Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography

Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratiﬁcation biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer description of cardiac function can be obtained by estimating functional biomarkers from the complete cardiac cycle. In this work we propose for the ﬁrst time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle. These biomarkers will allow clinicians to obtain a much richer picture of the heart in health and disease. The AI model is based on the ’nn-Unet’ framework and was trained and tested using four diﬀerent databases. Results show excellent agreement between manual and automated analysis and showcase the potential of the advanced systolic and diastolic biomarkers for patient stratiﬁcation. Finally, for a subset of 50 cases, we perform a correlation analysis between clinical biomarkers derived from echocardiography and cardiac magnetic resonance and we show a very strong relationship between the two modalities.
Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. It enables the rapid prototyping of complex communication system architectures and provides native support for the integration of neural networks. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation. This allows researchers to focus on their research, making it more impactful and reproducible, while saving time implementing components outside their area of expertise. This white paper provides a brief introduction to Sionna, explains its design principles and features, as well as future extensions, such as integrated ray tracing and custom CUDA kernels. We believe that Sionna is a valuable tool for research on next-generation communication systems, such as 6G, and we welcome contributions from our community.
GradViT: Gradient Inversion of Vision Transformers

In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given model weights and the corresponding gradients. We introduce a method, named GradViT, that optimizes random noise into naturally looking images via an iterative process. The optimization objective consists of (i) a loss on matching the gradients, (ii) image prior in the form of distance to batch-normalization statistics of a pretrained CNN model, and (iii) a total variation regularization on patches to guide correct recovery locations. We propose a unique loss scheduling function to overcome local minima during optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and observe unprecedentedly high fidelity and closeness to the original (hidden) data. During the analysis we find that vision transformers are significantly more vulnerable than previously studied CNNs due to the presence of the attention mechanism. Our method demonstrates new state-of-the-art results for gradient inversion in both qualitative and quantitative metrics. Project page at https://gradvit.github. io/.
Efficient Neural Network Analysis with Sum-of-Infeasibilities

Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activation functions. Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation. The cost function, referred to as the Sum-of-Infeasibilities (SoI), is designed so that its minimum is zero and achieved only if all the activation functions are satisfied. We propose a stochastic procedure, DeepSoI, to efficiently minimize the SoI. An extension to a canonical case-analysis-based complete search procedure can be achieved by replacing the convex procedure executed at each search state with DeepSoI. Extending the complete search with DeepSoI achieves multiple simultaneous goals: 1) it guides the search towards a counter-example; 2) it enables more informed branching decisions; and 3) it creates additional opportunities for bound derivation. An extensive evaluation across different benchmarks and solvers demonstrates the benefit of the proposed techniques. In particular, we demonstrate that SoI significantly improves the performance of an existing complete search procedure. Moreover, the SoI-based implementation outperforms other state-of-the-art complete verifiers. We also show that our technique can efficiently improve upon the perturbation bound derived by a recent adversarial attack algorithm.
AGN-driven outflows and the formation of Lyα nebulae around high-z quasars

The detection of Lyα nebulae around z ≳ 6 quasars reveals extended gas reservoirs around the first rapidly growing supermassive black holes. Observations of z > 6 quasars can be explained by cosmological models provided that the black holes by which they are powered evolve in rare, massive dark matter haloes. Whether these models also explain the observed extended Lyα emission remains an open question. We post-process a suite of cosmological, radiation-hydrodynamic simulations targeting a quasar host halo at z > 6 with the Lyα radiative transfer code Rascas. A combination of recombination radiation from photo-ionised hydrogen and emission from collisionally excited gas powers Lyα nebulae with a surface brightness profile in close agreement with observations. We also find that, even on its own, resonant scattering of the Lyα line associated to the quasar’s broad line region can also generate Lyα emission on $\sim 100 \, \rm kpc$ scales, resulting in comparable agreement with observed surface brightness profiles. Even if powered by a broad quasar Lyα line, Lyα nebulae have narrow line-widths $\lesssim 1000 \, \rm km \, s^{-1}$, consistent with observational constraints. Even if there is no quasar, we find that halo gas cooling produces a faint, extended Lyα glow. However, to explain the brightest Lyα nebulae, our simulations unambiguously require quasar-powered outflows to clear out the galactic nucleus and allow the Lyα flux to escape and still remain resonant with halo gas. The close match between observations and simulations with quasar outflows suggests that AGN feedback already operates before z = 6 and confirms that high-z quasars reside in massive haloes tracing overdensities.
Coherent structure detection and the inverse cascade mechanism in two-dimensional Navier–Stokes turbulence

Abstract Coherent structures in two-dimensional Navier–Stokes turbulence are ubiquitously observed in nature, experiments and numerical simulations. The present study conducts a comparison between several structure detection schemes based on the Okubo–Weiss criterion, the vorticity magnitude and Lagrangian coherent structures (LCSs), focusing on the inverse cascade in two-dimensional hydrodynamic turbulence. A recently introduced vortex scaling phenomenology (Burgess & Scott, J. Fluid Mech., vol. 811, 2017, pp. 742–756) allows the quantification of the respective thresholds required by these methods based on physical properties of the flow. The resulting improved comparability allows us to identify characteristic relative differences in the detection sensitivity between the employed structure detection techniques. With respect to the inverse cascade of energy, coherent structures contribute, as expected, substantially less to the cross-scale flux than the residual incoherent parts of the flow although the energetically dominant coherent structures lead to an important large-scale deformation of the energy spectrum. This cascade inactivity can be understood by an increased misalignment of strain-rate and subgrid stress tensors within coherent structures. At the same time, the structures exhibit strong and localised nonlinear cross-scale interactions that appear to stabilise them. We quantify and interpret the resulting shape preservation of coherent structures in terms of a multi-scale gradient approach (Eyink, J. Fluid Mech., vol. 549, 2006, pp. 191–214) as the depletion of strain rotation and vorticity gradient stretching whereas the dynamics of the residual fluctuations are consistent with the vortex thinning picture.
On the Robustness of Offensive Language Classifiers

Social media platforms are deploying machine learning based offensive language classification systems to combat hateful, racist, and other forms of offensive speech at scale. However, despite their real-world deployment, we do not yet comprehensively understand the extent to which offensive language classifiers are robust against adversarial attacks. Prior work in this space is limited to studying robustness of offensive language classifiers against primitive attacks such as misspellings and extraneous spaces. To address this gap, we systematically analyze the robustness of state-of-the-art offensive language classifiers against more crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. Our results on multiple datasets show that these crafty adversarial attacks can degrade the accuracy of offensive language classifiers by more than 50% while also being able to preserve the readability and meaning of the modified text.
A Broad Study of Pre-training for Domain Generalization and Adaptation

Deep models must learn robust and transferable representations in order to perform well on new domains. While domain transfer methods (e.g., domain adaptation, domain generalization) have been proposed to learn transferable representations across domains, they are typically applied to ResNet backbones pre-trained on ImageNet. Thus, existing works pay little attention to the effects of pre-training on domain transfer tasks. In this paper, we provide a broad study and in-depth analysis of pre-training for domain adaptation and generalization, namely: network architectures, size, pre-training loss, and datasets. We observe that simply using a state-of-the-art backbone outperforms existing state-of-the-art domain adaptation baselines and set new baselines on Office-Home and DomainNet improving by 10.7\% and 5.5\%. We hope that this work can provide more insights for future domain transfer research.
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes

Neural networks are known to be highly sensitive to adversarial examples. These may arise due to different factors, such as random initialization, or spurious correlations in the learning problem. To better understand these factors, we provide a precise study of the adversarial robustness in different scenarios, from initialization to the end of training in different regimes, as well as intermediate scenarios, where initialization still plays a role due to"lazy"training. We consider over-parameterized networks in high dimensions with quadratic targets and infinite samples. Our analysis allows us to identify new tradeoffs between approximation (as measured via test error) and robustness, whereby robustness can only get worse when test error improves, and vice versa. We also show how linearized lazy training regimes can worsen robustness, due to improperly scaled random initialization. Our theoretical results are illustrated with numerical experiments.
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from their satisfactory performance. However, the previous works mainly focus on encoding biological features and chemical structures of drugs and targets, with a lack of exploiting the essential topological information from the drug-target affinity network. In this paper, we propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to incorporate the intrinsic properties of drug/target molecules and the topological affinities of drug-target pairs. In this architecture, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets. Comprehensive experimental results under different scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art models and shows better model generalization among all the scenarios.
A P ] 2 2 M ar 2 02 2 ON THE L 2 RATE OF CONVERGENCE IN THE LIMIT FROM HARTREE TO VLASOV – POISSON EQUATION

Using a new stability estimate for the difference of the square roots of two solutions of the Vlasov–Poisson equation, we obtain the convergence in L of the Wigner transform of a solution of the Hartree equation with Coulomb potential to a solution of the Vlasov–Poisson equation, with a rate of convergence proportional to ~, improving the rate ~3/4−ε obtained in [L. Lafleche, C. Saffirio: Analysis & PDE, to appear] for this particular case. Another reason of interest of this new method is that it is thought of as being closer to methods used to prove the mean-field limit from the many-body Schrödinger equation towards the Hartree–Fock equation for mixed states.
Prevalence and determinants of peripheral arterial disease in children with nephrotic syndrome

Peripheral arterial disease (PAD) is the least studied complication of nephrotic syndrome (NS). Risk factors which predispose children with NS to developing PAD include hyperlipidaemia, hypertension and prolonged use of steroids. The development of PAD significantly increases the morbidity and mortality associated with NS as such children are prone to sudden cardiac death. The ankle brachial index (ABI) is a tool that has been proven to have high specificity and sensitivity in detecting PAD even in asymptomatic individuals. We aimed to determine the prevalence of PAD in children with NS and to identify risk factors that can independently predict its development. A comparative cross-sectional study was conducted involving 200 subjects (100 with NS and 100 apparently healthy comparative subjects that were matched for age, sex and socioeconomic class). Systolic blood pressures were measured in all limbs using the pocket Doppler machine (Norton Doppler scan machine). ABI was calculated as a ratio of ankle to arm systolic blood pressure. PAD was defined as ABI less than 0.9. The prevalence of PAD was significantly higher in children with NS than matched comparison group (44.0% vs 6.0%, p < 0.001). Average values of waist and hip circumference were significantly higher in subjects with PAD than those without PAD (61.68± 9.1cm and 67.6± 11.2 cm vs 57.03 ± 8.3cm and 65.60± 12.5cm respectively, p< 0.005). Serum lipids (triglyceride, very low density lipoprotein, total cholesterol and low density lipoprotein) were also significantly higher in subjects with PAD than those without PAD [106.65mg/dl (67.8–136.7) vs 45.72mg/dl (37.7–61.3), 21.33mg/dl (13.6–27.3) vs 9.14mg/dl (7.5–12.3), 164.43mg/dl (136.1–259.6) vs 120.72mg/dl (111.1–142.1) and 93.29mg/dl (63.5–157.3) vs 61.84mg/dl (32.6–83.1), respectively p< 0.05]. Increasing duration since diagnosis of NS, having a steroid resistant NS and increasing cumulative steroid dose were independent predictors of PAD in children with NS; p< 0.05 respectively. With these findings, it is recommended that screening for PAD in children with NS should be done to prevent cardiovascular complications before they arise.
A bilateral SPCN is elicited by to‐be‐memorized visual stimuli displayed along the vertical midline

Abstract We recently showed that deploying attention to target stimuli displayed along the vertical meridian elicits a bilateral N2pc, that we labeled N2pcb (Psychophysiology). Here we investigated whether a different component, the sustained posterior contralateral negativity (SPCN), shows the same property when a varying number of visual stimuli are displayed either laterally or on the vertical meridian. We displayed one or two cues that designated candidate targets to be detected in a search array that was displayed after a retention interval. The cues were either on the horizontal meridian or on the vertical meridian. When the cues were on the horizontal meridian, we observed an N2pc followed by an SPCN in their classic form, as negativity increments contralateral to the cues. As expected, SPCN amplitude was greater when two cues had to be memorized than when only one cue had to be memorized. When the cues were on the vertical meridian, we observed an N2pcb followed by a bilateral SPCN (or SPCNb). Critically, like SPCN, SPCNb amplitude was greater when two cues had to be memorized than when only one cue had to be memorized. A series of additional parametrical and topographical comparisons between N2pcb and SPCNb revealed similarities but also some important differences between these two components that we interpreted as evidence for their distinct neural sources.
Habitat‐linked genetic variation supports microgeographic adaptive divergence in an island‐endemic bird species

We investigated the potential mechanisms driving habitat‐linked genetic divergence within a bird species endemic to a single 250‐km2 island. The island scrub‐jay (Aphelocoma insularis) exhibits microgeographic divergence in bill morphology across pine–oak ecotones on Santa Cruz Island, California (USA), similar to adaptive differences described in mainland congeners over much larger geographic scales. To test whether individuals exhibit genetic differentiation related to habitat type and divergence in bill length, we genotyped over 3000 single nucleotide polymorphisms in 123 adult island scrub‐jay males from across Santa Cruz Island using restriction site‐associated DNA sequencing. Neutral landscape genomic analyses revealed that genome‐wide genetic differentiation was primarily related to geographic distance and differences in habitat composition. We also found 168 putatively adaptive loci associated with habitat type using multivariate redundancy analysis while controlling for spatial effects. Finally, two genome‐wide association analyses revealed a polygenic basis to variation in bill length with multiple loci detected in or near genes known to affect bill morphology in other birds. Our findings support the hypothesis that divergent selection at microgeographic scales can cause adaptive divergence in the presence of ongoing gene flow.
Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes

Abstract Early psychosocial adversities exist at many levels, including caregiving-related, extrafamilial, and sociodemographic, which despite their high interrelatedness may have unique impacts on development. In this paper, we focus on caregiving-related early adversities (crEAs) and parse the heterogeneity of crEAs via data reduction techniques that identify experiential cooccurrences. Using network science, we characterized crEA cooccurrences to represent the comorbidity of crEA experiences across a sample of school-age children (n = 258; 6–12 years old) with a history of crEAs. crEA dimensions (variable level) and crEA subtypes (subject level) were identified using parallel factor analysis/principal component analysis and graph-based Louvain community detection. Bagging enhancement with cross-validation provided estimates of robustness. These data-driven dimensions/subtypes showed evidence of stability, transcended traditional sociolegally defined groups, were more homogenous than sociolegally defined groups, and reduced statistical correlations with sociodemographic factors. Finally, random forests showed both unique and common predictive importance of the crEA dimensions/subtypes for childhood mental health symptoms and academic skills. These data-driven outcomes provide additional tools and recommendations for crEA data reduction to inform precision medicine efforts in this area.
Combined hyperthermia and radiotherapy for prostate cancer: a systematic review

Abstract Optimization of treatment strategies for prostate cancer patients treated with curative radiation therapy (RT) represents one of the major challenges for the radiation oncologist. Dose escalation or combination of RT with systemic therapies is used to improve tumor control in patients with unfavorable prostate cancer, at the risk of increasing rates and severity of treatment-related toxicities. Elevation of temperature to a supra-physiological level has been shown to both increase tumor oxygenation and reduce DNA repair capabilities. Thus, hyperthermia (HT) combined with RT represents a compelling treatment strategy to improve the therapeutic ratio in prostate cancer patients. The aim of the present systematic review is to report on preclinical and clinical evidence supporting the combination of HT and RT for prostate cancer, discussing future applications and developments of this combined treatment.
Pearls of wisdom for aspiring physician-scientist residency applicants and program directors

Postgraduate physician-scientist training programs (PSTPs) enhance the experiences of physician-scientist trainees following medical school graduation. PSTPs usually span residency and fellowship training, but this varies widely by institution. Applicant competitiveness for these programs would be enhanced, and unnecessary trainee anxiety relieved, by a clear understanding of what factors define a successful PSTP matriculant. Such information would also be invaluable to PSTP directors and would allow benchmarking of their admissions processes with peer programs. We conducted a survey of PSTP directors across the US to understand the importance they placed on components of PSTP applications. Of 41 survey respondents, most were from internal medicine and pediatrics residency programs. Of all components in the application, two elements were considered very important by a majority of PSTP directors: (a) having one or more first-author publications and (b) the thesis advisor’s letter. Less weight was consistently placed on factors often considered more relevant for non-physician-scientist postgraduate applicants — such as US Medical Licensing Examination scores, awards, and leadership activities. The data presented here highlight important metrics for PSTP applicants and directors and suggest that indicators of scientific productivity and commitment to research outweigh traditional quantitative measures of medical school performance.