Miracle-dz/UniFAR-FTUs huggingface dataset:
Adakites: some variations on a theme

Adakites were proposed over a decade ago to be products of the melting of young subducted oceanic crust. In fact, several new localities have been discovered since the original work documented approximately ten localities in modern arcs (e. g., southwestern Japan, Trans Mexican Volcanic Belt, etc.). But work over the past ten years has also shown that adakites can be generated by other processes during subduction (e. g., along the edge of tears in the subducting slab, remnant slabs left in the upper mantle, etc.). In addition, adakites appear to be associated with a suite of rocks including high-Mg andesites resulting from either adakite interaction with the mantle (Adak-type) or melting of the mantle during adakite interaction (Piip-type), niobium enriched arc basalts (NEAB) that are believed to be derived from the partial melting of a mantle metasomatized extensively by adakites, and possibly boninites (several researchers have found an adakite component in boninites). A new rock suite, the adakite metasomatic volcanic series, has been proposed to account for the various associations. In addition, a large number of NEAB have been found to contain ultramafic mantle xenoliths with clear evidence of reaction between ultradepleted mantle and adakites.
Concomitant antihypertensive medication and outcome of patients with metastatic castration‐resistant prostate cancer receiving enzalutamide or abiraterone acetate

Abstract Background The introduction of novel hormonal therapies represented by enzalutamide (ENZ) and abiraterone acetate (ABI) has reached a great progress in the treatment of metastatic castration‐resistant prostate cancer (mCRPC). The majority of mCRPC patients are elderly suffering from chronic co‐morbidities requiring use of various concomitant medications. In the present study, we focused on impact of concomitant antihypertensive medication on the outcomes of mCRPC patients treated with ENZ or ABI. Methods In total, 300 patients were included and their clinical data were retrospectively analyzed. Results Angiotensin‐converting enzyme inhibitors (ACEIs) represented the only concomitant medication significantly associated with survival. The median radiographic progression‐free survival (rPFS) and overall survival (OS) for patients using ACEIs were 15.5 and 32.3 months compared to 10.7 and 24.0 months for those not using ACEIs (p = 0.0053 and p = 0.0238, respectively). Cox multivariable analysis revealed the use of ACEIs a significant predictive factor for both rPFS (HR = 0.704, p = 0.0364) and OS (HR = 0.592, p = 0.0185). Conclusion The findings of this study suggest an association between the concomitant use of ACEIs and longer survival of mCRPC patients receiving ENZ or ABI therapy.
Making a HIIT: Methods for quantifying intensity in high-intensity interval training in schools and validity of session rating of perceived exertion

ABSTRACT Using the data from Making a HIIT, this paper aimed to: 1) investigate the different heart rate (HR) quantification methods reported in school-based high-intensity interval training (HIIT) studies; and 2) assess the criterion validity of session rating of perceived exertion (RPE). During an 8-week HIIT intervention, 213 students (13.1 (0.6) years; 46% female) completed 10-minute HIIT workouts during physical education lessons. In total, 1057 HR and RPE measurements were collected across 68 HIIT workouts. For aim 1, the average and peak HR across all participants and workouts were 79% (8%) and 92% (6%) of HRmax, respectively. The average RPE was 6 (2) points on a 10-point scale. An average of 51% of students in a class had an average HR ≥ 80% for each workout. The between-person variation for peak and average HR were 19% and 30% , respectively. Both average and peak HR decreased by 0.5% each week (p < 0.001). To assess aim 2, a within-participant correlation was calculated for the internal training load produced using HR and RPE data. The correlation was 0.39 (p < 0.001), which suggests utility of using RPE when HR is not a viable option.
A modified recombinant adenovirus vector containing dual rabies virus G expression cassettes confers robust and long-lasting humoral immunity in mice, cats, and dogs

ABSTRACT During the COVID-19 epidemic, the incidence of rabies has increased in several countries, especially in remote and disadvantaged areas, due to inadequate surveillance and declining immunization coverage. Multiple vaccinations with inactivated rabies virus vaccines for pre- or post-exposure prophylaxis are considered inefficient, expensive and impractical in developing countries. Herein, three modified human recombinant adenoviruses type 5 designated Adv-RVG, Adv-E1-RVG, and Adv-RVDG, carrying rabies virus G (RVG) expression cassettes in various combinations within E1 or E3 genomic regions, were constructed to serve as rabies vaccine candidates. Adv-RVDG mediated greater RVG expression both in vitro and in vivo and induced a more robust and durable humoral immune response than the rabies vaccine strain SAD-L16, Adv-RVG, and Adv-E1-RVG by more effectively activating the dendritic cells (DCs) – follicular helper T (Tfh) cells – germinal centre (GC) / memory B cells (MBCs) – long-lived plasma cells (LLPCs) axis with 100% survival after a lethal RABV challenge in mice during the 24-week study period. Similarly, dogs and cats immunized with Adv-RVDG showed stronger and longer-lasting antibody responses than those vaccinated with a commercial inactivated rabies vaccine and showed good tolerance to Adv-RVDG. In conclusion, our study demonstrated that simultaneous insertion of protective antigens into the E1 and E3 genomic regions of adenovirus vector can significantly enhance the immunogenicity of adenoviral-vectored vaccines, providing a theoretical and practical basis for the subsequent development of multivalent and multi-conjugated vaccines using recombinant adenovirus platform. Meanwhile, our data suggest Adv-RVDG is a safe, efficient, and economical vaccine for mass-coverage immunization.
Cell-Type Specific Connectivity of Whisker-Related Sensory and Motor Cortical Input to Dorsal Striatum

The anterior dorsolateral striatum (DLS) is heavily innervated by convergent excitatory projections from the primary motor (M1) and sensory cortex (S1) and considered an important site of sensorimotor integration. M1 and S1 corticostriatal synapses have functional differences in their connection strength with striatal spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS and, as a result, exert distinct influences on sensory-guided behaviors. In the present study, we tested whether M1 and S1 inputs exhibit differences in the subcellular anatomical distribution of striatal neurons. We injected adeno-associated viral vectors encoding spaghetti monster fluorescent proteins (sm.FPs) into M1 and S1 in male and female mice and used confocal microscopy to generate 3D reconstructions of corticostriatal inputs to single identified SPNs and FSIs obtained through ex vivo patch clamp electrophysiology. We found that M1 and S1 dually innervate SPNs and FSIs; however, there is a consistent bias towards the M1 input in SPNs that is not found in FSIs. In addition, M1 and S1 inputs were distributed similarly across the proximal, medial, and distal regions of SPN and FSI dendrites. Notably, closely localized M1 and S1 clusters of inputs were more prevalent in SPNs than FSIs, suggesting that cortical inputs are integrated through cell-type specific mechanisms. Our results suggest that the stronger functional connectivity from M1 to SPNs compared to S1, as previously observed, is due to a higher quantity of synaptic inputs. Our results have implications for how sensorimotor integration is performed in the striatum through cell-specific differences in corticostriatal connections.
Behind-the-horizon excitations from a single 2d CFT

In this work, we consider the atypical non-equilibrium state found in [1708.06328] which holographically represents a behind-the-horizon excitation in a blackhole spacetime. The special feature of this state is that it looks like an equilibrium state when probed by a class of low-energy operators. First, we retrieve this property using the uniformization mapping in the limit of large central charge, in the process we are able to derive rather than presume approximate thermal physics. Furthermore, in the large-c and high-energy limit we realize these excitations as elements of the commutant algebra of a GNS-representation of the light operator algebra. Instead of analytically continuing a mixed heavy-light Euclidean correlator to a Lorentzian correlator, we identify the Euclidean correlator as a GNS-linear form and interpret the Lorentzian correlator as a vacuum expectation value of representatives of the light operator algebra on the GNS-vacuum.
Metastability of stratified magnetohydrostatic equilibria and their relaxation

Motivated by explosive releases of energy in fusion, space and astrophysical plasmas, we consider the nonlinear convective stability of stratified magnetohydrodynamic (MHD) equilibria in 2D. We demonstrate that, unlike the Schwarzschild criterion in hydrodynamics (``entropy must increase upwards for convective stability''), the so-called modified Schwarzschild criterion for 2D MHD (or for any kind of fluid dynamics with more than one source of pressure) guarantees only linear stability. As a result, in 2D MHD (unlike in hydrodynamics) there exist metastable equilibria that are unstable to nonlinear perturbations despite being stable to linear ones. We show that the minimum-energy configurations attainable by these atmospheres via non-diffusive reorganization can be determined by solving a combinatorial optimization problem. We find inter alia that these minimum-energy states are usually 2D, even when the original metastable equilibrium was 1D. We demonstrate with direct numerical simulations that these 2D states are fairly accurate predictors of the final state reached by laminar relaxation of metastable equilibria at small Reynolds number. To describe relaxation at large Reynolds number, we construct a statistical mechanical theory based on the maximization of Boltzmann's mixing entropy that is analogous to the Lynden-Bell statistical mechanics of self-gravitating systems and collisionless plasmas, and to the Robert-Sommeria-Miller (RSM) theory of 2D vortex turbulence. The minimum-energy states described above are, we show, the low-temperature limit of this theory. We demonstrate that the predictions of the statistical mechanics are in reasonable agreement with direct numerical simulations.
Quality and Quantity of Machine Translation References for Automated Metrics

Automatic machine translation metrics typically rely on human translations to determine the quality of system translations. Common wisdom in the field dictates that the human references should be of very high quality. However, there are no cost-benefit analyses that could be used to guide practitioners who plan to collect references for machine translation evaluation. We find that higher-quality references lead to better metric correlations with humans at the segment-level. Having up to 7 references per segment and taking their average (or maximum) helps all metrics. Interestingly, the references from vendors of different qualities can be mixed together and improve metric success. Higher quality references, however, cost more to create and we frame this as an optimization problem: given a specific budget, what references should be collected to maximize metric success. These findings can be used by evaluators of shared tasks when references need to be created under a certain budget.
Graph Elimination Networks

Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers. Existing research typically attributes this problem to node over-smoothing, where node representations become indistinguishable after multiple rounds of propagation. In this paper, we delve into the neighborhood propagation mechanism of GNNs and discover that the real root cause of GNNs' performance degradation in deep layers lies in ineffective neighborhood feature propagation. This propagation leads to an exponential growth of a node's current representation at every propagation step, making it extremely challenging to capture valuable dependencies between long-distance nodes. To address this issue, we introduce Graph Elimination Networks (GENs), which employ a specific algorithm to eliminate redundancies during neighborhood propagation. We demonstrate that GENs can enhance nodes' perception of distant neighborhoods and extend the depth of network propagation. Extensive experiments show that GENs outperform the state-of-the-art methods on various graph-level and node-level datasets.
Learning-based agricultural management in partially observable environments subject to climate variability

Agricultural management, with a particular focus on fertilization strategies, holds a central role in shaping crop yield, economic profitability, and environmental sustainability. While conventional guidelines offer valuable insights, their efficacy diminishes when confronted with extreme weather conditions, such as heatwaves and droughts. In this study, we introduce an innovative framework that integrates Deep Reinforcement Learning (DRL) with Recurrent Neural Networks (RNNs). Leveraging the Gym-DSSAT simulator, we train an intelligent agent to master optimal nitrogen fertilization management. Through a series of simulation experiments conducted on corn crops in Iowa, we compare Partially Observable Markov Decision Process (POMDP) models with Markov Decision Process (MDP) models. Our research underscores the advantages of utilizing sequential observations in developing more efficient nitrogen input policies. Additionally, we explore the impact of climate variability, particularly during extreme weather events, on agricultural outcomes and management. Our findings demonstrate the adaptability of fertilization policies to varying climate conditions. Notably, a fixed policy exhibits resilience in the face of minor climate fluctuations, leading to commendable corn yields, cost-effectiveness, and environmental conservation. However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events. This research charts a promising course toward adaptable fertilization strategies that can seamlessly align with dynamic climate scenarios, ultimately contributing to the optimization of crop management practices.
Polynomial Fourier decay for fractal measures and their pushforwards

We prove that the pushforwards of a very general class of fractal measures $\mu$ on $\mathbb{R}^d$ under a large family of non-linear maps $F \colon \mathbb{R}^d \to \mathbb{R}$ exhibit polynomial Fourier decay: there exist $C,\eta>0$ such that $|\widehat{F\mu}(\xi)|\leq C|\xi|^{-\eta}$ for all $\xi\neq 0$. Using this, we prove that if $\Phi = \{ \varphi_a \colon [0,1] \to [0,1] \}_{a \in \mathcal{A}}$ is an iterated function system consisting of analytic contractions, and there exists $a \in \mathcal{A}$ such that $\varphi_a$ is not an affine map, then every non-atomic self-conformal measure for $\Phi$ has polynomial Fourier decay; this result was obtained simultaneously by Algom, Rodriguez Hertz, and Wang. We prove applications related to the Fourier uniqueness problem, Fractal Uncertainty Principles, and normal numbers in fractal sets.
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic Unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation is by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gassian mixture model (GMM). We evaluate our approach on well established semantic segmentation datasets and demonstrate it compares favorably against state-of-the-art (SOTA) UDA semantic segmentation methods.
3D Human Pose Perception from Egocentric Stereo Videos

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. We will release UnrealEgo2, UnrealEgo-RW, and trained models on our project page.
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation

Event-based cameras provide accurate and high temporal resolution measurements for performing computer vision tasks in challenging scenarios, such as high-dynamic range environments and fast-motion maneuvers. Despite their advantages, utilizing deep learning for event-based vision encounters a significant obstacle due to the scarcity of annotated data caused by the relatively recent emergence of event-based cameras. To overcome this limitation, leveraging the knowledge available from annotated data obtained with conventional frame-based cameras presents an effective solution based on unsupervised domain adaptation. We propose a new algorithm tailored for adapting a deep neural network trained on annotated frame-based data to generalize well on event-based unannotated data. Our approach incorporates uncorrelated conditioning and self-supervised learning in an adversarial learning scheme to close the gap between the two source and target domains. By applying self-supervised learning, the algorithm learns to align the representations of event-based data with those from frame-based camera data, thereby facilitating knowledge transfer.Furthermore, the inclusion of uncorrelated conditioning ensures that the adapted model effectively distinguishes between event-based and conventional data, enhancing its ability to classify event-based images accurately.Through empirical experimentation and evaluation, we demonstrate that our algorithm surpasses existing approaches designed for the same purpose using two benchmarks. The superior performance of our solution is attributed to its ability to effectively utilize annotated data from frame-based cameras and transfer the acquired knowledge to the event-based vision domain.
Non-prompt $\mathrm{J}/\psi$ production in proton-proton collisions with ALICE

$\mathrm{J}/\psi$ production in high-energy hadronic collisions is sensitive to both perturbative and non-perturbative aspects of quantum chromodynamics (QCD) calculations. The production of a heavy-quark pair is well-described by perturbative QCD, whereas the formation of the bound state involves non-perturbative processes, treated in different ways by various available theoretical models. ALICE can measure inclusive $\mathrm{J}/\psi$ at both forward and midrapidity down to low ${p_{\rm T}}$ and the prompt and non-prompt $\mathrm{J}/\psi$ separation can be performed at midrapidity. The study of the production of non-prompt $\mathrm{J}/\psi$ originating from the decay of beauty hadrons, besides allowing to isolate the prompt $\mathrm{J}/\psi$ cross section from the inclusive $\mathrm{J}/\psi$ cross section, can be used to estimate open beauty-hadron production. Heavy-flavour particle production in pp collisions as a function of charged-particle multiplicity can provide insight into the processes occuring in the collision at the partonic level, as well as the interplay between the hard and soft mechanisms in particle production.
Quantitative Stability of the Pushforward Operation by an Optimal Transport Map

We study the quantitative stability of the mapping that to a measure associates its pushforward measure by a fixed (non-smooth) optimal transport map. We exhibit a tight H\"older-behavior for this operation under minimal assumptions. Our proof essentially relies on a new bound that quantifies the size of the singular sets of a convex and Lipschitz continuous function on a bounded domain.
Efficient Magnon Injection and Detection via the Orbital Rashba Edelstein Effect

Orbital currents and accumulation provide a new avenue to boost spintronic effects in nanodevices. Here we use interconversion effects between charge current and orbital angular momentum to demonstrate a dramatic increase in the magnon spin injection and detection efficiencies in nanodevices consisting of a magnetic insulator contacted by Pt/CuOx electrodes. Moreover, we note distinct variations in efficiency for magnon spin injection and detection, indicating a disparity in the direct and inverse orbital Rashba Edelstein effect efficiencies.
Families of costs with zero and nonnegative MTW tensor in optimal transport

We compute explicitly the MTW tensor (or cross curvature) for the optimal transport problem on $\mathbb{R}^n$ with a cost function of form $\mathsf{c}(x, y) = \mathsf{u}(x^{\mathfrak{t}}y)$, where $\mathsf{u}$ is a scalar function with inverse $\mathsf{s}$, $x^{\ft}y$ is a nondegenerate bilinear pairing of vectors $x, y$ belonging to an open subset of $\mathbb{R}^n$. The condition that the MTW-tensor vanishes on null vectors under the Kim-McCann metric is a fourth-order nonlinear ODE, which could be reduced to a linear ODE of the form $\mathsf{s}^{(2)} - S\mathsf{s}^{(1)} + P\mathsf{s} = 0$ with constant coefficients $P$ and $S$. The resulting inverse functions include {\it Lambert} and {\it generalized inverse hyperbolic\slash trigonometric} functions. The square Euclidean metric and $\log$-type costs are equivalent to instances of these solutions. The optimal map for the family is also explicit. For cost functions of a similar form on a hyperboloid model of the hyperbolic space and unit sphere, we also express this tensor in terms of algebraic expressions in derivatives of $\mathsf{s}$ using the Gauss-Codazzi equation, obtaining new families of strictly regular costs for these manifolds, including new families of {\it power function costs}. We analyze the $\sinh$-type hyperbolic cost, providing examples of $\mathsf{c}$-convex functions and divergence.