Miracle-dz/UniFAR-FTUs huggingface dataset:
Uplift of Central Mongolia Recorded in Vesicular Basalts

Epeirogenic histories of highland areas have confounded earth scientists for decades, as there are few sedimentary records of paleoelevation in eroding highlands. For example, mechanisms that have led to the high elevations of the Hangay Mountains in central Mongolia are not clear, nor is it well understood how the epeirogenic history of central Mongolia is connected to that of a broader region of high elevation that extends hundreds of kilometers to the north, east, and west. However, preserved basaltic lava flows record paleoelevation in the size distributions of vesicles at the tops and bottoms of flow units. As an initial step toward better understanding the tectonics of this part of Asia, we collected and analyzed samples from several basaltic lava flows from throughout the Hangay Mountains to use as a paleoaltimeter on the basis of lava flow vesicularity. Samples were dated and scanned with x-ray tomography to provide quantitative information regarding their internal vesicle size distributions. This yielded the pressure difference between the top and bottom of each flow for paleoelevation calculation. Results suggest that the Hangay Mountains experienced uplift of more than 1 km sometime during the past 9 m.yr. The magnitude of uplift of the Hangay, in addition to the composition of its lavas, the geomorphology of the region, its drainage pattern history, and other proxies, bears on possible mechanisms for uplift of this part of central Asia.
What works for whom and why? Treatment effects and their moderators among forcibly displaced people receiving psychological and psychosocial interventions: study protocol for an individual patient data meta-analysis

Introduction Forcibly displaced people (FDP) have a high risk of developing mental disorders such as post-traumatic stress (PTS) disorder. Providing adequate mental healthcare for FDP is crucial but despite overall efficacy of many existing interventions, a large proportion of FDP does not benefit from treatment, highlighting the necessity of further investigating factors contributing to individual differences in treatment outcome. Yet, the few studies that have explored moderators of treatment effects are often insufficiently powered. Therefore, the present Individual Patient Data meta-analysis (IPD-MA) will investigate treatment effects and their moderators—variables related to beneficiaries, providers, intervention and study characteristics in relation to PTS outcomes. Methods and analysis A systematic literature search will be conducted from database inception in the databases PsycINFO, Cochrane, Embase, PTSDpubs and Web of Science. Only studies published in English, German, French, Spanish, Portuguese, and Dutch will be considered. Retrieved records will be screened for eligibility. Randomised controlled trials on adult FDP receiving psychological and psychosocial interventions aimed at alleviating symptoms such as PTS compared with a control condition without intervention will be included in this IPD-MA. Subsequently, authors of eligible studies will be contacted to request individual patient data (IPD). All datasets obtained will be synthesised into one large dataset which will be analysed using a one-stage approach by conducting mixed-effects linear regression models (ie, primary analysis). Additionally, aggregate data meta-analyes will be run using a two-stage approach by conducting multivariate regression models including all IPD (transformed) and available meta-data from study reports (ie, secondary analysis). PTS will serve as primary outcome measure, while mental health outcomes other than PTS, attendance, attrition, treatment non-response and adverse outcomes will be examined as secondary outcomes. Ethics and dissemination This IPD-MA does not require ethical approval. The results will be published in international peer-reviewed journals. PROSPERO registration number CRD42022299510.
Increase in winter haze over eastern China in recent decades: Roles of variations in meteorological parameters and anthropogenic emissions

The increase in winter haze over eastern China in recent decades due to variations in meteorological parameters and anthropogenic emissions was quantified using observed atmospheric visibility from the National Climatic Data Center Global Summary of Day database for 1980–2014 and simulated PM2.5 concentrations for 1985–2005 from the Goddard Earth‐Observing System (GEOS) chemical transport model (GEOS‐Chem). Observed winter haze days averaged over eastern China (105–122.5°E, 20–45°N) increased from 21 days in 1980 to 42 days in 2014 and from 22 to 30 days between 1985 and 2005. The GEOS‐Chem model captured the increasing trend of winter PM2.5 concentrations for 1985–2005, with concentrations averaged over eastern China increasing from 16.1 µg m−3 in 1985 to 38.4 µg m−3 in 2005. Considering variations in both anthropogenic emissions and meteorological parameters, the model simulated an increase in winter surface‐layer PM2.5 concentrations of 10.5 (±6.2) µg m−3 decade−1 over eastern China. The increasing trend was only 1.8 (±1.5) µg m−3 decade−1 when variations in meteorological parameters alone were considered. Among the meteorological parameters, the weakening of winds by −0.09 m s−1 decade−1 over 1985–2005 was found to be the dominant factor leading to the decadal increase in winter aerosol concentrations and haze days over eastern China during recent decades.
New species of Camptochlamys and Chlamys (Mollusca: Bivalvia: Pectinidae) from near the Cretaceous/Tertiary boundary at Ocean Point, North Slope, Alaska

Two new species of pectinid bivalves, Camptochlamys alaskensis and Chlamys aquilonia, are present in shallow-marine deposits near Ocean Point, northeastern Alaska, at about 70°N latitude. The precise age of these deposits is debatable, but paleontological and isotopic age estimates indicate placement near the Cretaceous–Tertiary boundary. The occurrence of Camptochlamys extends the chronostratigraphic and geographic range of this genus, previously unknown from any strata above the uppermost Jurassic (Tithonian) of Europe and unknown from any strata in North America. In contrast, the new Chlamys species represents an evolutionary advance beyond its nearest relative in the Maastrichtian of northern Europe and resembles Cenozoic members of its clade in certain features of shell microstructure. These new species are thus a microcosm of the debate regarding the age of these deposits and strengthen the hypothesis that the Ocean Point fauna lived in an Arctic Ocean basin that was isolated from the world ocean.
Ceragenin-coated endotracheal tubes for the reduction of ventilator-associated pneumonia: a prospective, longitudinal, cross-over, interrupted time, implementation study protocol (CEASE VAP study)

Background Critically ill patients are at high risk of acquiring ventilator-associated pneumonia (VAP), which occurs in approximately 20% of mechanically ventilated patients. VAP results either from aspiration of pathogen-contaminated oropharyngeal secretions or contaminated biofilms that form on endotracheal tubes (ETTs) after intubation. VAP results in increased duration of mechanical ventilation, increased intensive care unit and hospital length of stay, increased risk of death and increased healthcare costs. Because of its impact on patient outcomes and the healthcare system, VAP is regarded as an important patient safety issue and there is an urgent need for better evidence on the efficacy of prevention strategies. Modified ETTs that reduce aspiration of oropharyngeal secretions with subglottic secretion drainage or reduce the occurrence of biofilm with a coating of ceragenins (CSAs) are available for clinical use in Canada. In this implementation study, we will evaluate the efficacy of these two types of Health Canada-licensed ETTs on the occurrence of VAP, and impact on patient-centred outcomes. Methods In this ongoing, pragmatic, prospective, longitudinal, interrupted time, cross-over implementation study, we will compare the efficacy of a CSA-coated ETT (CeraShield N8 Pharma) with an ETT with subglottic secretion drainage (Taper Guard, Covidien). The study periods consist of four alternating time periods of 11 or 12 weeks or a total of 23 weeks for each ETT. All patients intubated with the study ETT in each time period will be included in an intention-to-treat analysis. Outcomes will include VAP incidence, mortality and health services utilisation including antibiotic use and length of stay. Ethics and dissemination This study has been approved by the Health Sciences Research Ethics Board at Queen’s University. The results of this study will be actively disseminated through manuscript publication and conference presentations. Trial registration number NCT05761613.
A Dynamical Model of Neural Scaling Laws

On a variety of tasks, the performance of neural networks predictably improves with training time, dataset size and model size across many orders of magnitude. This phenomenon is known as a neural scaling law. Of fundamental importance is the compute-optimal scaling law, which reports the performance as a function of units of compute when choosing model sizes optimally. We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. This reproduces many observations about neural scaling laws. First, our model makes a prediction about why the scaling of performance with training time and with model size have different power law exponents. Consequently, the theory predicts an asymmetric compute-optimal scaling rule where the number of training steps are increased faster than model parameters, consistent with recent empirical observations. Second, it has been observed that early in training, networks converge to their infinite-width dynamics at a rate $1/\textit{width}$ but at late time exhibit a rate $\textit{width}^{-c}$, where $c$ depends on the structure of the architecture and task. We show that our model exhibits this behavior. Lastly, our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning

Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitations that prevent their broader adoption in many real-world systems, which often require a higher bar for trustworthiness and usability. Since FMs are trained using loss functions aimed at reconstructing the training corpus in a self-supervised manner, there is no guarantee that the model's output aligns with users' preferences for a specific task at hand. In this survey paper, we propose a conceptual framework that encapsulates different modes by which agents could interact with FMs and guide them suitably for a set of tasks, particularly through knowledge augmentation and reasoning. Our framework elucidates agent role categories such as updating the underlying FM, assisting with prompting the FM, and evaluating the FM output. We also categorize several state-of-the-art approaches into agent interaction protocols, highlighting the nature and extent of involvement of the various agent roles. The proposed framework provides guidance for future directions to further realize the power of FMs in practical AI systems.
Analysis of weak Galerkin mixed FEM based on the velocity-pseudostress formulation for Navier-Stokes equation on polygonal meshes

The present article introduces, mathematically analyzes, and numerically validates a new weak Galerkin (WG) mixed-FEM based on Banach spaces for the stationary Navier--Stokes equation in pseudostress-velocity formulation. More precisely, a modified pseudostress tensor, called $ \boldsymbol{\sigma} $, depending on the pressure, and the diffusive and convective terms has been introduced in the proposed technique, and a dual-mixed variational formulation has been derived where the aforementioned pseudostress tensor and the velocity, are the main unknowns of the system, whereas the pressure is computed via a post-processing formula. Thus, it is sufficient to provide a WG space for the tensor variable and a space of piecewise polynomial vectors of total degree at most 'k' for the velocity. Moreover, in order to define the weak discrete bilinear form, whose continuous version involves the classical divergence operator, the weak divergence operator as a well-known alternative for the classical divergence operator in a suitable discrete subspace is proposed. The well-posedness of the numerical solution is proven using a fixed-point approach and the discrete versions of the Babu\v{s}ka-Brezzi theory and the Banach-Ne\v{c}as-Babu\v{s}ka theorem. Additionally, an a priori error estimate is derived for the proposed method. Finally, several numerical results illustrating the method's good performance and confirming the theoretical rates of convergence are presented.
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving

This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system. The combination directly addresses the challenges of adaptability and safety in autonomous driving. Motion planning algorithms are essential for navigating dynamic and complex scenarios. Traditional methods, however, lack the flexibility required for unpredictable environments, whereas machine learning techniques, particularly reinforcement learning (RL), offer adaptability but suffer from instability and a lack of explainability. Our unique solution synergizes the predictability and stability of traditional motion planning algorithms with the dynamic adaptability of RL, resulting in a system that efficiently manages complex situations and adapts to changing environmental conditions. Evaluation of our integrated approach shows a significant reduction in collisions, improved risk management, and improved goal success rates across multiple scenarios. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-RL.
Moving Aggregate Modified Autoregressive Copula-Based Time Series Models (MAGMAR-Copulas) Without Markov Restriction

Copula-based time series models implicitly assume a finite Markov order. In reality a time series may not follow the Markov property. We modify the copula-based time series models by introducing a moving aggregate (MAG) part into the model updating equation. The functional form of the MAG-part is given as the inverse of a conditional copula. The resulting MAG-modified Autoregressive Copula-Based Time Series model (MAGMAR-Copula) is discussed in detail and distributional properties are derived in a D-vine framework. The model nests the classical ARMA model as well as the copula-based time series model. The modeling performance is compared with the model from \cite{mcneil2022time} modeling US inflation. Our model is competitive in terms of information criteria. It is a generalization of ARMA and also copula-based time series models and is in spirit similar to other moving average time series models such as ARMA and GARCH.
2AFC Prompting of Large Multimodal Models for Image Quality Assessment

While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification

Verifying biomedical claims fails if no evidence can be discovered. In these cases, the fact-checking verdict remains unknown and the claim is unverifiable. To improve this situation, we have to understand if there are any claim properties that impact its verifiability. In this work we assume that entities and relations define the core variables in a biomedical claim’s anatomy and analyze if their properties help us to differentiate verifiable from unverifiable claims. In a study with trained annotation experts we prompt them to find evidence for biomedical claims, and observe how they refine search queries for their evidence search. This leads to the first corpus for scientific fact verification annotated with subject–relation–object triplets, evidence documents, and fact-checking verdicts (the BEAR-FACT corpus). We find (1) that discovering evidence for negated claims (e.g., X–does-not-cause–Y) is particularly challenging. Further, we see that annotators process queries mostly by adding constraints to the search and by normalizing entities to canonical names. (2) We compare our in-house annotations with a small crowdsourcing setting where we employ both medical experts and laypeople. We find that domain expertise does not have a substantial effect on the reliability of annotations. Finally, (3), we demonstrate that it is possible to reliably estimate the success of evidence retrieval purely from the claim text (.82F_1), whereas identifying unverifiable claims proves more challenging (.27F_1)
MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data

X-ray computed tomography (CT) based on photon counting detectors (PCD) extends standard CT by counting detected photons in multiple energy bins. PCD data can be used to increase the contrast-to-noise ratio (CNR), increase spatial resolution, reduce radiation dose, reduce injected contrast dose, and compute a material decomposition using a specified set of basis materials. Current commercial and prototype clinical photon counting CT systems utilize PCD-CT reconstruction methods that either reconstruct from each spectral bin separately, or first create an estimate of a material sinogram using a specified set of basis materials and then reconstruct from these material sinograms. However, existing methods are not able to utilize simultaneously and in a modular fashion both the measured spectral information and advanced prior models in order to produce a material decomposition. We describe an efficient, modular framework for PCD-based CT reconstruction and material decomposition using on Multi-Agent Consensus Equilibrium (MACE). Our method employs a detector proximal map or agent that uses PCD measurements to update an estimate of the pathlength sinogram. We also create a prior agent in the form of a sinogram denoiser that enforces both physical and empirical knowledge about the material-decomposed sinogram. The sinogram reconstruction is computed using the MACE algorithm, which finds an equilibrium solution between the two agents, and the final image is reconstructed from the estimated sinogram. Importantly, the modularity of our method allows the two agents to be designed, implemented, and optimized independently. Our results on simulated data show a substantial (450%) CNR boost vs conventional maximum likelihood reconstruction when applied to a phantom used to evaluate low contrast detectability.
Interpretation of Intracardiac Electrograms Through Textual Representations

Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography. For serious cases of AFib, catheter ablations are performed to collect intracardiac electrograms (EGMs). EGMs offer intricately detailed and localized electrical activity of the heart and are an ideal modality for interpretable cardiac studies. Recent advancements in artificial intelligence (AI) has allowed some works to utilize deep learning frameworks to interpret EGMs during AFib. Additionally, language models (LMs) have shown exceptional performance in being able to generalize to unseen domains, especially in healthcare. In this study, we are the first to leverage pretrained LMs for finetuning of EGM interpolation and AFib classification via masked language modeling. We formulate the EGM as a textual sequence and present competitive performances on AFib classification compared against other representations. Lastly, we provide a comprehensive interpretability study to provide a multi-perspective intuition of the model's behavior, which could greatly benefit the clinical use.
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL framework, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise

Despite the success of the Adam optimizer in practice, the theoretical understanding of its algorithmic components still remains limited. In particular, most existing analyses of Adam show the convergence rate that can be simply achieved by non-adative algorithms like SGD. In this work, we provide a different perspective based on online learning that underscores the importance of Adam's algorithmic components. Inspired by Cutkosky et al. (2023), we consider the framework called online learning of updates, where we choose the updates of an optimizer based on an online learner. With this framework, the design of a good optimizer is reduced to the design of a good online learner. Our main observation is that Adam corresponds to a principled online learning framework called Follow-the-Regularized-Leader (FTRL). Building on this observation, we study the benefits of its algorithmic components from the online learning perspective.