Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of schemes for earthquake prediction: Editor's introduction

SUMMARY This contribution introduces a series of papers on the assessment of schemes for earthquake prediction, originally presented at an RAS/JAG Discussion Meeting. The papers also address the social and administrative implications of earthquake prediction and the question whether this objective is feasible in either a strictly scientific or a socially useful sense. Contributions on the assessment process itself stress the importance of clearly defining procedures and anticipated outcomes in advance of any prediction experiment. After-the-fact searches for correlations drastically weaken the statistical significance of any association which is found. The record of a proposed prediction scheme can be made to appear more significant by comparing it with an inadequate or unsuitable model of seismicity. Because the distribution of earthquakes is clustered in both time and space, simple models (e.g. Poisson distribution in time) do not fully characterize it, and statistical tests which rely on such assumptions are inappropriate. The history of attempts to predict earthquakes suggests that little if any progress is being made towards the goal of specifying—in advance and within reasonably tight limits—the time and place of occurrence and the size of the forthcoming event. The view that prediction may be impossible is regaining support following a change in perception regarding the nature and origin of earthquakes and faulting. Reid's elastic rebound model and its associated conceptual framework of a near-homogeneous Earth are being superseded by a fundamentally heterogenous model employing the stochastically oriented concept of self-organized criticality. Within the latter model, both the initiation and the eventual progress of rupture on a fault depend on a multitude of factors related to both local stress and strength, which are inter-related and heterogeneous beyond practical measurement. Thus, the course of a fracture towards large-scale failure cannot practically be determined in advance. Other contributors show that an unusually high degree of scientific confidence is required in order for a prediction to be of significant social value. Therefore, in terms of saving both lives and property, statistical approaches to hazard assessment, combined with appropriate programmes of risk reduction, continue to offer the most cost-effective means of reducing earthquake-related losses.
Who is the patient at risk for EBV reactivation and disease: expert opinion focused on post-transplant lymphoproliferative disorders following hematopoietic stem cell transplantation

ABSTRACT Introduction Post-transplant lymphoproliferative disorders (PTLD) represent a diverse group of diseases. They develop as a consequence of uncontrolled proliferation of lymphoid or plasmacytic cells resulting from T-cell immunosuppression after transplantation of either hematopoietic cells (HCT) or solid organs (SOT), caused mainly by latent Epstein-Barr virus (EBV). The risk for EBV recurrence is dependent on the level of incompetency of the immune system, presented as an impairment of T-cell immunity. Areas covered This review summarizes the data on incidence and risk factors of EBV infection in patients after HCT. The median rate of EBV infection in HCT recipients was estimated at 30% after allogeneic and<1% after autologous transplant; 5% in non-transplant hematological malignancies; 30% in SOT recipients. The median rate of PTLD after HCT is estimated at 3%. The most frequently reported risk factors for EBV infection and disease include: donor EBV-seropositivity, use of T-cell depletion, especially with ATG; reduced-intensity conditioning; mismatched family or unrelated donor transplants; and acute or chronic graft-versus-host-disease. Expert opinion The major risk factors for EBV infection and EBV-PTLD can be easily identified: EBV-seropositive donor, depletion of T-cells, and the use of immunosuppressive therapy. Strategies for avoiding risk factors include elimination EBV from the graft and improving T-cell function.
Contrasting warming responses of photosynthesis in early- and late-successional tropical trees

Abstract The productivity and climate feedbacks of tropical forests depend on tree physiological responses to warmer and, over large areas, seasonally drier conditions. However, knowledge regarding such responses is limited due to data scarcity. We studied the impact of growth temperature on net photosynthesis (An), maximum rates of Rubisco carboxylation at 25 °C (Vcmax25), stomatal conductance (gs) and the slope parameter of the stomatal conductance-photosynthesis model (g1), in 10 early successional (ES) and 8 late-successional (LS) tropical tree species grown at three sites along an elevation gradient in Rwanda, differing by 6.8 °C in daytime ambient air temperature. The effect of seasonal drought on An was also investigated. We found that warm climate decreased wet-season An in LS species, but not in ES species. Values of Vcmax25 were lower at the warmest site across both successional groups, and An and Vcmax25 were higher in ES compared with LS species. Stomatal conductance exhibited no significant site differences and g1 was similar across both sites and successional groups. Drought strongly reduced An at warmer sites but not at the coolest montane site and this response was similar in both ES and LS species. Our results suggest that warming has negative effects on leaf-level photosynthesis in LS species, while both LS and ES species suffer photosynthesis declines in a warmer climate with more pronounced droughts. The contrasting responses of An between successional groups may lead to shifts in species’ competitive balance in a warmer world, to the disadvantage of LS trees.
Gender‐specific prevalence and trend of heart failure in China from 1990 to 2019

Heart failure (HF) is one of the leading causes of the global burden of disability and mortality. However, the comprehensive epidemic status of HF in China is unclear. Notably, the gender‐specific survey for HF prevalence is lacking. The present study aimed to analyse the gender‐specific prevalence and temporal trend of HF in China and explore the attributable aetiology and risk factors.
Optimizing Sepsis Care through Heuristics Methods in Process Mining: A Trajectory Analysis

Process mining can help acquire insightful knowledge and heighten the system's performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional hospital in the Netherlands from the registration to the discharge phase. Based on this real-world case study, the event log comprises events and activities related to the emergency ward, admission to hospital wards, and discharge enriched with data from lab experiments and triage checklists. At first, we aim to discover this process through Heuristics Miner (HM) and Inductive Miner (IM) methods. Then, we analyze a systematic process model based on organizational information and knowledge. Besides, we address conformance checking given medical guidelines for these patients and monitor the related flows on the systematic process model. The results show that HM and IM are inadequate in identifying the relevant process. However, using a systematic process model based on expert knowledge and organizational information resulted in an average fitness of 97.8%, a simplicity of 77.7%, and a generalization of 80.2%. The analyses demonstrate that process mining can shed light on the patient flow in the hospital and inspect the day-to-day clinical performance versus medical guidelines. Also, the process models obtained by the HM and IM methods cannot provide a concrete comprehension of the process structure for stakeholders compared to the systematic process model. The implications of our findings include the potential for process mining to improve the quality of healthcare services, optimize resource allocation, and reduce costs. Our study also highlights the importance of considering expert knowledge and organizational information in developing effective process models.
Network Autocorrelation in Transport Network and Flow Systems

The use of Moran's I to assess the existence of network autocorrelation in flows over the arcs of real (tangible) and abstract (intangible) networks is examined. Residuals of a migration model developed here reveal the presence of such autocorrelation or dependence. Two approaches for removing the observed dependence are examined.
A CQM-based approach to solving a combinatorial problem with applications in drug design

The use of D-Wave's Leap Hybrid solver is demonstrated here in solving a Knapsack optimization problem: finding meal combinations from a fixed menu that fit a diner's constraints. This is done by first formulating the optimization problem as a Constrained Quadratic Model (CQM) and then submitting it to a quantum annealer. We highlight here the steps needed, as well as the implemented code, and provide solutions from a Chicken and Waffle restaurant menu. Additionally, we discuss how this model may be generalized to find optimal drug molecules within a large search space with many complex, and often contradictory, structures and property constraints.
A fuzzy automated object classification by infrared laser camera

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.
Comparison between layer-to-layer network training and conventional network training using Convolutional Neural Networks

Title: Comparison between layer-to-layer network training and conventional network training using Deep Convolutional Neural Networks Abstract: Convolutional neural networks (CNNs) are widely used in various applications due to their effectiveness in extracting features from data. However, the performance of a CNN heavily depends on its architecture and training process. In this study, we propose a layer-to-layer training method and compare its performance with the conventional training method. In the layer-to-layer training approach, we treat a portion of the early layers as a student network and the later layers as a teacher network. During each training step, we incrementally train the student network to learn from the output of the teacher network, and vice versa. We evaluate this approach on VGG16, ResNext, and DenseNet networks without pre-trained ImageNet weights and a regular CNN model. Our experiments show that the layer-to-layer training method outperforms the conventional training method for both models. Specifically, we achieve higher accuracy on the test set for the VGG16, ResNext, and DeseNet networks and the CNN model using layer-to-layer training compared to the conventional training method. Overall, our study highlights the importance of layer-wise training in CNNs and suggests that layer-to-layer training can be a promising approach for improving the accuracy of CNNs.
Parameter Efficient Local Implicit Image Function Network for Face Segmentation

Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial regions like eyes, lips, nose, hair, etc. In this work, we make use of the structural consistency of the human face to propose a lightweight face-parsing method using a Local Implicit Function network, FP-LIIF. We propose a simple architecture having a convolutional encoder and a pixel MLP decoder that uses 1/26th number of parameters compared to the state-of-the-art models and yet matches or outperforms state-of-the-art models on multiple datasets, like CelebAMask-HQ and LaPa. We do not use any pretraining, and compared to other works, our network can also generate segmentation at different resolutions without any changes in the input resolution. This work enables the use of facial segmentation on low-compute or low-bandwidth devices because of its higher FPS and smaller model size.
Analysis for the Effect of Inverter Ripple Current on Fuel Cell Operating Condition

The effect of inverter ripple current on fuel cell stack performance and stack lifetime remains uncertain. This paper provides a first attempt to examine the impact of inverter load dynamics on the fuel cell. Since reactant utilization is known to impact the mechanical state of a fuel cell, it is suggested that the varying reactant conditions surrounding the cell govern, at least in part, the lifetime of the cells. This paper investigates these conditions through the use of a dynamic model for the bulk conditions within the stack, as well as a one-dimensional model for the detailed mass transport occurring within the electrode of a cell. These two independent modeling approaches help to verify their respective numerical procedures. In this work, the inverter load is imposed as a boundary condition to the models. Results show the transient behavior of the reactant concentrations within the stack, and of the mass diffusion within the electrode under inverter loads with frequencies between 30 Hz and 1250 Hz.
The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events

Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-paced settings: (1) navigating the unique affordances of VSPs, (2) understanding VSP content and determining its authenticity, and (3) novel user behaviors on VSPs for spreading misinformation. By highlighting these challenges, we hope that researchers can reflect on how to adapt existing research methods and tools to these new contexts, or develop entirely new ones.
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design

AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research has called for increased scrutiny around the validity of AI system designs. However, in real-world settings, it is often not possible to fully address questions around the validity of an AI tool without also considering the design of associated organizational and public policies. Yet, considerations around how an AI tool may interface with policy are often only discussed retrospectively, after the tool is designed or deployed. In this short position paper, we discuss opportunities to promote multi-stakeholder deliberations around the design of AI-based technologies and associated policies, at the earliest stages of a new project.
On Graphical Methods in Stochastic Choice

In recent years there has been an influx of papers which use graph theoretic tools to study stochastic choice. Fiorini (2004) serves as a base for this literature by providing a graphical representation of choice probabilities and showing that every interior node of this graph must satisfy inflow equals outflow. We show that this inflow equals outflow property is almost characteristic of choice probabilities. In doing so, we characterize choice probabilities through graph theoretic tools. We offer two applications of this result to random utility. First, we offer a novel axiomatization of random utility on an incomplete domain. Second, we construct a new hypothesis test for random utility which offers computational improvements over the hypothesis test of Kitamura and Stoye (2018).
A link between Kendall’s τ, the length measure and the surface of bivariate copulas, and a consequence to copulas with self-similar support

Abstract Working with shuffles, we establish a close link between Kendall’s τ \tau , the so-called length measure, and the surface area of bivariate copulas and derive some consequences. While it is well known that Spearman’s ρ \rho of a bivariate copula A A is a rescaled version of the volume of the area under the graph of A A , in this contribution we show that the other famous concordance measure, Kendall’s τ \tau , allows for a simple geometric interpretation as well – it is inextricably linked to the surface area of A A .
Supplementary Materials for: Generalizable Local Feature Pre-training for Deformable Shape Analysis

In this document, we collect all the results and discussions, which, due to the page limit, could not find space in the main manuscript. This supplementary material consists of two parts. First, in Appendix A, we describe more implementation details mainly regarding our pilot study, local feature pre-training, and experiments on downstream deformable shape data. Next, in Appendix B, we present additional experimental results and analysis of our local feature pre-training strategy and its generalization in downstream tasks, including deformable shape matching and segmentation.
Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation

In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE). Most previous bottom-up methods try to consider the relation of instances to identify different body parts during the post processing, while ignoring to model the relation among instances or environment in the feature learning process. In addition, most existing works adopt the operations of upsampling and downsampling. During the sampling process, there will be a problem of misalignment with the source features, resulting in deviations in the keypoint features learned by the model. To overcome the above limitations, we propose a convolutional neural network for bottom-up human pose estimation. It invovles two basic modules: (i) Global Relation Modeling (GRM) module globally learns relation (e.g., environment context, instance interactive information) among region of image by fusing multiple stages features in the feature learning process. It combines with the spatial-channel attention mechanism, which focuses on achieving adaptability in spatial and channel dimensions. (ii) Multi-branch Feature Align (MFA) module aggregates features from multiple branches to align fused feature and obtain refined local keypoint representation. Our model has the ability to focus on different granularity from local to global regions, which significantly boosts the performance of the multi-person pose estimation. Our results on the COCO and CrowdPose datasets demonstrate that it is an efficient framework for multi-person pose estimation.
A Survey of Machine Learning-Based Ride-Hailing Planning

Ride-hailing is a sustainable transportation paradigm where riders access door-to-door traveling services through a mobile phone application, which has attracted a colossal amount of usage. There are two major planning tasks in a ride-hailing system: (1) matching, i.e., assigning available vehicles to pick up the riders, and (2) repositioning, i.e., proactively relocating vehicles to certain locations to balance the supply and demand of ride-hailing services. Recently, many studies of ride-hailing planning that leverage machine learning techniques have emerged. In this article, we present a comprehensive overview on latest developments of machine learning-based ride-hailing planning. To offer a clear and structured review, we introduce a taxonomy into which we carefully fit the different categories of related works according to the types of their planning tasks and solution schemes, which include collective matching, distributed matching, collective repositioning, distributed repositioning, and joint matching and repositioning. We further shed light on many real-world datasets and simulators that are indispensable for empirical studies on machine learning-based ride-hailing planning strategies. At last, we propose several promising research directions for this rapidly growing research and practical field.
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining

The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks. It will make the model to better understand the similarity between context, and more robust to the synonym substitution attack. However, many Pretrained Language Model (PLM) lack synonym knowledge due to limitation of small-scale synsets and PLM's pretraining objectives. In this paper, we propose a framework called Sem4SAP to mine synsets from Open Knowledge Graph (Open-KG) and using the mined synsets to do synonym-aware pretraining for language models. We propose to coarsly filter the content in Open-KG and use the frequency information to better help the clustering process under low-resource unsupervised conditions. We expand the mined synsets by migrating core semantics between synonymous expressions.We also propose two novel and effective synonym-aware pre-training methods for injecting synonym knowledge into PLMs.Extensive experiments demonstrate that Sem4SAP can dramatically outperform the original PLMs and other baselines on ten different tasks.
Probing optimisation in physics-informed neural networks

A novel comparison is presented of the effect of optimiser choice on the accuracy of physics-informed neural networks (PINNs). To give insight into why some optimisers are better, a new approach is proposed that tracks the training trajectory curvature and can be evaluated on the fly at a low computational cost. The linear advection equation is studied for several advective velocities, and we show that the optimiser choice substantially impacts PINNs model performance and accuracy. Furthermore, using the curvature measure, we found a negative correlation between the convergence error and the curvature in the optimiser local reference frame. It is concluded that, in this case, larger local curvature values result in better solutions. Consequently, optimisation of PINNs is made more difficult as minima are in highly curved regions.