Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiparametric MRI for assessment of early response to neoadjuvant sunitinib in renal cell carcinoma

Purpose To detect early response to sunitinib treatment in metastatic clear cell renal cancer (mRCC) using multiparametric MRI. Method Participants with mRCC undergoing pre-surgical sunitinib therapy in the prospective NeoSun clinical trial (EudraCtNo: 2005-004502-82) were imaged before starting treatment, and after 12 days of sunitinib therapy using morphological MRI sequences, advanced diffusion-weighted imaging, measurements of R2* (related to hypoxia) and dynamic contrast-enhanced imaging. Following nephrectomy, participants continued treatment and were followed-up with contrast-enhanced CT. Changes in imaging parameters before and after sunitinib were assessed with the non-parametric Wilcoxon signed-rank test and the log-rank test was used to assess effects on survival. Results 12 participants fulfilled the inclusion criteria. After 12 days, the solid and necrotic tumor volumes decreased by 28% and 17%, respectively (p = 0.04). However, tumor-volume reduction did not correlate with progression-free or overall survival (PFS/OS). Sunitinib therapy resulted in a reduction in median solid tumor diffusivity D from 1298x10-6 to 1200x10-6mm2/s (p = 0.03); a larger decrease was associated with a better RECIST response (p = 0.02) and longer PFS (p = 0.03) on the log-rank test. An increase in R2* from 19 to 28s-1 (p = 0.001) was observed, paralleled by a decrease in Ktrans from 0.415 to 0.305min-1 (p = 0.01) and a decrease in perfusion fraction from 0.34 to 0.19 (p<0.001). Conclusions Physiological imaging confirmed efficacy of the anti-angiogenic agent 12 days after initiating therapy and demonstrated response to treatment. The change in diffusivity shortly after starting pre-surgical sunitinib correlated to PFS in mRCC undergoing nephrectomy, however, no parameter predicted OS. Trial registration EudraCtNo: 2005-004502-82.
Calorie Restriction Protects against Contrast-Induced Nephropathy via SIRT1/GPX4 Activation

Calorie restriction (CR) extends lifespan and increases resistance to multiple forms of stress, including renal ischemia-reperfusion (I/R) injury. However, whether CR has protective effects on contrast-induced nephropathy (CIN) remains to be determined. In this study, we evaluated the therapeutic effects of CR on CIN and investigated the potential mechanisms. CIN was induced by the intravenous injection of iodinated contrast medium (CM) iopromide (1.8 g/kg) into Sprague Dawley rats with normal food intake or 40% reduced food intake, 4 weeks prior to iopromide administration. We found that CR was protective of CIN, assessed by renal structure and function. CM increased apoptosis, reactive oxygen species (ROS), and inflammation in the renal outer medulla, which were decreased by CR. The silent information regulator 1 (SIRT1) participated in the protective effect of CR on CIN, by upregulating glutathione peroxidase 4 (GPX4), a regulator of ferroptosis, because this protective effect was reversed by EX527, a specific SIRT1 antagonist. Our study showed that CR protected CIN via SIRT1/GPX4 activation. CR may be used to mitigate CIN.
Chemical Composition and Antibacterial Activity of the Lippia origanoides Kunth Essential Oil from the Carajás National Forest, Brazil

Species of the genus Lippia are rich in essential oils and have shown antibacterial properties, which may be related to oils' chemical composition. The present work aimed to evaluate the antimicrobial potential of Lippia origanoides Kunth against two bacteria strains: Escherichia coli and Staphylococcus aureus. Leaf essential oils were obtained by hydrodistillation in a modified Clevenger-type apparatus, and their chemical composition was determined by gas chromatography coupled to mass spectrometry (GC/MS) and flame ionization detection (GC/FID). We identified 28 compounds, representing 98.87% of the total concentration of the essential oil. The compounds identified at the highest concentrations were 1,8-cineole (35.04%), carvacrol (11.32%), p-cymene (8.53%), α-pinene (7.17%), and γ-terpinene (7.16%). The leaf essential oil of L. origanoides showed antibacterial action on biological isolates of Escherichia coli and Staphylococcus aureus. For Escherichia coli, the oil presented bactericidal action at concentrations of 5–20 μL/mL. Regarding Staphylococcus aureus, the bactericidal effect was noted at 20 μL/mL and the bacteriostatic action was noted around 2.5–10 μL/mL. Given the results obtained, L. origanoides essential oil showed promising biological potential against Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria, thus encouraging further studies on substances isolated from this species to contribute to the development of new antimicrobial drugs.
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks. This paper investigates whether and to what extent one can use differential Privacy (DP) to protect both privacy and robustness in FL. To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. Our experiments show that both DP variants do d fend against backdoor attacks, albeit with varying levels of protection-utility trade-offs, but anyway more effectively than other robustness defenses. DP also mitigates white-box membership inference attacks in FL, and our work is the first to show it empirically. Neither LDP nor CDP, however, defend against property inference. Overall, our work provides a comprehensive, re-usable measurement methodology to quantify the trade-offs between robustness/privacy and utility in differentially private FL.
Critical gauged Schr\"{o}dinger equations in R^2 with vanishing potentials

We study a class of gauged nonlinear Schr¨odinger equations in the plane 2 , where h u ( s ) = ´ s 0 r 2 u 2 ( r ) dr , λ, µ > 0 are constants, V ( | x | ) and K ( | x | ) are continuous functions vanishing at inﬁnity. Assume that f is of critical exponential growth and g ( x ) = g ( | x | ) satisﬁes some technical assumptions with 1 ≤ q < 2, we obtain the existence of two nontrivial solutions via the Mountain-Pass theorem and Ekeland’s variational principle. Moreover, with the help of the genus theory, we prove the existence of inﬁnitely many solutions if f in addition
Rebounding Cores to Build Star Cluster Multiple Populations

We present a novel approach to the riddle of star cluster multiple populations. Stars form from molecular cores. But not all cores form stars. Following their initial compression, such “failed” cores re-expand, rather than collapsing. We propose that their formation and subsequent dispersal regulate the gas density of cluster-forming clumps and, therefore, their core and star formation rates. Clumps for which failed cores are the dominant core type experience star formation histories with peaks and troughs (i.e., discrete star formation episodes). In contrast, too few failed cores results in smoothly decreasing star formation rates. We identify three main parameters shaping the star formation history of a clump: the star and core formation efficiencies per free-fall time, and the timescale on which failed cores return to the clump gas. The clump mass acts as a scaling factor. We use our model to constrain the density and mass of the Orion Nebula Cluster progenitor clump, and to caution that the star formation histories of starburst clusters may contain close-by peaks concealed by stellar age uncertainties. Our model generates a great variety of star formation histories. Intriguingly, the chromosome maps and O–Na anticorrelations of old globular clusters also present diverse morphologies. This prompts us to discuss our model in the context of globular cluster multiple stellar populations. More massive globular clusters exhibit stronger multiple stellar population patterns, which our model can explain if the formation of the polluting stars requires a given stellar mass threshold.
Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering

Multi‐fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the approximation of high‐dimensional scalar functions with low intrinsic dimensionality. By introducing a low dimensional bias we can fight the curse of dimensionality affecting these quantities of interest, especially for many‐query applications. We seek a gradient‐based reduction of the parameter space through linear active subspaces or a nonlinear transformation of the input space. Then we build a low‐fidelity response surface based on such reduction, thus enabling nonlinear autoregressive multi‐fidelity Gaussian process regression without the need of running new simulations with simplified physical models. This has a great potential in the data scarcity regime affecting many engineering applications. In this work we present a new multi‐fidelity approach that involves active subspaces and the nonlinear level‐set learning method, starting from the preliminary analysis previously conducted (Romor F, Tezzele M, Rozza G. Proceedings in Applied Mathematics & Mechanics. Wiley Online Library; 2021). The proposed framework is tested on two high‐dimensional benchmark functions, and on a more complex car aerodynamics problem. We show how a low intrinsic dimensionality bias can increase the accuracy of Gaussian process response surfaces.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration

We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine without keypoint detection. On a coarse scale and guided by a weighting scheme, our model firstly learns to match down-sampled nodes whose vicinity points share more overlap, which significantly shrinks the search space of a consecutive stage. On a finer scale, node proposals are consecutively expanded to patches that consist of groups of points together with associated descriptors. Point correspondences are then refined from the overlap areas of corresponding patches, by a density-adaptive matching module capable to deal with varying point density. Extensive evaluation of CoFiNet on both indoor and outdoor standard benchmarks shows our superiority over existing methods. Especially on 3DLoMatch where point clouds share less overlap, CoFiNet significantly outperforms state-of-the-art approaches by at least 5% on Registration Recall, with at most two-third of their parameters.
From classical to quantum regime of topological surface states via defect engineering

Since the notion of topological insulator (TI) was envisioned in late 2000s, topology has become a new paradigm in condensed matter physics. Realization of topology as a generic property of materials has led to numerous predictions of topological effects. Although most of the classical topological effects, directly resulting from the presence of the spin-momentum-locked topological surface states (TSS), were experimentally confirmed soon after the theoretical prediction of TIs, many topological quantum effects remained elusive for a long while. It turns out that native defects, particularly interfacial defects, have been the main culprit behind this impasse. Even after quantum regime is achieved for the bulk states, TSS still tends to remain in the classical regime due to high density of interfacial defects, which frequently donate mobile carriers due to the very nature of the topologically-protected surface states. However, with several defect engineering schemes that suppress these effects, a series of topological quantum effects have emerged including quantum anomalous Hall effect, quantum Hall effect, quantized Faraday/Kerr rotations, topological quantum phase transitions, axion insulating state, zeroth-Landau level state, etc. Here, we review how these defect engineering schemes have allowed topological surface states to pull out of the murky classical regime and reveal their elusive quantum signatures, over the past decade.
Measuring and Modeling Neighborhoods

Granular geographic data present new opportunities to understand how neighborhoods are formed, and how they influence politics. At the same time, the inherent subjectivity of neighborhoods creates methodological challenges in measuring and modeling them. We develop an open-source survey instrument that allows respondents to draw their neighborhoods on a map. We also propose a statistical model to analyze how the characteristics of respondents and local areas determine subjective neighborhoods. We conduct two surveys: collecting subjective neighborhoods from voters in Miami, New York City, and Phoenix, and asking New York City residents to draw a community of interest for inclusion in their city council district. Our analysis shows that, holding other factors constant, White respondents include census blocks with more White residents in their neighborhoods. Similarly, Democrats and Republicans are more likely to include co-partisan areas. Furthermore, our model provides more accurate out-of-sample predictions than standard neighborhood measures.
Learning Graph Cellular Automata

Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we focus on a generalised version of typical CA, called graph cellular automata (GCA), in which the lattice structure is replaced by an arbitrary graph. In particular, we extend previous work that used convolutional neural networks to learn the transition rule of conventional CA and we use graph neural networks to learn a variety of transition rules for GCA. First, we present a general-purpose architecture for learning GCA, and we show that it can represent any arbitrary GCA with finite and discrete state space. Then, we test our approach on three different tasks: 1) learning the transition rule of a GCA on a Voronoi tessellation; 2) imitating the behaviour of a group of flocking agents; 3) learning a rule that converges to a desired target state.
The weighted periodic-parabolic degenerate logistic equation

The main goal of this paper is twofold. First, it characterizes the existence of positive periodic solutions for a general class of weighted periodic-parabolic logistic problems of degenerate type (see (1.1)). This result provides us with is a substantial generalization of Theorem 1.1 of Daners and López-Gómez [12] even for the elliptic counterpart of (1.1), and of some previous findings of the authors in [1] and [2]. Then, it sharpens some results of [19] by enlarging the class of critical degenerate weight functions for which (1.1) admits a positive periodic solution in an unbounded interval of values of the parameter λ. The latest findings, not having any previous elliptic counterpart, are utterly new and of great interest in Population Dynamics. 2010 MSC: 35K55, 35K57, 35B09, 35B10, 35Q92.
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions

We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions. The goal of this abstract is twofold: (1) To garner greater interest amongst the tensor research community for creating methods and analysis for approximate RL, (2) To elucidate the generalised setting of factored action spaces where tensor decompositions can be used. We use cooperative multi-agent reinforcement learning scenario as the exemplary setting where the action space is naturally factored across agents and learning becomes intractable without resorting to approximation on the underlying hypothesis space for candidate solutions.
RoMA: Robust Model Adaptation for Offline Model-based Optimization

We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries. A popular approach to solving this problem is maintaining a proxy model, e.g., a deep neural network (DNN), that approximates the true objective function. Here, the main challenge is how to avoid adversarially optimized inputs during the search, i.e., the inputs where the DNN highly overestimates the true objective function. To handle the issue, we propose a new framework, coined robust model adaptation (RoMA), based on gradient-based optimization of inputs over the DNN. Specifically, it consists of two steps: (a) a pre-training strategy to robustly train the proxy model and (b) a novel adaptation procedure of the proxy model to have robust estimates for a specific set of candidate solutions. At a high level, our scheme utilizes the local smoothness prior to overcome the brittleness of the DNN. Experiments under various tasks show the effectiveness of RoMA compared with previous methods, obtaining state-of-the-art results, e.g., RoMA outperforms all at 4 out of 6 tasks and achieves runner-up results at the remaining tasks.
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition

It is well known that vision classification models suffer from poor calibration in the face of data distribution shifts. In this paper, we take a geometric approach to this problem. We propose Geometric Sensitivity Decomposition (GSD) which decomposes the norm of a sample feature embedding and the angular similarity to a target classifier into an instance-dependent and an instance-independent component. The instance-dependent component captures the sensitive information about changes in the input while the instance-independent component represents the insensitive information serving solely to minimize the loss on the training dataset. Inspired by the decomposition, we analytically derive a simple extension to current softmax-linear models, which learns to disentangle the two components during training. On several common vision models, the disentangled model outperforms other calibration methods on standard calibration metrics in the face of out-of-distribution (OOD) data and corruption with significantly less complexity. Specifically, we surpass the current state of the art by 30.8% relative improvement on corrupted CIFAR100 in Expected Calibration Error. Code available at https://github.com/GT-RIPL/Geometric-Sensitivity-Decomposition.git.
Virtual Artin groups

Starting from the observation that the standard presentation of a virtual braid group mixes the standard presentation of the corresponding braid group with the standard presentation of the corresponding symmetric group and some mixed relations that mimic the action of the symmetric group on its root system, we define a virtual Artin group VA[Γ]${\rm VA}[\Gamma ]$ of a Coxeter graph Γ$\Gamma$ mixing the standard presentation of the Artin group A[Γ]$A[\Gamma ]$ with the standard presentation of the Coxeter group W[Γ]$W[\Gamma ]$ and some mixed relations that mimic the action of W[Γ]$W[\Gamma ]$ on its root system. By definition, we have two epimorphisms πK:VA[Γ]→W[Γ]$\pi _K:{\rm VA}[\Gamma ]\rightarrow W[\Gamma ]$ and πP:VA[Γ]→W[Γ]$\pi _P:{\rm VA}[\Gamma ]\rightarrow W[\Gamma ]$ whose kernels are denoted by KVA[Γ]${\rm KVA}[\Gamma ]$ and PVA[Γ]${\rm PVA}[\Gamma ]$ , respectively. We calculate presentations for these two subgroups. In particular, KVA[Γ]${\rm KVA}[\Gamma ]$ is an Artin group. We prove that the center of any virtual Artin group is trivial. In the case where Γ$\Gamma$ is of spherical type or of affine type, we show that each free of infinity parabolic subgroup of KVA[Γ]${\rm KVA}[\Gamma ]$ is also of spherical type or of affine type, and we show that VA[Γ]${\rm VA}[\Gamma ]$ has a solution to the word problem. In the case where Γ$\Gamma$ is of spherical type we show that KVA[Γ]${\rm KVA}[\Gamma ]$ satisfies the K(π,1)$K(\pi ,1)$ conjecture and we infer the cohomological dimension of KVA[Γ]${\rm KVA}[\Gamma ]$ and the virtual cohomological dimension of VA[Γ]${\rm VA}[\Gamma ]$ . In the case where Γ$\Gamma$ is of affine type we determine upper bounds for the cohomological dimension of KVA[Γ]${\rm KVA}[\Gamma ]$ and for the virtual cohomological dimension of VA[Γ]${\rm VA}[\Gamma ]$ .
Stochastic Low-frequency Variability in Three-dimensional Radiation Hydrodynamical Models of Massive Star Envelopes

Increasing main-sequence stellar luminosity with stellar mass leads to the eventual dominance of radiation pressure in stellar-envelope hydrostatic balance. As the luminosity approaches the Eddington limit, additional instabilities (beyond conventional convection) can occur. These instabilities readily manifest in the outer envelopes of OB stars, where the opacity increase associated with iron yields density and gas-pressure inversions in 1D models. Additionally, recent photometric surveys (e.g., TESS) have detected excess broadband low-frequency variability in power spectra of OB star lightcurves, called stochastic low-frequency variability (SLFV). This motivates our novel 3D Athena++ radiation hydrodynamical (RHD) simulations of two 35 M ⊙ star envelopes (the outer ≈15% of the stellar radial extent), one on the zero-age main sequence and the other in the middle of the main sequence. Both models exhibit turbulent motion far above and below the conventional iron-opacity peak convection zone (FeCZ), obliterating any “quiet” part of the near-surface region and leading to velocities at the photosphere of 10–100 km s−1, directly agreeing with spectroscopic data. Surface turbulence also produces SLFV in model lightcurves with amplitudes and power-law slopes that are strikingly similar to those of observed stars. The characteristic frequencies associated with SLFV in our models are comparable to the thermal time in the FeCZ (≈3–7 day−1). These ab initio simulations are directly validated by observations and, though more models are needed, we remain optimistic that 3D RHD models of main-sequence O-star envelopes exhibit SLFV originating from the FeCZ.