Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, lab, modeling, and analysis activities, called ModEx. The BER’s ‘Model-Experimentation’, ModEx, is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process-based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the ‘AI Architectures and Co-design’ session and associated outcomes. The AI Architectures and Co-design session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including: (1) DOE high-performance computing (HPC) Systems, (2) Cloud HPC Systems, and (3) Edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this co-design area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as: (1) reimagining co-design, (2) data acquisition to distribution, (3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with earth system modeling and simulation, and (4) AI-enabled sensor integration into earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.
Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study

Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multispectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations

Fluid mechanics is a fundamental field in engineering and science. Solving the Navier-Stokes equation (NSE) is critical for understanding the behavior of fluids. However, the NSE is a complex partial differential equation that is difficult to solve, and classical numerical methods can be computationally expensive. In this paper, we present an innovative approach for solving the NSE using Physics Informed Neural Networks (PINN) and several novel techniques that improve their performance. The first model is based on an assumption that involves approximating the velocity component by employing the derivative of a stream function. This assumption serves to simplify the system and guarantees that the velocity adheres to the divergence-free equation. We also developed a second more flexible model that approximates the solution without any assumptions. The proposed models can effectively solve two-dimensional NSE. Moreover, we successfully applied the second model to solve the three-dimensional NSE. The results show that the models can efficiently and accurately solve the NSE in three dimensions. These approaches offer several advantages, including high trainability, flexibility, and efficiency.
Pallet Detection from Synthetic Data Using Game Engines

This research sets out to assess the viability of using game engines to generate synthetic training data for machine learning in the context of pallet segmentation. Using synthetic data has been proven in prior research to be a viable means of training neural networks and saves hours of manual labour due to the reduced need for manual image annotation. Machine vision for pallet detection can benefit from synthetic data as the industry increases the development of autonomous warehousing technologies. As per our methodology, we developed a tool capable of automatically generating large amounts of annotated training data from 3D models at pixel-perfect accuracy and a much faster rate than manual approaches. Regarding image segmentation, a Mask R-CNN pipeline was used, which achieved an AP50 of 86% for individual pallets.
Sound Dynamic Deadlock Prediction in Linear Time

Deadlocks are one of the most notorious concurrency bugs, and significant research has focused on detecting them efficiently. Dynamic predictive analyses work by observing concurrent executions, and reason about alternative interleavings that can witness concurrency bugs. Such techniques offer scalability and sound bug reports, and have emerged as an effective approach for concurrency bug detection, such as data races. Effective dynamic deadlock prediction, however, has proven a challenging task, as no deadlock predictor currently meets the requirements of soundness, high-precision, and efficiency. In this paper, we first formally establish that this tradeoff is unavoidable, by showing that (a) sound and complete deadlock prediction is intractable, in general, and (b) even the seemingly simpler task of determining the presence of potential deadlocks, which often serve as unsound witnesses for actual predictable deadlocks, is intractable. The main contribution of this work is a new class of predictable deadlocks, called sync(hronization)-preserving deadlocks. Informally, these are deadlocks that can be predicted by reordering the observed execution while preserving the relative order of conflicting critical sections. We present two algorithms for sound deadlock prediction based on this notion. Our first algorithm SPDOffline detects all sync-preserving deadlocks, with running time that is linear per abstract deadlock pattern, a novel notion also introduced in this work. Our second algorithm SPDOnline predicts all sync-preserving deadlocks that involve two threads in a strictly online fashion, runs in overall linear time, and is better suited for a runtime monitoring setting. We implemented both our algorithms and evaluated their ability to perform offline and online deadlock-prediction on a large dataset of standard benchmarks. Our results indicate that our new notion of sync-preserving deadlocks is highly effective, as (i) it can characterize the vast majority of deadlocks and (ii) it can be detected using an online, sound, complete and highly efficient algorithm.
Opinion Mining from YouTube Captions Using ChatGPT: A Case Study of Street Interviews Polling the 2023 Turkish Elections

Opinion mining plays a critical role in understanding public sentiment and preferences, particularly in the context of political elections. Traditional polling methods, while useful, can be expensive and less scalable. Social media offers an alternative source of data for opinion mining but presents challenges such as noise, biases, and platform limitations in data collection. In this paper, we propose a novel approach for opinion mining, utilizing YouTube's auto-generated captions from public interviews as a data source, specifically focusing on the 2023 Turkish elections as a case study. We introduce an opinion mining framework using ChatGPT to mass-annotate voting intentions and motivations that represent the stance and frames prior to the election. We report that ChatGPT can predict the preferred candidate with 97\% accuracy and identify the correct voting motivation out of 13 possible choices with 71\% accuracy based on the data collected from 325 interviews. We conclude by discussing the robustness of our approach, accounting for factors such as captions quality, interview length, and channels. This new method will offer a less noisy and cost-effective alternative for opinion mining using social media data.
A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important

Many models for spatial and spatio-temporal data assume that"near things are more related than distant things,"which is known as the first law of geography. While geography may be important, it may not be all-important, for at least two reasons. First, technology helps bridge distance, so that regions separated by large distances may be more similar than would be expected based on geographical distance. Second, geographical, political, and social divisions can make neighboring regions dissimilar. We develop a flexible Bayesian approach for learning from spatial data which units are close in an unobserved socio-demographic space and hence which units are similar. As a by-product, the Bayesian approach helps quantify the relative importance of socio-demographic space relative to geographical space. To demonstrate the proposed approach, we present simulations along with an application to county-level data on median household income in the U.S. state of Florida.
Graphon Estimation in bipartite graphs with observable edge labels and unobservable node labels

Many real-world data sets can be presented in the form of a matrix whose entries correspond to the interaction between two entities of different natures (number of times a web user visits a web page, a student's grade in a subject, a patient's rating of a doctor, etc.). We assume in this paper that the mentioned interaction is determined by unobservable latent variables describing each entity. Our objective is to estimate the conditional expectation of the data matrix given the unobservable variables. This is presented as a problem of estimation of a bivariate function referred to as graphon. We study the cases of piecewise constant and H\"older-continuous graphons. We establish finite sample risk bounds for the least squares estimator and the exponentially weighted aggregate. These bounds highlight the dependence of the estimation error on the size of the data set, the maximum intensity of the interactions, and the level of noise. As the analyzed least-squares estimator is intractable, we propose an adaptation of Lloyd's alternating minimization algorithm to compute an approximation of the least-squares estimator. Finally, we present numerical experiments in order to illustrate the empirical performance of the graphon estimator on synthetic data sets.
A physics-informed neural network framework for modeling obstacle-related equations

Deep learning has been highly successful in some applications. Nevertheless, its use for solving partial differential equations (PDEs) has only been of recent interest with current state-of-the-art machine learning libraries, e.g., TensorFlow or PyTorch. Physics-informed neural networks (PINNs) are an attractive tool for solving partial differential equations based on sparse and noisy data. Here extend PINNs to solve obstacle-related PDEs which present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of the solution that lies above a given obstacle. The performance of the proposed PINNs is demonstrated in multiple scenarios for linear and nonlinear PDEs subject to regular and irregular obstacles.
A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://github.com/ChanglongJiangGit/A2J-Transformer.
Hierarchical Catalogue Generation for Literature Review: A Benchmark

Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. We observe that a high-quality catalogue-guided generation process can effectively alleviate this problem. Therefore, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review as the first step for review generation, which aims to produce a hierarchical catalogue of a review paper given various references. We construct a novel English Hierarchical Catalogues of Literature Reviews Dataset with 7.6k literature review catalogues and 389k reference papers. To accurately assess the model performance, we design two evaluation metrics for informativeness and similarity to ground truth from semantics and structure.Our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. We further benchmark diverse experiments on state-of-the-art summarization models like BART and large language models like ChatGPT to evaluate their capabilities. We further discuss potential directions for this task to motivate future research.
Approximation des equations de Maxwell par des schemas decentres en elements finis

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Approximation des equations de Maxwell par des schemas decentres en elements finis Jean-Pierre Cioni, Loula Fezoui, Hervé Steve
Factors influencing mammalian community occupancy in Dhorpatan Hunting Reserve, Nepal

Abstract The decline in mammalian species diversity is increasing worldwide, with areas characterized by high human activities experiencing more prominent effects. Knowledge of spatial distributions of species and factors acting on them is necessary for effective management. We evaluated community‐level occupancy of mammal species in Dhorpatan Hunting Reserve (DHR), Nepal using remote cameras during 15 March–15 June 2022. We used mammal species detections from remote cameras and multispecies hierarchical occupancy modeling to assess the effects of environmental and anthropogenic variables on community‐level occupancy of detected mammal species. We identified a highly heterogeneous mammal species community at DHR with greatest detection probability (0.21) for red fox (Vulpes vulpes) and lowest (0.08) for blue sheep (Pseudois nayaur). Naïve occupancy ranged from 0.31 for giant‐flying squirrel (Petaurista magnificus) to 0.84 for red fox. Mammal community occupancy increased with increasing canopy cover and number of livestock detections, but overall occupancy declined close to human settlements. The findings of this study can be used for developing policy at DHR for the management of mammal species through reducing the potential increase of human settlements or livestock grazing.
Enhancing type 2 diabetes treatment through digital plans of care. Patterns of access to a care-planning app over the first 3 months of a digital health intervention

Enhancing type 2 diabetes treatment through digital plans of care. Patterns of access to a care-planning app over the first 3 months of a digital health intervention Adrian H. Heald, Sarah Roberts, Lucia Albeda Gimeno, John Martin Gibson, Anuj Saboo and Jonathan Abraham Department of Diabetes and Endocrinology, Salford Royal Hospital, Salford, The School of Medicine and Manchester Academic Health Sciences Centre, University of Manchester, Manchester and Healum, London, UK5April20235April2023122
Do releases of the mirid predator Macrolophus basicornis (Hemiptera: Miridae) together with the egg parasitoid Trichogramma pretiosum (Hymenoptera: Trichogrammatidae) improve biological control of Tuta absoluta (Lepidoptera: Gelechiidae) in tomato?

During the past decade, the use of predatory mirids alone or combined with releases of egg parasitoids of the genus Trichogramma have been tested in Europe for biological control of the worldwide invasive pest, Tuta absoluta (Meyrick). Here, we evaluated the control of this pest by the release of the Neotropical mirid Macrolophus basicornis (Stal), the Neotropic/Nearctic parasitoid Trichogramma pretiosum Riley, and by combined releases of the predator and the parasitoid. Tests were conducted in greenhouse compartments during the summer and fall season. Each compartment contained 10 tomato plants in which only the pest was released or the pest with 1 or 2 natural enemies. Plant damage, and pest and natural enemy densities were checked weekly on one apical, medium, and bottom leaf of 5 plants. Both M. basicornis and T. pretiosum significantly reduced T. absoluta density when released alone. Combined releases resulted in a 10% higher reduction during the summer season, but not during the fall season. The damage caused by T. absoluta was significantly higher in control treatments than in all natural enemy treatments: at the end of the summer trial leaves were completely damaged in the control treatment, whereas only up to 25% leaf damage occurred in the natural enemy treatments. Combined releases did not result in lower damage than with releases of either M. basicornis or T. pretiosum. Practical aspects of single and combined releases are discussed. Graphical Abstract
Auxin inhibits lignin and cellulose biosynthesis in stone cells of pear fruit via the PbrARF13‐PbrNSC‐PbrMYB132 transcriptional regulatory cascade

Summary Stone cells are often present in pear fruit, and they can seriously affect the fruit quality when present in large numbers. The plant growth regulator NAA, a synthetic auxin, is known to play an active role in fruit development regulation. However, the genetic mechanisms of NAA regulation of stone cell formation are still unclear. Here, we demonstrated that exogenous application of 200 μm NAA reduced stone cell content and also significantly decreased the expression level of PbrNSC encoding a transcriptional regulator. PbrNSC was shown to bind to an auxin response factor, PbrARF13. Overexpression of PbrARF13 decreased stone cell content in pear fruit and secondary cell wall (SCW) thickness in transgenic Arabidopsis plants. In contrast, knocking down PbrARF13 expression using virus‐induced gene silencing had the opposite effect. PbrARF13 was subsequently shown to inhibit PbrNSC expression by directly binding to its promoter, and further to reduce stone cell content. Furthermore, PbrNSC was identified as a positive regulator of PbrMYB132 through analyses of co‐expression network of stone cell formation‐related genes. PbrMYB132 activated the expression of gene encoding cellulose synthase (PbrCESA4b/7a/8a) and lignin laccase (PbrLAC5) binding to their promotors. As expected, overexpression or knockdown of PbrMYB132 increased or decreased stone cell content in pear fruit and SCW thickness in Arabidopsis transgenic plants. In conclusion, our study shows that the ‘PbrARF13‐PbrNSC‐PbrMYB132’ regulatory cascade mediates the biosynthesis of lignin and cellulose in stone cells of pear fruit in response to auxin signals and also provides new insights into plant SCW formation.
A critical review of methods, principles and progress for estimating the gross primary productivity of terrestrial ecosystems

The gross primary productivity (GPP) of terrestrial ecosystems reflects the total amount of organic carbon assimilated by vegetation through photosynthesis per given unit of time and area, which represents the largest carbon flux in carbon budget and plays a fundamental part in the carbon cycle. However, challenges such as determining how to select appropriate methods to improve GPP estimation accuracy at the regional/global scale remain. Therefore, it is of great importance to comprehensively review the research progress on the methods for estimating the GPP of terrestrial ecosystems and to summarize their flaws, merits and application fields. In this study, we reviewed studies of GPP estimation at different spatiotemporal scales, and systematically reviewed the principles, formulas, representative methods (Ground observations, Model simulations, SIF based GPP, and NIRv based GPP) at different scales and models (Statistical/Ecological process/Machine learning/Light use efficiency models), as well as the advantages and limitations of each research method/models. A comprehensive comparison of GPP research methods was performed. We expect that this work will provide some straightforward references for researchers to further understand and to choose appropriate models for assessing forest ecosystem GPP according to the research objectives and area. Thus, critical and effective GPP estimation methods can be established for the terrestrial carbon cycle, carbon neutralization accounting and local carbon emission reduction policy formulation and implementation.