Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging Scales in Black Hole Accretion and Feedback: Magnetized Bondi Accretion in 3D GRMHD

Fueling and feedback couple supermassive black holes (SMBHs) to their host galaxies across many orders of magnitude in spatial and temporal scales, making this problem notoriously challenging to simulate. We use a multi-zone computational method based on the general relativistic magnetohydrodynamic (GRMHD) code KHARMA that allows us to span 7 orders of magnitude in spatial scale, to simulate accretion onto a non-spinning SMBH from an external medium with a Bondi radius of R B ≈ 2 × 105 GM •/c 2, where M • is the SMBH mass. For the classic idealized Bondi problem, spherical gas accretion without magnetic fields, our simulation results agree very well with the general relativistic analytic solution. Meanwhile, when the accreting gas is magnetized, the SMBH magnetosphere becomes saturated with a strong magnetic field. The density profile varies as ∼r −1 rather than r −3/2 and the accretion rate Ṁ is consequently suppressed by over 2 orders of magnitude below the Bondi rate ṀB . We find continuous energy feedback from the accretion flow to the external medium at a level of ∼10−2Ṁc2∼5×10−5ṀBc2 . Energy transport across these widely disparate scales occurs via turbulent convection triggered by magnetic field reconnection near the SMBH. Thus, strong magnetic fields that accumulate on horizon scales transform the flow dynamics far from the SMBH and naturally explain observed extremely low accretion rates compared to the Bondi rate, as well as at least part of the energy feedback.
Highly Anisotropic Elastic Properties of Suspended Black Arsenic Nanoribbons

Anisotropy, as an exotic degree of freedom, enables us to discover the emergent two-dimensional (2D) layered nanomaterials with low in-plane symmetry and to explore their outstanding properties and promising applications. 2D black arsenic (b-As) with puckered structure has garnered increasing attention these years owing to its extreme anisotropy with respect to the electrical, thermal, and optical properties. However, the investigation on mechanical properties of 2D b-As is still lacking, despite much effort on theoretical simulations. Herein, we report the highly anisotropic elastic properties of suspended b-As nanoribbons via atomic force microscope-based nanoindentation. It was found that the extracted Young's modulus of b-As nanoribbons exhibits remarkable anisotropy, which approximates to 72.2 +- 5.4 and 44.3 +- 1.4 GPa along zigzag and armchair directions, respectively. The anisotropic ratio reaches up to ~ 1.6. We expect that these results could lay a solid foundation for the potential applications of 2D anisotropic nanomaterials in the next-generation nanomechanics and optoelectronics.
Effective pair correlations of fractional powers of complex grid points

Using a standard definition of fractional powers on the universal cover $\exp:S\to \mathbb{C}^*$ seen as an infinite helicoid embedded in $\mathbb{R}^3$, we study the statistics of pairs from the countable family $\{n^\alpha \, : \, n \in \exp^{-1}(\Lambda) \}$ for every complex grid $\Lambda$ and every real parameter $\alpha \in \, ]0,1[\,$. We prove the convergence of the empirical pair correlations measures towards a rotation invariant measure with explicit density. In particular, with the scaling factor $N\mapsto N^{1-\alpha}$, we prove that there exists an exotic pair correlation function which exhibits a level repulsion phenomenon. For other scaling factors, we prove that either the pair correlations are Poissonian or there is a total loss of mass. In addition, we give an error term for this convergence, with explicit dependence on parameters of the grid $\Lambda$.
Disorder-dependent Li diffusion in $\mathrm{Li_6PS_5Cl}$ investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.
Overlap in soil water sources of savanna woody seedlings and grasses

Seasonal availability of water is a key controlling factor in semi‐arid savanna vegetation structure, function and interactions. Understanding of woody plant interactions with grasses in savannas has long been underpinned by Walter's two‐layered niche differentiation hypothesis that postulates that grasses and trees source water from different depths. The Walter hypothesis persists in the literature, despite contrary evidence and a lack of quantitative empirical tests of the theory. We conducted a greenhouse experiment to determine the following: (1) whether tree seedlings and grasses obtain water from different depths on rocky and sandy soils; (2) whether interspecific competition affected tissue water content of clipped grasses; and (3) the influence of repeated grass clipping on soil moisture. Grass competition significantly reduced tree seedling rooting depth on both rocky and sandy substrates. Trees had significantly longer roots on rocky substrates than on sandy substrates for all combinations (trees only, trees with unclipped grasses and trees with clipped grasses). Results indicated a three‐tier soil moisture depletion pattern, with a top layer (15 cm) exclusively exploited by grasses, an intermediate zone (25–35 cm) utilised by both grass and tree seedling roots and deeper subsoil exclusively tapped by tree seedling roots. Our results are consistent with Walter's hypothesis, but we distinguished between three rather than two layers of tree and grass root interactions in acquiring soil moisture. Copyright © 2012 John Wiley & Sons, Ltd.
Mean field optimization problems: stability results and Lagrangian discretization

We formulate and investigate a mean field optimization (MFO) problem over a set of probability distributions $\mu$ with a prescribed marginal $m$. The cost function depends on an aggregate term, which is the expectation of $\mu$ with respect to a contribution function. This problem is of particular interest in the context of Lagrangian potential mean field games (MFGs) and their discretization. We provide a first-order optimality condition and prove strong duality. We investigate stability properties of the MFO problem with respect to the prescribed marginal, from both primal and dual perspectives. In our stability analysis, we propose a method for recovering an approximate solution to an MFO problem with the help of an approximate solution to an MFO with a different marginal $m$, typically an empirical distribution. We combine this method with the stochastic Frank-Wolfe algorithm of a previous publication of ours to derive a complete resolution method.
Machine learning refinement of in situ images acquired by low electron dose LC-TEM

We have studied a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy. Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and without a solution present. The former images were used as noisy images, and the latter images were used as corresponding ground truth images. The number of pairs of image sets was 1,204, and the image sets included images acquired at several different magnifications and electron doses. The trained model converted a noisy image into a clear image. The time necessary for the conversion was on the order of 10 ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window in the DM software because of the low electron dose, it was visible in a successive refined image generated by our ML model.
Vanishing Gradients in Reinforcement Finetuning of Language Models

Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms. This work identifies a fundamental optimization obstacle in RFT: we prove that the expected gradient for an input vanishes when its reward standard deviation under the model is small, even if the expected reward is far from optimal. Through experiments on an RFT benchmark and controlled environments, as well as a theoretical analysis, we then demonstrate that vanishing gradients due to small reward standard deviation are prevalent and detrimental, leading to extremely slow reward maximization. Lastly, we explore ways to overcome vanishing gradients in RFT. We find the common practice of an initial supervised finetuning (SFT) phase to be the most promising candidate, which sheds light on its importance in an RFT pipeline. Moreover, we show that a relatively small number of SFT optimization steps on as few as 1% of the input samples can suffice, indicating that the initial SFT phase need not be expensive in terms of compute and data labeling efforts. Overall, our results emphasize that being mindful for inputs whose expected gradient vanishes, as measured by the reward standard deviation, is crucial for successful execution of RFT.
Generating Medical Prescriptions with Conditional Transformer

Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement. However, access to real medication prescriptions is often limited due to the sensitive nature of the information expressed. Additionally, manually labelling these instructions for training and fine-tuning Natural Language Processing (NLP) models can be tedious and expensive. We introduce a novel task-specific model architecture, Label-To-Text-Transformer (\textbf{LT3}), tailored to generate synthetic medication prescriptions based on provided labels, such as a vocabulary list of medications and their attributes. LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions. We evaluate LT3's performance by contrasting it with a state-of-the-art Pre-trained Language Model (PLM), T5, analysing the quality and diversity of generated texts. We deploy the generated synthetic data to train the SpacyNER model for the Named Entity Recognition (NER) task over the n2c2-2018 dataset. The experiments show that the model trained on synthetic data can achieve a 96-98\% F1 score at Label Recognition on Drug, Frequency, Route, Strength, and Form. LT3 codes and data will be shared at \url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}
Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models

Generative models have demonstrated revolutionary success in various visual creation tasks, but in the meantime, they have been exposed to the threat of leaking private information of their training data. Several membership inference attacks (MIAs) have been proposed to exhibit the privacy vulnerability of generative models by classifying a query image as a training dataset member or nonmember. However, these attacks suffer from major limitations, such as requiring shadow models and white-box access, and either ignoring or only focusing on the unique property of diffusion models, which block their generalization to multiple generative models. In contrast, we propose the first generalized membership inference attack against a variety of generative models such as generative adversarial networks, [variational] autoencoders, implicit functions, and the emerging diffusion models. We leverage only generated distributions from target generators and auxiliary non-member datasets, therefore regarding target generators as black boxes and agnostic to their architectures or application scenarios. Experiments validate that all the generative models are vulnerable to our attack. For instance, our work achieves attack AUC $>0.99$ against DDPM, DDIM, and FastDPM trained on CIFAR-10 and CelebA. And the attack against VQGAN, LDM (for the text-conditional generation), and LIIF achieves AUC $>0.90. $ As a result, we appeal to our community to be aware of such privacy leakage risks when designing and publishing generative models.
Eigenvector Continuation and Projection-Based Emulators

Eigenvector continuation is a computational method for parametric eigenvalue problems that uses subspace projection with a basis derived from eigenvector snapshots from different parameter sets. It is part of a broader class of subspace-projection techniques called reduced-basis methods. In this colloquium article, we present the development, theory, and applications of eigenvector continuation and projection-based emulators. We introduce the basic concepts, discuss the underlying theory and convergence properties, and present recent applications for quantum systems and future prospects.
Research Africa Network (RAN) RAN Working Paper WP/19/042 Sustaining cultural tourism through higher female participation in Nigeria: the role of corporate social responsibility in oil host communities 1

This paper adds to the gender discourse in sustainable African tourism development from the corporate social responsibility (CSR) perspective. Specifically, we examine the impact of CSR on the development of rural women in cultural tourism. A total of 600 rural women were sampled across the Niger Delta. Results from the use of a logit model indicate a significant relationship between CSR and cultural tourism development in oil host communities in Nigeria. This implies that CSR of a multinational oil company (MOC) is a critical factor for sustaining cultural tourism. The findings suggest increased female participation in General Memorandum of Understanding (GMoU) interventions of MOC and the need to pay close attention to which extent the participation of rural women in the GMoU projects may be limited by cultural and traditional obstacles.
Widespread gas hydrate instability on the upper U.S. Beaufort margin

The most climate‐sensitive methane hydrate deposits occur on upper continental slopes at depths close to the minimum pressure and maximum temperature for gas hydrate stability. At these water depths, small perturbations in intermediate ocean water temperatures can lead to gas hydrate dissociation. The Arctic Ocean has experienced more dramatic warming than lower latitudes, but observational data have not been used to study the interplay between upper slope gas hydrates and warming ocean waters. Here we use (a) legacy seismic data that constrain upper slope gas hydrate distributions on the U.S. Beaufort Sea margin, (b) Alaskan North Slope borehole data and offshore thermal gradients determined from gas hydrate stability zone thickness to infer regional heat flow, and (c) 1088 direct measurements to characterize multidecadal intermediate ocean warming in the U.S. Beaufort Sea. Combining these data with a three‐dimensional thermal model shows that the observed gas hydrate stability zone is too deep by 100 to 250 m. The disparity can be partially attributed to several processes, but the most important is the reequilibration (thinning) of gas hydrates in response to significant (~0.5°C at 2σ certainty) warming of intermediate ocean temperatures over 39 years in a depth range that brackets the upper slope extent of the gas hydrate stability zone. Even in the absence of additional ocean warming, 0.44 to 2.2 Gt of methane could be released from reequilibrating gas hydrates into the sediments underlying an area of ~5–7.5 × 103 km2 on the U.S. Beaufort Sea upper slope during the next century.
European Journal of Combinatorics

We prove discrete Helly-type theorems for pseudohalfplanes, which extend recent results of Jensen, Joshi and Ray about half-planes. Among others we show that given a family of pseudohalf-planes H and a set of points P , if every triple of pseudohalfplanes has a common point in P then there exists a set of at most two points that hits every pseudohalfplane of H . We also prove that if every triple of points of P is contained in a pseudohalfplane of H then there are two pseudohalfplanes of H that cover all points of P . To prove our results we regard pseudohalfplane hypergraphs, define their extremal vertices and show that these behave in many ways as points on the boundary of the convex hull of a set of points. Our methods are purely combinatorial. In addition we determine the maximal possible chromatic number of the regarded hypergraph families.
Shadow economy and its impact on economic growth

This research aims to investigate the correlation between economic growth and the shadow economy in Albania between 1996 and 2019 and their connection to government tax revenue. Accurately measuring the informal economy can aid governments in developing more effective policies that are better targeted. To accurately measure the shadow economy, the autoregressive distributed lag (ARDL) bound test was conducted after verifying the data series’ stationarity. The findings of the Granger causality test revealed a one-way relationship where the shadow economy impacted economic growth in Albania. However, there was no significant correlation between the two variables. However, there was a crucial and adverse association in the long run between the shadow economy and tax revenue (% GDP). The augmented Dickey-Fuller test (ADF) test produced a significant outcome, demonstrating that the shadow economy has a detrimental influence on tax revenue. In conclusion, the study emphasizes the Albanian government’s need to combat the informal sector by enhancing tax collection.
Analysis of Diversity of Food Consumption in Families of Stunting Toddlers

The nutritional status of toddlers is an indicator of the diversity of family food consumption. This research aims to determine the diversity of food consumption in families of stunted toddlers and its relationship to the incidence of stunting in toddlers in Batang Alai Selatan District, Hulu Sungai Tengah Regency, South Kalimantan. This type of associative research uses primary and secondary data sourced from respondents and BPS data from Hulu Sungai Tengah Regency. This research uses analysis of food consumption patterns with the help of 24-hour food recall. The relationship between diversity in family food consumption and the incidence of stunting in children under five using Chi-square analysis. The research results show that the DDP score for food diversity for toddlers in Batang Alai Selatan District reached 77.3. The criteria for food consumption for families of children under five in the research area it still needs to be more diverse because it is still dominated by the grain food group (52.8%) and animal food (38.3%). The Chi-Square test results show that the relationship between food consumption diversity and the incidence of stunting in toddlers has an Asymp value. Sig. (2-sided) 0.000 < 0.05, the calculated Chi-Square value is 20.17 > Chi-square table 3.841, so it can be concluded that there is a significant relationship between the diversity of family food consumption and the incidence of stunting in toddlers
Predicting soil organic carbon percentage from loss-on-ignition using Bayesian Model Averaging.

A shortage of data for percentage of organic carbon (C%) makes calculation of soil profile carbon storage difficult. Loss on ignition (LOI) data, which are cheap to obtain and often readily available, can be used to estimate organic C%. This paper simultaneously considers several predictors of organic C%: LOI, parent material, drainage status, type of soil horizon, clay content, and pH. In order to model appropriately the existence of multiple hypotheses and the consequent model uncertainty, a Bayesian Model Averaging (BMA) approach was used. BMA considers all models that result from all possible combinations of explanatory variables. Based on a BMA approach and Scottish Soil Survey data, it was found that the most important factors to predict organic C% were LOI, clay content, a dummy for Countesswells Association (till derived from granite), and a dummy for B horizon soils. The validation analysis showed that prediction accuracy for organic C% was better with the BMA approach than with an ordinary least-squares approach that includes no other predictors apart from LOI (i.e. 22% reduction in horizons A, Ap, and C).
Investigation of Root Canal Anatomy of Mandibular Permanent canine in Egyptian Subpopulation: A Cone-Beam Computed Tomography Study

Introduction: The aim of this study was to investigate the root and root canal anatomy of mandibular canine in Egyptian subpopulation. Methods: 1000 CBCT scans of completely erupted mandibular permanent canines were acquired from Egyptian patients, The number and percentages of roots, canals, and types of canal morphology were then examined in teeth utilizing Vertucci’s classification . Results: the percentage for mandibular canine with two roots was very small (1.2%), the percentages for a two root canals mandibular canine was 21%, Vertucci type V recorded the highest percentage (7.8%), followed by type II (6.6%), then type IV(4.9%) and the least percentage configuration was type III (1.7%). Conclusion: Different studies on different population and even on the same populations yielded variable results, therefore the existence of all possibilities for extra roots and extra canals should be always kept in mind. CBCT scan is a must in any case with suspected unusual anatomy, to avoid any mishaps. Clinical implications: