Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Solar System Basalt

Meteorites can provide important insights into the early universe and the properties of other planets or asteroids. And they continue to be good for surprises. In his Perspective, Palme highlights the report by Yamaguchi et al . , who have analyzed a meteorite from Northwest Africa (NWA011). This meteorite clearly originated from a partially molten object, but does not match the properties of known basalts from the inner solar system. Of the inner planets, only Mercury cannot be ruled out as a possible source of this meteorite.
Paleoenvironmental events during the last 13,000 years in the central Red Sea as recorded by pteropoda

A high-resolution record was obtained by investigating a sedimentary sequence from two cores taken in the central Red Sea (R/V Meteor cruise 5, leg 2). The numerical variations between nonmigratory and migratory pteropods and their stable isotopic record were studied together with variations in the carbonate and total organic carbon content in order to estimate changes in the structure of the water column during the last 13 kyr. The results indicate the existence of a highly stratified water column during deglaciation and early Holocene (13–8.5 ka). During this period the depth of the mixed layer varied between less than 50–100 m at most, and productivity was markedly reduced in comparison to the recent situation. The intermediate and deep water were constantly highly depleted in oxygen. A pronounced Younger Dryas event was recognized at 10.4 ka which coincides with an arid phase on the border land. The oxygenation of the intermediate water improved remarkably after 7.5 ka and peaked during the period between 4.6 and 2.0 ka. During this period the mixed layer reached its present depth and the productivity which was maximal during middle Holocene attained present level during the last 4.6 kyr.
Physical properties of Be-based fluoroperovskite compounds XBeF3 (X = K, Rb): a first-principles study

In this study, we used the ab-initio computational tools as implemented in the CASTEP code to explore the effects of pressure on the structural, elastic, electronic, thermodynamic and optical properties of the fluoroperovskite compounds XBeF3 (X = K, Rb) based on Being. Exchange–correlation interactions were modeled using the GGA-PBEsol functional. The ground state of the title materials was characterized by calculating the optimized lattice parameter, the bulk modulus B and its pressure derivative, and the Goldsmith tolerance factor. These materials exhibit structural stability in the cubic structure even when subjected to significant pressure levels, extending up to 18 GPa. The analysis of numerical assessments of single-crystal elastic constants (Cij ), polycrystalline elastic moduli, namely shear modulus (G), Young’s modulus and Poisson’s ratio, as well as the anisotropy factor (A), highlights the mechanical stability, elastic anisotropy and ductility of considered the compounds. The thermodynamic properties of these materials were studied through the Debye quasi-harmonic model. Analysis of energy band structures and density of states spectra shows that XBeF3 (X = K, Rb) is insulating in nature, with band gaps of 7.99 and 7.26 eV, respectively. Additionally, we calculated the linear optical spectra, including dielectric function, absorption coefficient, refractive index, optical reflectivity, and energy loss function. Based on the results obtained, these materials could be used in various optoelectronic devices operating in the UV spectrum and in energy storage devices.
From the sideline: Tissue‐specific nucleoporin function in health and disease, an update

The subcellular compartmentalisation of eukaryotic cells requires selective exchange between the cytoplasm and the nucleus. Intact nucleocytoplasmic transport is vital for normal cell function and mutations in the executing machinery have been causally linked to human disease. Central players in nucleocytoplasmic exchange are nuclear pore complexes (NPCs), which are built from ~30 distinct proteins collectively termed nucleoporins. Aberrant nucleoporin expression was detected in human cancers and autoimmune diseases since quite some time, while it was through the increasing use of next generation sequencing that mutations in nucleoporin genes associated with mainly rare hereditary diseases were revealed. The number of newly identified mutations is steadily increasing, as is the number of diseases. Mutational hotspots have emerged: mutations in the scaffold nucleoporins seemingly affect primarily inner organs, such as heart, kidney, and ovaries, whereas genetic alterations in peripheral, cytoplasmic nucleoporins affect primarily the central nervous system and development. In this review, we summarise latest insights on altered nucleoporin function in the context of human hereditary disorders, with a focus on those where mechanistic insights are beginning to emerge.
Effects of COVID-19 mRNA vaccination on HIV viremia and reservoir size

Objective: The immunogenic nature of COVID-19 mRNA vaccines led to some initial concern that these could stimulate the HIV reservoir. We analyzed changes in plasma HIV loads (pVL) and reservoir size following COVID-19 mRNA vaccination in 62 people with HIV (PWH) receiving antiretroviral therapy (ART), and analyzed province-wide trends in pVL before and after the mass vaccination campaign. Design: Longitudinal observational cohort and province-wide analysis. Methods: 62 participants were sampled pre-vaccination, and one month after their first and second COVID-19 immunizations. Vaccine-induced anti-SARS-CoV-2-Spike antibodies in serum were measured using the Roche Elecsys Anti-S assay. HIV reservoirs were quantified using the Intact Proviral DNA Assay; pVL were measured using the cobas 6800 (LLOQ:20 copies/mL). The province-wide analysis included all 290,401 pVL performed in British Columbia, Canada between 2012–2022. Results: Pre-vaccination, the median intact reservoir size was 77 (IQR:20–204) HIV copies/million CD4+ T-cells, compared to 74 (IQR:27–212) and 65 (IQR:22–174) post-first and -second dose, respectively (all comparisons p>0.07). Pre-vaccination, 82% of participants had pVL<20 copies/mL (max:110 copies/mL), compared to 79% post-first dose (max:183 copies/mL) and 85% post-second dose (max:79 copies/mL) (p>0.4). The magnitude of the vaccine-elicited anti-SARS-CoV-2-Spike antibody response did not correlate with changes in reservoir size nor detectable pVL frequency (p>0.6). We found no evidence linking the COVID-19 mass vaccination campaign to population-level increases in detectable pVL frequency among all PWH in the province, nor among those who maintained pVL suppression on ART. Conclusion: We found no evidence that COVID-19 mRNA vaccines induced changes in HIV reservoir size nor plasma viremia.
Co‐occurring conditions in Down syndrome: Findings from a clinical database

Individuals with Down syndrome (DS) experience a range of medical and neurodevelopmental conditions, necessitating systematic study of their occurrence and impact on neurodevelopmental outcomes. We describe the prevalence and relationships of medical, neurodevelopmental (ND), and mental health (MH) conditions in children with DS. We created a prospective clinical database of individuals with DS, integrated into the workflow of a specialty Down Syndrome Program at a specialty pediatric referral hospital. Conditions were collected through caregiver‐ and clinician report at clinical visits (N = 599). We calculated frequencies of medical, ND, and MH conditions and then assessed the relationship between medical, ND, and MH conditions using frequencies and comparative statistics. The most frequent co‐occurring conditions were vision (72.5%), ear/hearing (71.0%), gastrointestinal (61.3%), respiratory (45.6%), and feeding (33.6%) problems, with variation in frequency by age. ND and MH conditions were reported in one quarter, most commonly autism spectrum disorder and attention‐deficit/hyperactivity disorder. Those with ND and MH conditions had greater frequency of medical conditions, with highest rates of vision, ear/hearing, and gastrointestinal issues, and CHD. Systematically collected clinical data in a large cohort of children with DS reveals high prevalence of several co‐occurring medical, ND, and MH conditions. Clinical care requires an understanding of the complex relationship between medical conditions and neurodevelopment.
The cross-stream potential vorticity front and its role in meander-induced exchange in the Gulf Stream

Abstract The potential vorticity (PV) front observed in the upper thermocline at the northern edge of the Gulf Stream has been thought of as an inhibitor to lateral motion of parcels on isopycnals. The role of the PV front in relation to lateral motion and cross-stream exchange was investigated by monitoring the evolving PV field in the vicinity of Lagrangian parcels at the northern edge of the stream. The observational study involved shipboard acoustic Doppler current profiler (ADCP) and conductivity–temperature–depth surveys to sample the velocity and density fields around water parcels as they flowed through meanders. The observations reveal that the PV field in the vicinity of parcels moves laterally across the stream and evolves as parcels negotiate meander crests and troughs, allowing parcels to move across the stream without changing their Lagrangian PV. This lateral motion of the PV front suggests that it is more a response to than an inhibitor of the lateral motion of parcels in the meanders. The.
Inclusion in Virtual Reality Technology: A Scoping Review

Despite the significant growth in virtual reality applications and research, the notion of inclusion in virtual reality is not well studied. Inclusion refers to the active involvement of different groups of people in the adoption, use, design, and development of VR technology and applications. In this review, we provide a scoping analysis of existing virtual reality research literature about inclusion. We categorize the literature based on target group into ability, gender, and age, followed by those that study community-based design of VR experiences. In the latter group, we focus mainly on Indigenous Peoples as a clearer and more important example. We also briefly review the approaches to model and consider the role of users in technology adoption and design as a background for inclusion studies. We identify a series of generic barriers and research gaps and some specific ones for each group, resulting in suggested directions for future research.
Non-Equilibrium Pathways for Excitation of Bulk and Surface Phonons through Anharmonic Coupling

Upon impulsive optical excitation of solid-state materials, the non-equilibrium flow of energy from the excited electronic system to the lattice degrees of freedom typically happens in a few picoseconds. Here we identified the surface of thin Bi films grown on Si(001) as an additional subsystem which is excited much slower on a 100 ps timescale that is caused by decoupling due to mismatched phonon dispersions relations of bulk and surface. Anharmonic coupling among the phonon systems provides pathways for excitations which exhibits a 1/T-dependence causing a speed-up of surface excitation at higher temperatures. A quantitative justification is provided by phonon Umklapp processes from lattice thermal conductivity of the Bi bulk. Three-temperature model simulations reveal a pronounced non-equilibrium situation up to nanoseconds: initially, the surface is colder than the bulk, that situation is then inverted during cooling and the surface feeds energy back into the bulk phonon system.
Fractal Landscapes in Policy Optimization

Policy gradient lies at the core of deep reinforcement learning (RL) in continuous domains. Despite much success, it is often observed in practice that RL training with policy gradient can fail for many reasons, even on standard control problems with known solutions. We propose a framework for understanding one inherent limitation of the policy gradient approach: the optimization landscape in the policy space can be extremely non-smooth or fractal for certain classes of MDPs, such that there does not exist gradient to be estimated in the first place. We draw on techniques from chaos theory and non-smooth analysis, and analyze the maximal Lyapunov exponents and H\"older exponents of the policy optimization objectives. Moreover, we develop a practical method that can estimate the local smoothness of objective function from samples to identify when the training process has encountered fractal landscapes. We show experiments to illustrate how some failure cases of policy optimization can be explained by such fractal landscapes.
Identifiable Latent Polynomial Causal Models Through the Lens of Change

Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability. A recent breakthrough explores identifiability by leveraging the change of causal influences among latent causal variables across multiple environments \citep{liu2022identifying}. However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models. In this paper, we extend the scope of latent causal models to involve nonlinear causal relationships, represented by polynomial models, and general noise distributions conforming to the exponential family. Additionally, we investigate the necessity of imposing changes on all causal parameters and present partial identifiability results when part of them remains unchanged. Further, we propose a novel empirical estimation method, grounded in our theoretical finding, that enables learning consistent latent causal representations. Our experimental results, obtained from both synthetic and real-world data, validate our theoretical contributions concerning identifiability and consistency.
Causal progress with imperfect placebo treatments and outcomes

In the quest to make defensible causal claims from observational data, it is sometimes possible to leverage information from"placebo treatments"and"placebo outcomes"(or"negative outcome controls"). Existing approaches employing such information focus largely on point identification and assume (i)"perfect placebos", meaning placebo treatments have precisely zero effect on the outcome and the real treatment has precisely zero effect on a placebo outcome; and (ii)"equiconfounding", meaning that the treatment-outcome relationship where one is a placebo suffers the same amount of confounding as does the real treatment-outcome relationship, on some scale. We instead consider an omitted variable bias framework, in which users can postulate non-zero effects of placebo treatment on real outcomes or of real treatments on placebo outcomes, and the relative strengths of confounding suffered by a placebo treatment/outcome compared to the true treatment-outcome relationship. Once postulated, these assumptions identify or bound the linear estimates of treatment effects. While applicable in many settings, one ubiquitous use-case for this approach is to employ pre-treatment outcomes as (perfect) placebo outcomes. In this setting, the parallel trends assumption of difference-in-difference is in fact a strict equiconfounding assumption on a particular scale, which can be relaxed in our framework. Finally, we demonstrate the use of our framework with two applications, employing an R package that implements these approaches.
Weighted Distance Nearest Neighbor Condensing

The problem of nearest neighbor condensing has enjoyed a long history of study, both in its theoretical and practical aspects. In this paper, we introduce the problem of weighted distance nearest neighbor condensing, where one assigns weights to each point of the condensed set, and then new points are labeled based on their weighted distance nearest neighbor in the condensed set. We study the theoretical properties of this new model, and show that it can produce dramatically better condensing than the standard nearest neighbor rule, yet is characterized by generalization bounds almost identical to the latter. We then suggest a condensing heuristic for our new problem. We demonstrate Bayes consistency for this heuristic, and also show promising empirical results.
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning

Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL methods face is the confirmation bias, which occurs when the model is overfitted to the small labeled training dataset and produces overconfident, incorrect predictions. To address this issue, we propose SequenceMatch, an efficient SSL method that utilizes multiple data augmentations. The key element of SequenceMatch is the inclusion of a medium augmentation for unlabeled data. By taking advantage of different augmentations and the consistency constraints between each pair of augmented examples, SequenceMatch helps reduce the divergence between the prediction distribution of the model for weakly and strongly augmented examples. In addition, SequenceMatch defines two different consistency constraints for high and low-confidence predictions. As a result, SequenceMatch is more data-efficient than ReMixMatch, and more time-efficient than both ReMixMatch ($\times4$) and CoMatch ($\times2$) while having higher accuracy. Despite its simplicity, SequenceMatch consistently outperforms prior methods on standard benchmarks, such as CIFAR-10/100, SVHN, and STL-10. It also surpasses prior state-of-the-art methods by a large margin on large-scale datasets such as ImageNet, with a 38.46\% error rate. Code is available at https://github.com/beandkay/SequenceMatch.
Pre-training Music Classification Models via Music Source Separation

In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks. To this end, we first pre-train U-Net networks under various music source separation objectives, such as the isolation of vocal or instrumental sources from a musical piece; afterwards, we attach a convolutional tail network to the pre-trained U-Net and jointly finetune the whole network. The features learned by the separation network are also propagated to the tail network through skip connections. Experimental results in two widely used and publicly available datasets indicate that pre-training the U-Nets with a music source separation objective can improve performance compared to both training the whole network from scratch and using the tail network as a standalone in two music classification tasks: music auto-tagging, when vocal separation is used, and music genre classification for the case of multi-source separation.
Finetuning Offline World Models in the Real World

Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to learn skills. Recently, offline RL has been proposed as a framework for training RL policies on pre-existing datasets without any online interaction. However, constraining an algorithm to a fixed dataset induces a state-action distribution shift between training and inference, and limits its applicability to new tasks. In this work, we seek to get the best of both worlds: we consider the problem of pretraining a world model with offline data collected on a real robot, and then finetuning the model on online data collected by planning with the learned model. To mitigate extrapolation errors during online interaction, we propose to regularize the planner at test-time by balancing estimated returns and (epistemic) model uncertainty. We evaluate our method on a variety of visuo-motor control tasks in simulation and on a real robot, and find that our method enables few-shot finetuning to seen and unseen tasks even when offline data is limited. Videos, code, and data are available at https://yunhaifeng.com/FOWM .
R\'enyi entanglement asymmetry in 1+1-dimensional conformal field theories

In this paper, we consider the R\'enyi entanglement asymmetry of excited states in the 1+1 dimensional free compact boson conformal field theory (CFT) at equilibrium. We obtain a universal CFT expression written by correlation functions for the charged moments via the replica trick. We provide detailed analytic computations of the second R\'enyi entanglement asymmetry in the free compact boson CFT for excited states $\Psi=V_{\beta}+V_{-\beta}$ and $\Phi=V_{\beta}+J$ with $V_{\beta}$ and $J=i\partial\phi$ being the vertex operator and current operator respectively. We make numerical tests of the universal CFT computations using the XX spin chain model. Taking the non-Hermite fake RDMs into consideration, we propose an effective way to test them numerically, which can be applied to other excited states. The CFT predictions are in perfect agreement with the exact numerical calculations.
A comparative study of variational autoencoders, normalizing flows, and score-based diffusion models for electrical impedance tomography

Abstract Electrical Impedance Tomography (EIT) is a widely employed imaging technique in industrial inspection, geophysical prospecting, and medical imaging. However, the inherent nonlinearity and ill-posedness of EIT image reconstruction present challenges for classical regularization techniques, such as the critical selection of regularization terms and the lack of prior knowledge. Deep generative models (DGMs) have been shown to play a crucial role in learning implicit regularizers and prior knowledge. This study aims to investigate the potential of three DGMs – variational autoencoder networks, normalizing flow, and score-based diffusion model – to learn implicit regularizers in learning-based EIT imaging. We first introduce background information on EIT imaging and its inverse problem formulation. Next, we propose three algorithms for performing EIT inverse problems based on corresponding DGMs. Finally, we present numerical and visual experiments, which reveal that (1) no single method consistently outperforms the others across all settings, and (2) when reconstructing an object with two anomalies using a well-trained model based on a training dataset containing four anomalies, the conditional normalizing flow (CNF) model exhibits the best generalization in low-level noise, while the conditional score-based diffusion model (CSD*) demonstrates the best generalization in high-level noise settings. We hope our preliminary efforts will encourage other researchers to assess their DGMs in EIT and other nonlinear inverse problems.