Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty of Concentration–Terrestrial Carbon Feedback in Earth System Models*

AbstractCarbon uptake by land and ocean as a biogeochemical response to increasing atmospheric CO2 concentration is called concentration–carbon feedback and is one of the carbon cycle feedbacks of the global climate. This feedback can have a major impact on climate projections with an uncertain magnitude. This paper focuses on the concentration–carbon feedback in terrestrial ecosystems, analyzing the mechanisms and strength of the feedback reproduced by Earth system models (ESMs) participating in phase 5 of the Coupled Model Intercomparison Project. It is confirmed that multiple ESMs driven by a common scenario show a large spread of concentration–carbon feedback strength among models. Examining the behavior of the carbon fluxes and pools of the models showed that the sensitivity of plant productivity to elevated CO2 is likely the key to reduce the spread, although increasing CO2 stimulates other carbon cycle processes. Simulations with a single ESM driven by different CO2 pathways demonstrated that carbo.
Insights on Pinna nobilis population genetic structure in the Aegean and Ionian Sea

The fan mussel Pinna nobilis Linnaeus, 1758 is an endemic species of the Mediterranean Sea, protected by international agreements. It is one of the largest bivalves in the world, playing an important role in the benthic communities; yet it has been recently characterized as Critically Endangered by the IUCN, due to mass mortality events. In this context, the assessment of the genetic variation of the remaining P. nobilis populations and the evaluation of connectivity among them are crucial elements for the conservation of the species. For this purpose, samples were collected from six regions of the Eastern Mediterranean Sea; the Islands of Karpathos, Lesvos and Crete; the Chalkidiki and Attica Peninsulas; and the Amvrakikos Gulf. Sampling was performed either by collecting tissue from the individuals or by using a non-invasive method, i.e., by scraping the inside of their shells aiming to collect their mucus and thus avoid stress induction to them. Conventional molecular techniques with the use of the COI and 16S rRNA mitochondrial markers were selected for the depiction of the intra-population genetic variability. The analyses included 104 samples from the present study and publicly available sequences of individuals across the whole Mediterranean Sea. The results of this work (a) suggest the use of eDNA as an efficient sampling method for protected bivalves and (b) shed light to the genetic structure of P. nobilis population in the Eastern Mediterranean; this latter knowledge might prove to be fundamental for the species conservation and hence the ecosystem resilience. The haplotype analyses reinforced the evidence that there is a certain degree of connectivity among the distinct regions of the Mediterranean; yet there is evidence of population distinction within the basin, namely between the Western and the Eastern basins. The combination of both genetic markers in the same analysis along with the inclusion of a large number of individuals produced more robust results, revealing a group of haplotypes being present only in the Eastern Mediterranean and providing insights for the species’ most suitable conservation management.
FRESH™ 3D bioprinted cardiac tissue, a bioengineered platform for in vitro pharmacology

There is critical need for a predictive model of human cardiac physiology in drug development to assess compound effects on human tissues. In vitro two-dimensional monolayer cultures of cardiomyocytes provide biochemical and cellular readouts, and in vivo animal models provide information on systemic cardiovascular response. However, there remains a significant gap in these models due to their incomplete recapitulation of adult human cardiovascular physiology. Recent efforts in developing in vitro models from engineered heart tissues have demonstrated potential for bridging this gap using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in three-dimensional tissue structure. Here, we advance this paradigm by implementing FRESH™ 3D bioprinting to build human cardiac tissues in a medium throughput, well-plate format with controlled tissue architecture, tailored cellular composition, and native-like physiological function, specifically in its drug response. We combined hiPSC-CMs, endothelial cells, and fibroblasts in a cellular bioink and FRESH™ 3D bioprinted this mixture in the format of a thin tissue strip stabilized on a tissue fixture. We show that cardiac tissues could be fabricated directly in a 24-well plate format were composed of dense and highly aligned hiPSC-CMs at >600 million cells/mL and, within 14 days, demonstrated reproducible calcium transients and a fast conduction velocity of ∼16 cm/s. Interrogation of these cardiac tissues with the β-adrenergic receptor agonist isoproterenol showed responses consistent with positive chronotropy and inotropy. Treatment with calcium channel blocker verapamil demonstrated responses expected of hiPSC-CM derived cardiac tissues. These results confirm that FRESH™ 3D bioprinted cardiac tissues represent an in vitro platform that provides data on human physiological response.
Merwinite in diamond from São Luiz, Brazil: A new mineral of the Ca-rich mantle environment

Abstract Diamonds from Juina province, Brazil, and some others localities reveal the existence of a deep, Ca-rich carbonate-silicate source different from ultramafic and eclogite compositions. In this study, we describe the first observation of merwinite (Ca2.85Mg0.96Fe0.11Si2.04O8) in a diamond; it occurs as an inclusion in the central growth domain of a diamond from the São Luiz river alluvial deposits (Juina, Brazil). In addition, the diamond contains inclusions of walstromite-structured CaSiO3 in the core and (Mg0.86Fe0.14)2SiO4 olivine in the rim. According to available experimental data, under mantle conditions, merwinite can only be formed in a specific Ca-rich and Mg- and Si-depleted enviroment that differs from any known mantle lithology (peridotitic or eclogitic). We suggest that such chemical conditions can occur during the interaction of subduction-derived calcium carbonatite melt with peridotitic mantle. The partial reduction of the melt could cause the simultaneous crystallization of Ca-rich silicates (CaSiO3 and merwinite) and diamond at an early stage, and (Mg0.86Fe0.14)2SiO4 olivine and diamond at a later stage, after the Ca-Mg exchange between carbonatite melt and peridotite has ceased. This scenario is supported by the presence of calcite microinclusions within merwinite.
INFERRING EVOLUTIONARY ORDER AND DURATIONS USING BOTH STRATIGRAPHY AND CLADISTICS IN A FOSSIL LINEAGE (BRYOZOA: PERONOPORA)

Abstract In the Ordovician bryozoan genus, Peronopora, stratigraphic occurrences and cladistic branching order are significantly correlated, indicating sequential development of both patterns in geological time. Five species have stratigraphic first appearances in the exact order predicted by cladistics, but eleven species require downward-range extensions to match cladistic order. Reduced major-axis regression-based corrections and ghost range extensions represent two alternative modifications of first appearance data, with the latter more strongly supported by stratigraphic congruence indices. Tests of the robustness of observed first appearances, based on the density of sampled horizons and magnitudes of stratigraphic gaps, support the probabilistic appearance of descendant species stratigraphically above their putative ancestors in eight of fifteen ancestor-descendant pairs. A 24 m sampling gap occurs below the base of the Brannon Member of the Lexington Limestone, a unit marking the first appearances, or extended ranges, of nine species of Peronopora. A test for the presence of a uniform distribution of occurrence probabilities indicates that seven of the nine species could have originated during the time interval represented by the gap. The early branching rate within Peronopora, during the time span encompassing all first appearances of species, is 1.05 cladogram nodes per meter of strata. The rate of clade production within that interval is 0.73 (baseline) clades per meter of strata. Extrapolating downward using both rates indicates that the median position of the root of the generic clade is approximately 1.08 myr earlier than sampled. The estimated speciation rate in Peronopora is 5.73 species per myr, while the average time between speciation events is 186 kyr. Intraspecific clades, possibly including cryptic species, formed at a rate of 19.35 clades per myr, with an average waiting time per clade of 48.107 kyr. These metrics indicate very short origin times for species within the genus compared to their total stratigraphic ranges, a pattern consistent with punctuated speciation.
Medicinal Plant Resources of Rudrakod Sacred Grove in Nallamalais, Andhra Pradesh, India

Abstract Rudrakod sacred grove, located in Nallamalai hill ranges of Southern Andhra Pradesh harbour rich plant diversity. The Chenchu tribal communities living in and around the sacred groves are endowed with rich traditional botanical knowledge pertaining to medicinal values of plant species. In the present study, we documented 69 vascular plant species of medicinal value used by the tribes. The paper deals with the systematic enumeration of the species with brief description, their distribution, phenology, local names, and medicinal uses along with mode of administration. Over-exploitation and unscientific collection of some medicinal plants threatening the resource and warrants sustainable harvesting by the local communities.
Inflammatory dietary potential is associated with vitamin depletion and gut microbial dysbiosis in early pregnancy

Background: Pregnancy alters many physiological systems, including the maternal gut microbiota. Diet is a key regulator of this system and can alter host inflammation. Multiple perinatal disorders have been associated with inflammation, maternal metabolic alterations, and gut microbiota dysbiosis, including gestational diabetes mellitus, preeclampsia, preterm birth, and mood disorders. However, the effects of high inflammatory diets on the gut microbiota during pregnancy has yet to be fully explored. Objective: To use a systems-based approach to characterize associations among dietary inflammatory potential, a measure of diet quality, and the gut microbiome during pregnancy. Methods: Forty-nine pregnant persons were recruited prior to 16 weeks of gestation. Participants completed a food frequency questionnaire (FFQ) and provided fecal samples. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII) from FFQ data. Fecal samples were analyzed using 16S rRNA amplicon sequencing. Differential taxa abundance with respect to DII score were identified, and microbial metabolic potential was predicted using PICRUSt2. Results: Inflammatory diets were associated with decreased vitamin and mineral intake and dysbiotic gut microbiome structure and metabolism. Gut microbial compositional differences revealed a decrease in short chain fatty acid producers such as g_Faecalibacterium, upregulation of vitamin B12 synthesis, methylglyoxal detoxification, galactose metabolism and multi drug efflux systems in pregnant individuals with increased DII scores. Conclusions: Dietary inflammatory potential was associated with depletion of vitamins & minerals and gut microbiota metabolic dysregulation.
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?

Programming language understanding and representation (a.k.a code representation learning) has always been a hot and challenging task in software engineering. It aims to apply deep learning techniques to produce numerical representations of the source code features while preserving its semantics. These representations can be used for facilitating subsequent code-related tasks. The abstract syntax tree (AST), a fundamental code feature, illustrates the syntactic information of the source code and has been widely used in code representation learning. However, there is still a lack of systematic and quantitative evaluation of how well AST-based code representation facilitates subsequent code-related tasks. In this paper, we first conduct a comprehensive empirical study to explore the effectiveness of the AST-based code representation in facilitating follow-up code-related tasks. To do so, we compare the performance of models trained with code token sequence (Token for short) based code representation and AST-based code representation on three popular types of code-related tasks. Surprisingly, the overall quantitative statistical results demonstrate that models trained with AST-based code representation consistently perform worse across all three tasks compared to models trained with Token-based code representation. Our further quantitative analysis reveals that models trained with AST-based code representation outperform models trained with Token-based code representation in certain subsets of samples across all three tasks. We also conduct comprehensive experiments to evaluate and reveal the impact of the choice of AST parsing/preprocessing/encoding methods on AST-based code representation and subsequent code-related tasks. Our study provides future researchers with detailed guidance on how to select solutions at each stage to fully exploit AST.
Multiple Control Functionals for Interconnected Time-Delay Systems

Safety is essential for autonomous systems, in particular for interconnected systems in which the interactions among subsystems are involved. Motivated by the recent interest in cyber-physical and interconnected autonomous systems, we address the safe stabilization problem of interconnected systems with time delays. We propose multiple control Lyapunov and barrier functionals for the stabilization and safety control problems, respectively. In order to investigate the safe stabilization control problem, the proposed multiple control functionals are combined together via two methods: the optimization-based method and the sliding mode based method. The resulting controllers can be of either explicit or implicit forms, both of which ensure the safe stabilization objective of the whole system. The derived results are illustrated via a reach-avoid problem of multi-robot systems.
Scalable Meta-Learning with Gaussian Processes

Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these methods are either computationally expensive or introduce assumptions that hinder a principled propagation of uncertainty between task models. This may disrupt the balance between exploration and exploitation during optimization. In this paper, we develop ScaML-GP, a modular GP model for meta-learning that is scalable in the number of tasks. Our core contribution is a carefully designed multi-task kernel that enables hierarchical training and task scalability. Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a test-task prior that combines the posteriors of meta-task GPs. In synthetic and real-world meta-learning experiments, we demonstrate that ScaML-GP can learn efficiently both with few and many meta-tasks.
BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos

Temporal sentence grounding aims to localize moments relevant to a language description. Recently, DETR-like approaches have shown notable progress by decoding the center and length of a target moment from learnable queries. However, they suffer from the issue of center misalignment raised by the inherent ambiguity of moment centers, leading to inaccurate predictions. To remedy this problem, we introduce a novel boundary-oriented moment formulation. In our paradigm, the model no longer needs to find the precise center but instead suffices to predict any anchor point within the interval, from which the onset and offset are directly estimated. Based on this idea, we design a Boundary-Aligned Moment Detection Transformer (BAM-DETR), equipped with a dual-pathway decoding process. Specifically, it refines the anchor and boundaries within parallel pathways using global and boundary-focused attention, respectively. This separate design allows the model to focus on desirable regions, enabling precise refinement of moment predictions. Further, we propose a quality-based ranking method, ensuring that proposals with high localization qualities are prioritized over incomplete ones. Extensive experiments verify the advantages of our methods, where our model records new state-of-the-art results on three benchmarks. Code is at https://github.com/Pilhyeon/BAM-DETR.
Navigating News Narratives: A Media Bias Analysis Dataset

The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change. This paper introduces the"Navigating News Narratives: A Media Bias Analysis Dataset", a comprehensive dataset to address the urgent need for tools to detect and analyze media bias. This dataset encompasses a broad spectrum of biases, making it a unique and valuable asset in the field of media studies and artificial intelligence. The dataset is available at https://huggingface.co/datasets/newsmediabias/news-bias-full-data.
A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products

In this paper we consider the numerical solution to the soft-margin support vector machine optimization problem. This problem is typically solved using the SMO algorithm, given the high computational complexity of traditional optimization algorithms when dealing with large-scale kernel matrices. In this work, we propose employing an NFFT-accelerated matrix-vector product using an ANOVA decomposition for the feature space that is used within an interior point method for the overall optimization problem. As this method requires the solution of a linear system of saddle point form we suggest a preconditioning approach that is based on low-rank approximations of the kernel matrix together with a Krylov subspace solver. We compare the accuracy of the ANOVA-based kernel with the default LIBSVM implementation. We investigate the performance of the different preconditioners as well as the accuracy of the ANOVA kernel on several large-scale datasets.
Spatial and Temporal Analysis of Drought over the Huang-Huai-Hai Plain and Its Surroundings Based on the Standardized Precipitation Index

Drought, one of the most severe hazards resulting in significant economic loses in agriculture and industry, is a natural phenomenon caused by the imbalance between import and demand for moisture. In the context of global climate change, the drought intensity, frequency, and its spatial patterns have been changing. An accurate understanding of these changing processes would be greatly useful in identifying regional climate change, regional drought forecasting, managing droughts, and ultimately restraining the risks incurred by droughts in practice. First, the authors conceptually divided the Huang-Huai-Hai plain and its surroundings into four sub-regions (A, B, C, and D). Second, the Standardized Precipitation Index (SPI) of different timescales in each sub-region was calculated at point scales. Third, after classifying drought severity into four classes, we performed temporal and spatial analysis of drought over this region based on drought frequencies of varying severity. Results indicate that there were marked spatial and temporal variations in drought over this study area. 1) On a 3-month timescale, sub-regions A and B suffered from more "severe" and "extreme" droughts, whereas there was no significant difference for all classes of drought between sub-regions; 2) On a 6-month timescale, sub-region A and B suffered from more "normal" drought, while sub-regions C and D suffered from relatively less "extreme" drought except the north of Anhui Province and the east of Henan Province; 3) On a 12-month timescale, sub-regions A and B also suffered from more "normal" drought, while sub-regions C and D suffered from more "severe" and "extreme" droughts; 4) As for seasonal drought, on a 3-month timescale, all sub-regions showed spatial variations in drought frequency. In particular, sub-region D suffered from more "extreme" drought in the growing season of crops (typically in summer and autumn over this region) compared to less "total" drought. On a 12-month timescale, sub-regions C and D suffered from more droughts which would have impacted reservoirs, lakes, and groundwater level; 5) From the temporal analysis of SPI12, it was found that aggravating or alleviating trends of drought severity for all sub-regions were essentially fluctuant. There was a slightly aggravating trend during the period 1960s-2000s, suggesting a linear trend of -0.784%. However, there was a significant aggravating trend in the 1960s with a linear trend of -3.837%/9a, alleviating for the period 1970s-1980s with a linear trend of 2.771%/10a and 4.380%/10a, aggravating again during the 1990s with a linear trend of -4.727%/10a, and alleviating again from 2000 with a linear trend of 4.589%/9a.
Cofiltrations of spanning trees in multiparameter persistent homology

Given a multiparameter filtration of simplicial complexes, we consider the problem of explicitly constructing generators for the multipersistent homology groups with arbitrary PID coefficients. We propose the use of spanning trees as a tool to identify such generators by introducing a condition for persistent spanning trees, which is accompanied by an existence result for cofiltrations consisting of spanning trees. We also introduce a generalization of spanning trees, called spanning complexes, for dimensions higher than one, and we establish their existence as a first step towards this direction.
QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11 - B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02{\deg} * 0.02{\deg}(approximately 2km * 2km) for many applied energy fields.