Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT

Generative adversarial networks (GANs) are able to model accurately the distribution of complex, high-dimensional datasets, for example images. This characteristic makes high-quality GANs useful for unsupervised anomaly detection in medical imaging. However, differences in training datasets such as output image dimensionality and appearance of semantically meaningful features mean that GAN models from the natural image processing domain may not work 'out-of-the-box' for medical imaging applications, necessitating re-implementation and re-evaluation. In this work we adapt and evaluate three GAN models to the application of modelling 3D healthy image patches for pulmonary CT. To the best of our knowledge, this is the first time that such a detailed evaluation has been performed. The deep convolutional GAN (DCGAN), styleGAN and the bigGAN architectures were selected for investigation due to their ubiquity and high performance in natural image processing. We train different variants of these methods and assess their performance using the widely used Frechet Inception Distance (FID). In addition, the quality of the generated images was evaluated by a human observer study, the ability of the networks to model 3D domain-specific features was investigated, and the structure of the GAN latent spaces was analysed. Results show that the 3D styleGAN approaches produce realistic-looking images with meaningful 3D structure, but suffer from mode collapse which must be explicitly addressed during training to obtain diversity in the samples. Conversely, the 3D DCGAN models show a greater capacity for image variability, but at the cost of poor-quality images. The 3D bigGAN models provide an intermediate level of image quality, but most accurately model the distribution of selected semantically meaningful features. The results suggest that future development is required to realise a 3D GAN with sufficient representational capacity for patch-based lung CT anomaly detection and we offer recommendations for future areas of research, such as experimenting with other architectures and incorporation of position-encoding.
The effect of a flight stream on subsonic turbulent jets

This study concerns a turbulent jet at Mach number $M_j=0.9$, subject to a uniform external flow stream at $M_f=0.15$. We assess the mechanisms that underpin the reduction in fluctuation energy that is known to occur when a jet is surrounded by a flight stream. The analysis combines experimental and numerical databases, spectral proper orthogonal decomposition (SPOD) and linear modelling. The experiments involve Time-Resolved, Stereo PIV measurements at different cross-sections of the jet. A companion large-eddy simulation was performed with the same operating conditions using the"CharLES"solver by Cascade Technologies in order to obtain a complete and highly resolved 3D database. We show that the energy reduction is spread over a broad region of the frequency-wavenumber space and involves, apart from the known stabilization of the modal Kelvin-Helmholtz (KH) instability, the attenuation of flow structures associated with the non-modal Orr and lift-up mechanisms. Streaky structures, associated with helical azimuthal wavenumbers and very slow time scales, are the most strongly affected by the flight stream, in terms of energy attenuation and spatial distortion. The energy reductions are accompanied by a weakening of the low-rank behaviour of the jet dynamics revealed by previous studies. These trends are found to be consistent with results of a local linear model based on the modified mean flow in the flight stream case.
Deep Learning Enabled Time-Lapse 3D Cell Analysis

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on Github and the method is available as a service through the BisQue portal.
On the Role of Negative Precedent in Legal Outcome Prediction

Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to negative outcomes here, and introduce a new task of negative outcome prediction. We discover an asymmetry in existing models’ ability to predict positive and negative outcomes. Where the state-of-the-art outcome prediction model we used predicts positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1, worse than a random baseline. To address this performance gap, we develop two new models inspired by the dynamics of a court process. Our first model significantly improves positive outcome prediction score to 77.15 F1 and our second model more than doubles the negative outcome prediction performance to 24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals that there is still much room for improvement for outcome prediction models. https://github.com/valvoda/Negative-Precedent-in-Legal-Outcome-Prediction
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models

Automatically summarizing patients’ main problems from daily progress notes using natural language processing methods helps to battle against information and cognitive overload in hospital settings and potentially assists providers with computerized diagnostic decision support. Problem list summarization requires a model to understand, abstract, and generate clinical documentation. In this work, we propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization. We investigate the performance of T5 and BART, two state-of-the-art seq2seq transformer architectures, in solving this problem. We provide a corpus built on top of progress notes from publicly available electronic health record progress notes in the Medical Information Mart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain text, and we experiment with a data augmentation method and a domain adaptation pre-training method to increase exposure to medical vocabulary and knowledge. Evaluation methods include ROUGE, BERTScore, cosine similarity on sentence embedding, and F-score on medical concepts. Results show that T5 with domain adaptive pre-training achieves significant performance gains compared to a rule-based system and general domain pre-trained language models, indicating a promising direction for tackling the problem summarization task.
FedPerm: Private and Robust Federated Learning by Parameter Permutation

Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is paramount in FL. At the same time, the model must be protected from poisoning attacks from adversarial clients. Existing solutions address these two problems in isolation. We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates. The combination of these techniques further helps the federation server constrain parameter updates from clients so as to curtail effects of model poisoning attacks by adversarial clients. We further present FedPerm's unique hyperparameters that can be used effectively to trade off computation overheads with model utility. Our empirical evaluation on the MNIST dataset demonstrates FedPerm's effectiveness over existing Differential Privacy (DP) enforcement solutions in FL.
Open Long-Tailed Recognition in a Dynamic World

Real world data often exhibits a long-tailed and open-ended (i.e., with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are: 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes. Additionally, we propose an active learning scheme based on visual memory, which learns to recognize open classes in a data-efficient manner for future expansions. On three large-scale open long-tailed datasets we curated from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric) data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and iNaturalist-18), our approach, as a unified framework, consistently demonstrates competitive performance. Notably, our approach also shows strong potential for the active exploration of open classes and the fairness analysis of minority groups.
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various tasks, enabling the model to learn the meta-initialization of its own parameters to quickly adapt to new tasks using a small amount of labeled training data. A key challenge to few-shot learning is task uncertainty. Although a strong prior can be obtained from meta-learning with a large number of tasks, a precision model of the new task cannot be guaranteed because the vol-ume of the training dataset is normally too small. In this study, ﬁrst, in the process of choosing initialization parameters, the new method is proposed for task-speciﬁc learner adaptively learn to select initialization parameters that minimize the loss of new tasks. Then, we propose two improved methods for the meta-loss part: Method 1 generates weights by comparing meta-loss diﬀerences to improve the accuracy when there are few classes, and Method 2 introduces the homoscedastic uncertainty of each task to weigh multiple losses based on the original gradient descent, as a way to enhance the generalization ability to novel classes while en-suring accuracy improvement. Compared with previous gradient-based meta-learning methods, our model achieves better performance in regression tasks and few-shot classiﬁcation and improves the robustness of the model to the learning rate and query sets in the meta-test set. Meta-loss.
Conserved asymptotic charges for any massless particle

We compute the conserved charges associated with the asymptotic symmetries of massless particles by examining their free theory in Minkowski spacetime. We give a procedure to systematically deduce the fall off of the massless fields at spatial infinity and show that it has a universal behaviour when expressed in tangent space. We do this for generic massless particles. We do not impose gauge fixing conditions which allows us to uncover new nonzero charges for the graviton beyond the well-known supertranslation charges. We also compute conserved charges in the dual formulations of certain low spin particles and argue that this leads to an infinite number of new conserved charges.
Insights into the nature of X(3872) through B meson decays

We study the decays in the perturbative QCD (PQCD) approach, involving the puzzling resonance , where P represents a light pseudoscalar meson (K or π). Assuming to be a charmonium state, we obtain the following results. (a) The branching ratios of the and decays are consistent with the results predicted by the covariant light-front approach within errors; however, they are larger than those given by the generalized factorization approach. (b) The branching ratio of the decay is predicted as , which is smaller than the previous PQCD calculation result but still slightly larger than the upper limits set by Belle and BaBar. Hence, we suggest that the decays should be precisely measured by the LHCb and Belle II experiments to help probe the inner structure of . (c) Compared with the decays, the decays have significantly smaller branching ratios, which drop to values as low as . (d) The direct CP violations of these considered decays are small ( ) because the penguin contributions are loop suppressed compared to the tree contributions. The mixing-induced CP violation of the decay is highly consistent with the current world average value %. Experimentally testing the results for the branching ratios and CP violations, including the implicit and isospin symmetries of these decays, helps probe the nature of .
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons. In this paper we aim at creating a flexible, learned IK solver applicable to a wide variety of human morphologies. We extend a state-of-the-art machine learning IK solver to operate on the well known Skinned Multi-Person Linear model (SMPL). We call our model SMPL-IK, and show that when integrated into real-time 3D software, this extended system opens up opportunities for defining novel AI-assisted animation workflows. For example, when chained with existing pose estimation algorithms, SMPL-IK accelerates posing by allowing users to bootstrap 3D scenes from 2D images while allowing for further editing. Additionally, we propose a novel SMPL Shape Inversion mechanism (SMPL-SI) to map arbitrary humanoid characters to the SMPL space, allowing artists to leverage SMPL-IK on custom characters. In addition to qualitative demos showing proposed tools, we present quantitative SMPL-IK baselines on the H36M and AMASS datasets. Our code is publicly available https://github.com/boreshkinai/smpl-ik.
Design of a High-Power Resonant Converter for DC Wind Turbines

This paper presents a design procedure and loss estimation for a high-power, medium-voltage series resonant converter (entitled SRC#), intended for application in megawatt medium-voltage dc wind turbines. The converter is operated with a novel method of operation, entitled pulse removal technique, characterized by variable frequency and phase-shift modulation, below the resonant point of the LC tank. A step-by-step design procedure is proposed and circuit component ratings and efficiency are compared for two variants. The new method of operation reduces transformer size to below 50%, whereas losses are kept below 1.5% from zero to nominal power, due to soft-switching characteristics. An experimental setup, rated for 10 kW and 5 kV output was assembled to extract losses and validate the semiconductor loss model.
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making

Human-AI collaboration for decision-making strives to achieve team performance that exceeds the performance of humans or AI alone. However, many factors can impact success of Human-AI teams, including a user’s domain expertise, mental models of an AI system, trust in recommendations, and more. This article reports on a study that examines users’ interactions with three simulated algorithmic models, all with equivalent accuracy rates but each tuned differently in terms of true positive and true negative rates. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Users completed 140 trials across multiple stages, first without an AI and then with recommendations from an AI-Assistant. Although all users had prior experience with the task, their levels of proficiency varied widely. Our results demonstrated that while recommendations from an AI-Assistant can aid in users’ decision making, several underlying factors, including user base expertise and complementary human-AI tuning, significantly impact the overall team performance. First, users’ base performance matters, particularly in comparison to the performance level of the AI. Novice users improved, but not to the accuracy level of the AI. Highly proficient users were generally able to discern when they should follow the AI recommendation and typically maintained or improved their performance. Mid-performers, who had a similar level of accuracy to the AI, were most variable in terms of whether the AI recommendations helped or hurt their performance. Second, tuning an AI algorithm to complement users’ strengths and weaknesses also significantly impacted users’ performance. For example, users in our study were better at detecting flowing blood vessels, so when the AI was tuned to reduce false negatives (at the expense of increasing false positives), users were able to reject those recommendations more easily and improve in accuracy. Finally, users’ perception of the AI’s performance relative to their own performance had an impact on whether users’ accuracy improved when given recommendations from the AI. Overall, this work reveals important insights on the complex interplay of factors influencing Human-AI collaboration and provides recommendations on how to design and tune AI algorithms to complement users in decision-making tasks.
Adversarial Image Color Transformations in Explicit Color Filter Space

Deep Neural Networks have been shown to be vulnerable to adversarial images. Conventional attacks strive for indistinguishable adversarial images with strictly restricted perturbations. Recently, researchers have moved to explore distinguishable yet non-suspicious adversarial images and demonstrated that color transformation attacks are effective. In this work, we propose Adversarial Color Filter (AdvCF), a novel color transformation attack that is optimized with gradient information in the parameter space of a simple color filter. In particular, our color filter space is explicitly specified so that we are able to provide a systematic analysis of model robustness against adversarial color transformations, from both the attack and defense perspectives. In contrast, existing color transformation attacks do not offer the opportunity for systematic analysis due to the lack of such an explicit space. We further demonstrate the effectiveness of our AdvCF in fooling image classifiers and also compare it with other color transformation attacks regarding their robustness to defenses and image acceptability through an extensive user study. We also highlight the human-interpretability of AdvCF and show its superiority over the state-of-the-art human-interpretable color transformation attack on both image acceptability and efficiency. Additional results provide interesting new insights into model robustness against AdvCF in another three visual tasks.
Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to nonrandom missingness

Characterizing the cumulative burden of COVID-19 by race/ethnicity is of the utmost importance for public health researchers and policy makers in order to design effective mitigation measures. This analysis is hampered, however, by surveillance case data with substantial missingness in race and ethnicity covariates. Worse yet, this missingness likely depends on the values of these missing covariates, i.e. they are not missing at random (NMAR). We propose a Bayesian parametric model that leverages joint information on spatial variation in the disease and covariate missingness processes and can accommodate both MAR and NMAR missingness. We show that the model is locally identifiable when the spatial distribution of the population covariates is known and observed cases can be associated with a spatial unit of observation. We also use a simulation study to investigate the model's finite-sample performance. We compare our model's performance on NMAR data against complete-case analysis and multiple imputation (MI), both of which are commonly used by public health researchers when confronted with missing categorical covariates. Finally, we model spatial variation in cumulative COVID-19 incidence in Wayne County, Michigan using data from the Michigan Department and Health and Human Services. The analysis suggests that population relative risk estimates by race during the early part of the COVID-19 pandemic in Michigan were understated for non-white residents compared to white residents when cases missing race were dropped or had these values imputed using MI.
Humanized mice for investigating SARS‐CoV‐2 lung infection and associated human immune responses

There is an urgent need for animal models of coronavirus disease 2019 to study immunopathogenesis and test therapeutic intervenes. In this study, we showed that NOD/SCID IL2rg–/– (NSG) mice engrafted with human lung (HL) tissue (NSG‐L mice) could be infected efficiently by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), and that live virus capable of infecting Vero cells was found in the HL grafts and multiple organs from infected NSG‐L mice. RNA‐Sequencing identified a series of differentially expressed genes, which are enriched in viral defense responses, chemotaxis, IFN stimulation and pulmonary fibrosis, between HL grafts from infected and control NSG‐L mice. Furthermore, when infected with SARS‐CoV‐2, humanized mice with both human immune system (HIS) and autologous HL grafts (HISL mice) had bodyweight loss and hemorrhage and immune cell infiltration in HL grafts, which were not observed in immunodeficient NSG‐L mice, indicating the development of anti‐viral immune responses in these mice. In support of this possibility, the infected HISL mice showed bodyweight recovery and lack of detectable live virus at the later time. These results demonstrate that NSG‐L and HISL mice are susceptible to SARS‐CoV‐2 infection, offering a useful in vivo model for studying SARS‐CoV‐2 infection and the associated immune response and immunopathology, and testing anti‐SARS‐CoV‐2 therapies.