Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics for Machine Learning Detection of Large Vessel Occlusion on Computed Tomography Angiography

Detection of large vessel occlusion (LVO) using machine learning on computed tomography angiography (CTA) may help stroke triage, yet applicability across varied patient and image characteristics has not been examined. The study will examine which characteristics are important when using a convolutional neural network to identify LVO on CTA. A retrospective cohort study (November 2017 through May 2019) at a comprehensive stroke center evaluated 677 stroke-alerted patients with an LVO of the internal carotid artery, M1, or M2 (n=150), and a matching number without LVO was included. An Inception module-based network was trained for binary classification of LVO presence. Results were examined by LVO location, window settings, non-LVO findings, demographics, risk factors, presentation status, and times, interventions, and outcomes. Three hundred patients were included (48% women; median age 65). Mean+/-95% CI for cross-validation test and external validation, respectively, are area under precision-recall curve 0.871+/-0.094 and 0.742+/-0.018 and area under receiver operating characteristic curve 0.920+/-0.051 and 0.852+/-0.004. 145 true positive (TP), 5 false negative (FN), 39 false positive (FP), and 111 true negative (TN) patients were identified. Significant comparisons (P<0.05) were identified: lower window settings for misclassifications, smoking history for all FN versus 33% TP (P=0.005), and tissue plasminogen activator treatment for 41% FP versus 20% TN (P=0.017). Our LVO detection tool had high performance across patient characteristics with few exceptions. FP had pathology warranting detection, including distal occlusions. Lower window settings among misclassifications highlight the need for image quality when using machine learning for decision support.
Longitudinal dynamics of Streptococcus pneumoniae carriage and SARS-CoV-2 infection in households with children.

Background: To characterize interferences between Streptococcus pneumoniae and SARS-CoV-2 we investigated the longitudinal patterns of viral infection and pneumococcal carriage in households infected with SARS-CoV-2. Methods: SARS-CoV-2 and pneumococcus were detected with quantitative molecular methods in saliva from members of eighty participating households. Samples were collected between October 2020 and January 2021 from n=197 adults and n=118 children of which n=176 adults and n=98 children had a complete set of ten samples collected within 42 days since enrolment. Time-dependent Cox models were used to evaluate the associations between SARS-CoV-2 and pneumococcal carriage. Results: In the entire cohort, cumulative pneumococcal carriage and SARS-CoV-2 infection rates were 58% and 65%, respectively. Pneumococcal abundances were associated with an increased risk of SARS-CoV-2 infection (HR 1.14, 95% CI, 1.01-1.29, P=0.04) and delayed clearance of SARS-CoV-2 infection (HR 0.90, 95% CI, 0.82-0.99, P=0.03). Elevated viral loads were observed among pneumococcal carriers and individuals with high overall bacterial 16S abundances, however, there were no longitudinal differences in viral loads in linear mixed-effects models. Individuals with high 16S abundances displayed delayed viral clearance (HR 0.65, 95% CI 0.55-0.78, P<0.0001). Conclusions: Although we found insufficient evidence for a strong impact of SARS-CoV-2 infection on pneumococcal carriage. Results from the current study suggest that pneumococcal carriers may have an increased risk of SARS-CoV-2 infection and high pneumococcal abundances and 16S abundances may be associated with elevated viral loads and delayed clearance of SARS-CoV-2 infection.
Democratizing Making: Scaffolding Participation Using e-Waste to Engage Under-resourced Communities in Technology Design

Maker culture and DIY practices are central to democratizing the design of technology; enabling non-designers (future end-users) to actively participate in the design process. However, little is known about how individuals from under-resourced communities and low socioeconomic status (SES) backgrounds, can practically leverage maker practices to design technology, creating value for themselves or their communities. To investigate this, we collaborated with an e-waste recycling centre, involving 24 participants (staff and low-SES volunteers) in two participatory maker workshop activities. Participants were provided with a generative e-waste toolkit, through which they repurposed e-waste materials and developed novel technology prototypes that created value from their perspectives and agendas. Our findings unpack three factors that influenced their making: balancing personal and community needs; incorporating convenience and productivity; and re-thinking sustainability and connection; and discuss strategies for scaffolding participation and engagement of under-resourced communities in making using an e-waste generative toolkit to democratize technology design.
Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers

Estimating fluid dynamics is classically done through the simulation and integration of numerical models solving the Navier-Stokes equations, which is computationally complex and time-consuming even on high-end hardware. This is a notoriously hard problem to solve, which has recently been addressed with machine learning, in particular graph neural networks (GNN) and variants trained and evaluated on datasets of static objects in static scenes with fixed geometry. We attempt to go beyond existing work in complexity and introduce a new model, method and benchmark. We propose EAGLE, a large-scale dataset of 1.1 million 2D meshes resulting from simulations of unsteady fluid dynamics caused by a moving flow source interacting with nonlinear scene structure, comprised of 600 different scenes of three different types. To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer. It leverages node clustering, graph pooling and global attention to learn long-range dependencies between spatially distant data points without needing a large number of iterations, as existing GNN methods do. We show that our transformer outperforms state-of-the-art performance on, both, existing synthetic and real datasets and on EAGLE. Finally, we highlight that our approach learns to attend to airflow, integrating complex information in a single iteration.
Efficient phase-space generation for hadron collider event simulation

We present a simple yet efficient algorithm for phase-space integration at hadron colliders. Individual mappings consist of a single t-channel combined with any number of s-channel decays, and are constructed using diagrammatic information. The factorial growth in the number of channels is tamed by providing an option to limit the number of s-channel topologies. We provide a publicly available, parallelized code in C++ and test its performance in typical LHC scenarios.
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy

Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections. Its performance suffers from the non-vanishing biases introduced by the local inconsistent optimal and the rugged client-drifts by the local over-fitting. In this paper, we propose a novel and practical method, FedSpeed, to alleviate the negative impacts posed by these problems. Concretely, FedSpeed applies the prox-correction term on the current local updates to efficiently reduce the biases introduced by the prox-term, a necessary regularizer to maintain the strong local consistency. Furthermore, FedSpeed merges the vanilla stochastic gradient with a perturbation computed from an extra gradient ascent step in the neighborhood, thereby alleviating the issue of local over-fitting. Our theoretical analysis indicates that the convergence rate is related to both the communication rounds $T$ and local intervals $K$ with a upper bound $\small \mathcal{O}(1/T)$ if setting a proper local interval. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficiency of our proposed FedSpeed, which performs significantly faster and achieves the state-of-the-art (SOTA) performance on the general FL experimental settings than several baselines. Our code is available at \url{https://github.com/woodenchild95/FL-Simulator.git}.
What Is In a Survey? Simulation-Induced Selection Effects in Astronomy

Observational astronomy is plagued with selection effects that must be taken into account when interpreting data from astronomical surveys. Because of the physical limitations of observing time and instrument sensitivity, datasets are rarely complete. However, determining specifically what is missing from any sample is not always straightforward. For example, there are always more faint objects (such as galaxies) than bright ones in any brightness-limited sample, but faint objects may not be of the same kind as bright ones. Assuming they are can lead to mischaracterizing the population of objects near the boundary of what can be detected. Similarly, starting with nearby objects that can be well observed and assuming that objects much farther away (and sampled from a younger universe) are of the same kind can lead us astray. Demographic models of galaxy populations can be used as inputs to observing system simulations to create ``mock'' catalogues that can be used to characterize and account for multiple, interacting selection effects. The use of simulations for this purpose is common practice in astronomy, and blurs the line between observations and simulations; the observational data cannot be interpreted independent of the simulations. We will describe this methodology and argue that astrophysicists have developed effective ways to establish the reliability of simulation-dependent observational programs. The reliability depends on how well the physical and demographic properties of the simulated population can be constrained through independent observations. We also identify a new challenge raised by the use of simulations, which we call the ``problem of uncomputed alternatives. '' Sometimes the simulations themselves create unintended selection effects when the limits of what can be simulated lead astronomers to only consider a limited space of alternative proposals.
Ambipolar Heating of Magnetars

Magnetars, neutron stars thought to be with ultrastrong magnetic fields of 1014–15 G, are observed to be much hotter than ordinary pulsars with ∼1012 G, and additional heating sources are required. One possibility is heating by the ambipolar diffusion in the stellar core. This scenario is examined by calculating the models using the relativistic thermal evolutionary code without making the isothermal approximation. The results show that this scenario can be consistent with most of the observed magnetar temperature data.
Diffusion Probabilistic Models for Graph-Structured Prediction

This paper studies graph-structured prediction for supervised learning on graphs with node-wise or edge-wise target dependencies. To solve this problem, recent works investigated combining graph neural networks (GNNs) with conventional structured prediction algorithms like conditional random ﬁelds. However, in this work, we pursue an alternative direction building on the recent successes of diffusion probabilistic models (DPMs). That is, we propose a new framework using DPMs to make graph-structured predictions. In the fully supervised setting, our DPM captures the target dependencies by iteratively updating each target estimate based on the estimates of nearby targets. We also propose a variational expectation maximization algorithm to train our DPM in the semi-supervised setting. Extensive experiments verify that our framework consistently outperforms existing neural structured prediction models on inductive and transductive node classiﬁcation. We also demonstrate the competitive performance of our framework for algorithmic reasoning tasks. 1
Why Majority Judgement is not yet the solution for political elections, but can help finding it

Like many other voting systems, Majority Judgement suffers from the weaknesses of the underlying mathematical model: Elections as problem of choice or ranking. We show how the model can be enhanced to take into account the complete process starting from the whole set of persons having passive electoral rights and even the aspect of reelection. By a new view on abstentions from voting and an adaption of Majority Judgement with three grades, we are able to describe a complete process for an election that can be easily put into legislation and sets suitable incentives for politicians who want to be reelected.
Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations

Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours.
A composite electrodynamic mechanism to reconcile spatiotemporally resolved exciton transport in quantum dot superlattices

Quantum dot (QD) solids are promising optoelectronic materials; further advancing their device functionality requires understanding their energy transport mechanisms. The commonly invoked near-field Förster resonance energy transfer (FRET) theory often underestimates the exciton hopping rate in QD solids, yet no consensus exists on the underlying cause. In response, we use time-resolved ultrafast stimulated emission depletion (STED) microscopy, an ultrafast transformation of STED to spatiotemporally resolve exciton diffusion in tellurium-doped cadmium selenide–core/cadmium sulfide–shell QD superlattices. We measure the concomitant time-resolved exciton energy decay due to excitons sampling a heterogeneous energetic landscape within the superlattice. The heterogeneity is quantified by single-particle emission spectroscopy. This powerful multimodal set of observables provides sufficient constraints on a kinetic Monte Carlo simulation of exciton transport to elucidate a composite transport mechanism that includes both near-field FRET and previously neglected far-field emission/reabsorption contributions. Uncovering this mechanism offers a much-needed unified framework in which to characterize transport in QD solids and additional principles for device design.
Hyena Hierarchy: Towards Larger Convolutional Language Models

Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale. However, the core building block of Transformers, the attention operator, exhibits quadratic cost in sequence length, limiting the amount of context accessible. Existing subquadratic methods based on low-rank and sparse approximations need to be combined with dense attention layers to match Transformers, indicating a gap in capability. In this work, we propose Hyena, a subquadratic drop-in replacement for attention constructed by interleaving implicitly parametrized long convolutions and data-controlled gating. In recall and reasoning tasks on sequences of thousands to hundreds of thousands of tokens, Hyena improves accuracy by more than 50 points over operators relying on state-spaces and other implicit and explicit methods, matching attention-based models. We set a new state-of-the-art for dense-attention-free architectures on language modeling in standard datasets (WikiText103 and The Pile), reaching Transformer quality with a 20% reduction in training compute required at sequence length 2K. Hyena operators are twice as fast as highly optimized attention at sequence length 8K, and 100x faster at sequence length 64K.
Improving Scene Text Image Super-Resolution via Dual Prior Modulation Network

Scene text image super-resolution (STISR) aims to simultaneously increase the resolution and legibility of the text images, and the resulting images will significantly affect the performance of downstream tasks. Although numerous progress has been made, existing approaches raise two crucial issues: (1) They neglect the global structure of the text, which bounds the semantic determinism of the scene text. (2) The priors, e.g., text prior or stroke prior, employed in existing works, are extracted from pre-trained text recognizers. That said, such priors suffer from the domain gap including low resolution and blurriness caused by poor imaging conditions, leading to incorrect guidance. Our work addresses these gaps and proposes a plug-and-play module dubbed Dual Prior Modulation Network (DPMN), which leverages dual image-level priors to bring performance gain over existing approaches. Specifically, two types of prior-guided refinement modules, each using the text mask or graphic recognition result of the low-quality SR image from the preceding layer, are designed to improve the structural clarity and semantic accuracy of the text, respectively. The following attention mechanism hence modulates two quality-enhanced images to attain a superior SR result. Extensive experiments validate that our method improves the image quality and boosts the performance of downstream tasks over five typical approaches on the benchmark. Substantial visualizations and ablation studies demonstrate the advantages of the proposed DPMN. Code is available at: https://github.com/jdfxzzy/DPMN.
TMoE-P: Towards the Pareto Optimum for Multivariate Soft Sensors

Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of industrial manufacturing. The current progress stays in some direct applications of multitask network architectures; however, there are two fundamental issues remain yet to be investigated with these approaches: (1) negative transfer, where sharing representations despite the difference of discriminate representations for different objectives degrades performance; (2) seesaw phenomenon, where the optimizer focuses on one dominant yet simple objective at the expense of others. In this study, we reformulate the multi-variate soft sensor to a multi-objective problem, to address both issues and advance state-of-the-art performance. To handle the negative transfer issue, we first propose an Objective-aware Mixture-of-Experts (OMoE) module, utilizing objective-specific and objective-shared experts for parameter sharing while maintaining the distinction between objectives. To address the seesaw phenomenon, we then propose a Pareto Objective Routing (POR) module, adjusting the weights of learning objectives dynamically to achieve the Pareto optimum, with solid theoretical supports. We further present a Task-aware Mixture-of-Experts framework for achieving the Pareto optimum (TMoE-P) in multi-variate soft sensor, which consists of a stacked OMoE module and a POR module. We illustrate the efficacy of TMoE-P with an open soft sensor benchmark, where TMoE-P effectively alleviates the negative transfer and seesaw issues and outperforms the baseline models.
Some Fundamental Aspects about Lipschitz Continuity of Neural Network Functions

Lipschitz continuity is a simple yet pivotal functional property of any predictive model that lies at the core of its robustness, generalisation, and adversarial vulnerability. Our aim is to thoroughly investigate and characterise the Lipschitz behaviour of the functions learned via neural networks. Despite the signiﬁcant tightening of the bounds in the recent years, precisely estimating the Lipschitz constant continues to be a practical challenge and tight theoretical analyses, similarly, remain intractable. Therefore, we shift our perspective and instead attempt to uncover insights about the nature of Lipschitz constant of neural networks functions — by relying on the simplest and most general upper and lower bounds. We carry out an empirical investigation in a range of different settings (architectures, losses, optimisers, label noise, etc.), which reveals several fundamental and intriguing traits of the Lipschitz continuity of neural networks functions, In particular, we identify a remarkable double descent trend in both upper and lower bounds to the Lipschitz constant which tightly aligns with the typical double descent trend in the test loss.
Star formation concentration as a tracer of environmental quenching in action: a study of the eagle and c-eagle simulations

We study environmental quenching in the eagle/c-eagle cosmological hydrodynamic simulations over the last 11 Gyr (i.e. z = 0–2). The simulations are compared with observations from the SAMI Galaxy Survey at z = 0. We focus on satellite galaxies in galaxy groups and clusters ($10^{12}\, {\rm M}_{\odot }$ ≲ M200 < $3 \times 10^{15}\, {\rm M}_{\odot }$). A star-formation concentration index [C-index = log10(r50, SFR/r50, rband)] is defined, which measures how concentrated star formation is relative to the stellar distribution. Both eagle/c-eagle and SAMI show a higher fraction of galaxies with low C-index in denser environments at z = 0–0.5. Low C-index galaxies are found below the SFR–M⋆ main sequence (MS), and display a declining specific star formation rate (sSFR) with increasing radii, consistent with ‘outside-in’ environmental quenching. Additionally, we show that C-index can be used as a proxy for how long galaxies have been satellites. These trends become weaker at increasing redshift and are absent by z = 1–2. We define a quenching time-scale tquench as how long it takes satellites to transition from the MS to the quenched population. We find that simulated galaxies experiencing ‘outside-in’ environmental quenching at low redshift (z = 0 ∼ 0.5) have a long quenching time-scale (median tquench > 2 Gyr). The simulated galaxies at higher redshift (z = 0.7 ∼ 2) experience faster quenching (median tquench < 2 Gyr). At z ≳ 1–2 galaxies undergoing environmental quenching have decreased sSFR across the entire galaxy with no ‘outside-in’ quenching signatures and a narrow range of C-index, showing that on average environmental quenching acts differently than at z ≲ 1.