14. AU="Violetta Dziedziejko"
  15. AU="Pablo Cañón"
  16. AU="Boone, Darren"
  17. AU="Nadeem S. Sheikh"
  18. AU="Means, Gary"
  19. AU="Tania Kew"
  20. AU="Williams, Scott A"
  21. AU="Dvir, May"

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  1. Artikel ; Online: Chemosensory loss in COVID-19.

    Xu, Winnie / Sunavala-Dossabhoy, Gulshan / Spielman, Andrew I

    Oral diseases

    2022  Band 28 Suppl 2, Seite(n) 2337–2346

    Abstract: The COVID-19 pandemic caused by SARS-CoV-2 virus quickly spread globally, infecting over half a billion individuals, and killing over 6 million*. One of the more unusual symptoms was patients' complaints of sudden loss of smell and/or taste, a symptom ... ...

    Abstract The COVID-19 pandemic caused by SARS-CoV-2 virus quickly spread globally, infecting over half a billion individuals, and killing over 6 million*. One of the more unusual symptoms was patients' complaints of sudden loss of smell and/or taste, a symptom that has become more apparent as the virus mutated into different variants. Anosmia and ageusia, the loss of smell and taste, respectively, seem to be transient for some individuals, but for others persists even after recovery from the infection. Causes for COVID-19-associated chemosensory loss have undergone several hypotheses. These include non-functional or destroyed olfactory neurons and gustatory receptors or of their supporting cells, disruption of the signaling protein Neuropilin-1, and disruption in the interaction with semaphorins, key molecules in the gustatory and olfactory axon guidance. The current paper will review these hypotheses and chart out potential therapeutic avenues.
    Mesh-Begriff(e) Humans ; COVID-19/complications ; Pandemics ; SARS-CoV-2 ; Taste Disorders/etiology ; Olfaction Disorders/etiology ; Anosmia/etiology
    Sprache Englisch
    Erscheinungsdatum 2022-07-14
    Erscheinungsland Denmark
    Dokumenttyp Journal Article ; Review
    ZDB-ID 1290529-x
    ISSN 1601-0825 ; 1354-523X
    ISSN (online) 1601-0825
    ISSN 1354-523X
    DOI 10.1111/odi.14300
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  2. Buch ; Online: Deep Latent State Space Models for Time-Series Generation

    Zhou, Linqi / Poli, Michael / Xu, Winnie / Massaroli, Stefano / Ermon, Stefano

    2022  

    Abstract: Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in ... ...

    Abstract Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.
    Schlagwörter Statistics - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-12-24
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  3. Artikel ; Online: Debridement, Antibiotics, and Implant Retention in Unicompartmental Knee Arthroplasty Infection.

    McCormick, Kyle L / Xu, Winnie / Cozzarelli, Nicholas F / Crawford, David / Wilson, Eric J / Berend, Keith R / Fricka, Kevin B / Lonner, Jess H / Geller, Jeffrey A

    The Journal of arthroplasty

    2024  

    Abstract: Background: This multicenter study sought to further investigate the method and outcome of debridement, antibiotics, and implant retention (DAIR) for the management of unicompartmental knee periprosthetic joint infection (PJI).: Methods: This ... ...

    Abstract Background: This multicenter study sought to further investigate the method and outcome of debridement, antibiotics, and implant retention (DAIR) for the management of unicompartmental knee periprosthetic joint infection (PJI).
    Methods: This retrospective study was performed on 52 patients who underwent DAIR for PJI of a unicompartmental knee arthroplasty (UKA) across 4 academic medical centers, all performed by fellowship-trained arthroplasty surgeons. Patient demographics, American Society of Anesthesiologists score, infecting organism, operative data, antibiotic data, and success in infection control at 1 year were collected.
    Results: The average time from index surgery to diagnosis of PJI was 11.1 weeks (range, 1.4 to 48). There was no correlation between time of diagnosis and success at 1 year (R = 0.09, P = .46). There was an association between surgical synovectomy and the eradication of infection (R = 0.28, P = .04). Overall, there was an 80.8% (42 of 52) infection-controlled success rate at 1 year from the DAIR procedure. All DAIR failures went on to require another procedure, either 1-stage (2 of 10) or 2-stage (8 of 10) revision to total knee arthroplasty (TKA). Of the DAIR successes, 6 (14.3%) went on to require conversion to TKA for progression of arthritis within 5 years.
    Conclusions: This study demonstrates that DAIR is a safe and moderately effective procedure in the setting of acute PJI of UKA across institutions, with a success rate consistent with DAIR for TKA. The data suggest that a wide exposure and thorough synovectomy be incorporated during the DAIR UKA to improve the likelihood of successful eradication of PJI at the 1-year mark.
    Level of evidence: Level III.
    Sprache Englisch
    Erscheinungsdatum 2024-03-26
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 632770-9
    ISSN 1532-8406 ; 0883-5403
    ISSN (online) 1532-8406
    ISSN 0883-5403
    DOI 10.1016/j.arth.2024.03.057
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  4. Artikel: Changes in bone microarchitecture following parathyroidectomy in patients with secondary hyperparathyroidism.

    Ruderman, Irene / Rajapakse, Chamith S / Xu, Winnie / Tang, Sisi / Robertson, Patricia L / Toussaint, Nigel D

    Bone reports

    2021  Band 15, Seite(n) 101120

    Abstract: Background: Secondary hyperparathyroidism (SHPT) in patients with chronic kidney disease (CKD) has a significant effect on bone, affecting both trabecular and cortical compartments. Although parathyroidectomy results in biochemical improvement in ... ...

    Abstract Background: Secondary hyperparathyroidism (SHPT) in patients with chronic kidney disease (CKD) has a significant effect on bone, affecting both trabecular and cortical compartments. Although parathyroidectomy results in biochemical improvement in mineral metabolism, changes in bone microarchitecture as evaluated by high-resolution imaging modalities are not known. Magnetic resonance imaging (MRI) provides in-depth three-dimensional assessment of bone microarchitecture, as well as determination of mechanical bone strength determined by finite element analysis (FEA).
    Methods: We conducted a single-centre longitudinal study to evaluate changes in bone microarchitecture with MRI in patients with SHPT undergoing parathyroidectomy. MRI was performed at the distal tibia at baseline (time of parathyroidectomy) and at least 12 months following surgery. Trabecular and cortical topological parameters as well as bone mechanical competence using FEA were assessed.
    Results: Fifteen patients with CKD (12 male, 3 female) underwent both MRI scans at the time of surgery and at least 12 months post-surgery. At baseline, 13 patients were on dialysis, one had a functioning kidney transplant, and one was pre-dialysis with stage 5 CKD. Seven patients received a kidney transplant following parathyroidectomy prior to follow-up MRI. MRI parameters in patients at follow up were consistent with loss in trabecular and cortical bone thickness (p = 0.006 and 0.03 respectively). Patients who underwent a kidney transplant in the follow-up period had reduction in trabecular thickness (p = 0.05), whereas those who continued on dialysis had reduction in cortical thickness (p = 0.04) and mechanical bone strength on FEA (p = 0.03).
    Conclusion: Patients with severe SHPT requiring parathyroidectomy have persistent changes in bone microarchitecture at least 12 months following surgery with evidence of ongoing decline in trabecular and cortical thickness.
    Sprache Englisch
    Erscheinungsdatum 2021-08-24
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2821774-3
    ISSN 2352-1872
    ISSN 2352-1872
    DOI 10.1016/j.bonr.2021.101120
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  5. Buch ; Online: Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations

    Xu, Winnie / Chen, Ricky T. Q. / Li, Xuechen / Duvenaud, David

    2021  

    Abstract: We perform scalable approximate inference in a continuous-depth Bayesian neural network family. In this model class, uncertainty about separate weights in each layer gives hidden units that follow a stochastic differential equation. We demonstrate ... ...

    Abstract We perform scalable approximate inference in a continuous-depth Bayesian neural network family. In this model class, uncertainty about separate weights in each layer gives hidden units that follow a stochastic differential equation. We demonstrate gradient-based stochastic variational inference in this infinite-parameter setting, producing arbitrarily-flexible approximate posteriors. We also derive a novel gradient estimator that approaches zero variance as the approximate posterior over weights approaches the true posterior. This approach brings continuous-depth Bayesian neural nets to a competitive comparison against discrete-depth alternatives, while inheriting the memory-efficient training and tunable precision of Neural ODEs.
    Schlagwörter Statistics - Machine Learning ; Computer Science - Machine Learning
    Erscheinungsdatum 2021-02-12
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    Dokumenttyp Buch ; Online
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  6. Artikel ; Online: Atherogenic Indices as a Predictor of Aortic Calcification in Prostate Cancer Patients Assessed Using

    Dai, Michelle / Xu, Winnie / Chesnais, Helene / Anabaraonye, Nancy / Parente, James / Chatterjee, Shampa / Rajapakse, Chamith S

    International journal of molecular sciences

    2022  Band 23, Heft 21

    Abstract: A major pathophysiological cause of cardiovascular disease is vascular plaque calcification. Fluorine 18−Sodium Fluoride (18F-NaF) PET/CT can be used as a sensitive imaging modality for detection of vascular calcification. The aim of this study was to ... ...

    Abstract A major pathophysiological cause of cardiovascular disease is vascular plaque calcification. Fluorine 18−Sodium Fluoride (18F-NaF) PET/CT can be used as a sensitive imaging modality for detection of vascular calcification. The aim of this study was to find a non-invasive, cost-efficient, and readily available metric for predicting vascular calcification severity. This retrospective study was performed on 36 participants who underwent 18F-NaF fused PET/CT scans. The mean standard uptake values (SUVs) were calculated from manually sectioned axial sections over the aortic arch and thoracic aorta. Correlation analyses were performed between SUVs and calculated atherogenic indices (AIs). Castelli’s Risk Index I (r = 0.63, p < 0.0001), Castelli’s Risk Index II (r = 0.64, p < 0.0001), Atherogenic Coefficient (r = 0.63, p < 0.0001), Atherogenic Index of Plasma (r = 0.51, p = 0.00152), and standalone high-density lipoprotein (HDL) cholesterol (r = −0.53, p = 0.000786) were associated with aortic calcification. AIs show strong association with aortic arch and thoracic aorta calcifications. AIs are better predictors of vascular calcification compared to standalone lipid metrics, with the exception of HDL cholesterol. Clinical application of AIs provides a holistic metric beneficial for enhancing screening and treatment protocols.
    Mesh-Begriff(e) Male ; Humans ; Sodium Fluoride ; Positron Emission Tomography Computed Tomography ; Retrospective Studies ; Prostatic Neoplasms ; Vascular Calcification/diagnostic imaging ; Vascular Calcification/etiology ; Radiopharmaceuticals
    Chemische Substanzen Sodium Fluoride (8ZYQ1474W7) ; Radiopharmaceuticals
    Sprache Englisch
    Erscheinungsdatum 2022-10-27
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms232113056
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  7. Buch ; Online: Self-Similarity Priors

    Poli, Michael / Xu, Winnie / Massaroli, Stefano / Meng, Chenlin / Kim, Kuno / Ermon, Stefano

    Neural Collages as Differentiable Fractal Representations

    2022  

    Abstract: Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxies and even images. In this work, ...

    Abstract Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxies and even images. In this work, we investigate the role of learning in the automated discovery of self-similarity and in its utilization for downstream tasks. To this end, we design a novel class of implicit operators, Neural Collages, which (1) represent data as the parameters of a self-referential, structured transformation, and (2) employ hypernetworks to amortize the cost of finding these parameters to a single forward pass. We investigate how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation. Neural Collages image compressors are orders of magnitude faster than other self-similarity-based algorithms during encoding and offer compression rates competitive with implicit methods. Finally, we showcase applications of Neural Collages for fractal art and as deep generative models.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-04-15
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  8. Buch ; Online: NoisyMix

    Erichson, N. Benjamin / Lim, Soon Hoe / Xu, Winnie / Utrera, Francisco / Cao, Ziang / Mahoney, Michael W.

    Boosting Model Robustness to Common Corruptions

    2022  

    Abstract: For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus robustness of neural networks is an increasingly important topic. Relatedly, ...

    Abstract For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus robustness of neural networks is an increasingly important topic. Relatedly, data augmentation schemes have been shown to improve robustness with respect to input perturbations and domain shifts. Motivated by this, we introduce NoisyMix, a novel training scheme that promotes stability as well as leverages noisy augmentations in input and feature space to improve both model robustness and in-domain accuracy. NoisyMix produces models that are consistently more robust and that provide well-calibrated estimates of class membership probabilities. We demonstrate the benefits of NoisyMix on a range of benchmark datasets, including ImageNet-C, ImageNet-R, and ImageNet-P. Moreover, we provide theory to understand implicit regularization and robustness of NoisyMix.
    Schlagwörter Computer Science - Machine Learning ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-02-02
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  9. Buch ; Online: Neural Functional Transformers

    Zhou, Allan / Yang, Kaien / Jiang, Yiding / Burns, Kaylee / Xu, Winnie / Sokota, Samuel / Kolter, J. Zico / Finn, Chelsea

    2023  

    Abstract: The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input by operating directly over their weight spaces. Nevertheless, constructing ... ...

    Abstract The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input by operating directly over their weight spaces. Nevertheless, constructing expressive and efficient neural functional architectures that can handle high-dimensional weight-space objects remains challenging. This paper uses the attention mechanism to define a novel set of permutation equivariant weight-space layers and composes them into deep equivariant models called neural functional Transformers (NFTs). NFTs respect weight-space permutation symmetries while incorporating the advantages of attention, which have exhibited remarkable success across multiple domains. In experiments processing the weights of feedforward MLPs and CNNs, we find that NFTs match or exceed the performance of prior weight-space methods. We also leverage NFTs to develop Inr2Array, a novel method for computing permutation invariant latent representations from the weights of implicit neural representations (INRs). Our proposed method improves INR classification accuracy by up to $+17\%$ over existing methods. We provide an implementation of our layers at https://github.com/AllanYangZhou/nfn.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-05-22
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  10. Artikel ; Online: Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis.

    Suri, Abhinav / Tang, Sisi / Kargilis, Daniel / Taratuta, Elena / Kneeland, Bruce J / Choi, Grace / Agarwal, Alisha / Anabaraonye, Nancy / Xu, Winnie / Parente, James B / Terry, Ashley / Kalluri, Anita / Song, Katie / Rajapakse, Chamith S

    Radiology. Artificial intelligence

    2023  Band 5, Heft 4, Seite(n) e220158

    Abstract: Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, ... ...

    Abstract Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (
    Sprache Englisch
    Erscheinungsdatum 2023-06-21
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.220158
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