Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial lipid phosphate phosphatase-3 deficiency that disrupts the endothelial barrier function is a modifier of cardiovascular development

Aims Lipid phosphate phosphatase-3 (LPP3) is expressed at high levels in endothelial cells (ECs). Although LPP3 is known to hydrolyse the phosphate group from lysolipids such as spingosine-1-phosphate and its structural homologues, the function of Lpp3 in ECs is not completely understood. In this study, we investigated how tyrosine-protein kinase receptor (TEK or Tie2) promoter–dependent deletion of Lpp3 alters EC activities. Methods and results Lpp3fl/fl mice were crossed with the tg.Tie2Cre transgenic line. Vasculogenesis occurred normally in embryos with Tie2Cre-mediated deletion of Lpp3 (called Lpp3ECKO), but embryonic lethality occurred in two waves, the first wave between E8.5 and E10.5, while the second between E11.5 and E13.5. Lethality in Lpp3ECKO embryos after E11.5 was accompanied by vascular leakage and haemorrhage, which likely resulted in insufficient cardiovascular development. Analyses of haematoxylin- and eosin-stained heart sections from E11.5 Lpp3ECKO embryos showed insufficient heart growth associated with decreased trabeculation, reduced growth of the compact wall, and absence of cardiac cushions. Staining followed by microscopic analyses of Lpp3ECKO embryos revealed the presence of apoptotic ECs. Furthermore, Lpp3-deficient ECs showed decreased gene expression and protein levels of Cyclin-D1, VE-cadherin, Fibronectin, Klf2, and Klf4. To determine the underlying mechanisms of vascular leakage and barrier disruption, we performed knockdown and rescue experiments in cultured ECs. LPP3 knockdown decreased transendothelial electrical resistance and increased permeability. Re-expression of β-catenin cDNA in LPP3-knockdown ECs partially restored the effect of the LPP3 loss, whereas re-expression of p120ctn cDNA did not. Conclusion These findings demonstrate the essential roles of LPP3 in the maturation of EC barrier integrity and normal cardiovascular development.
Cosmic Inflation From Fluctuating Baby-Skyrme Brane

In this work, we explore the inflationary dynamics induced by small fluctuations on the Skyrme brane, characterized by a time-dependent perturbative function $\tilde{\phi}$. In the low-energy regime, the model successfully reproduces standard inflation, with a potential term dictated by the Skyrmion at the brane. Gravity localization is achieved at the brane, and the lowest energy scale is established at the asymptotic boundary. The model demonstrates the capability to emulate standard inflation dynamics, resembling $\tilde{\phi}^4$ potential characteristics under certain conditions. At higher energy levels, the behaviour of $\tilde{\phi}$ is contingent upon the Skyrme term coupling constant $\lambda$, influencing reheating phases. The wave-like nature of fluctuations allows for energy transfer, resulting in a possibly lower reheating temperature. We also discuss the prospect of $\lambda$ changing sign during inflation, presenting a non-standard coupling dependent on the matter field.
Energy-Efficient Neuronal Computation via Quantal Synaptic Failures

Organisms evolve as compromises, and many of these compromises can be expressed in terms of energy efficiency. For example, a compromise between rate of information processing and the energy consumed might explain certain neurophysiological and neuroanatomical observations (e.g., average firing frequency and number of neurons). Using this perspective reveals that the randomness injected into neural processing by the statistical uncertainty of synaptic transmission optimizes one kind of information processing relative to energy use. A critical hypothesis and insight is that neuronal information processing is appropriately measured, first, by considering dendrosomatic summation as a Shannon-type channel (1948) and, second, by considering such uncertain synaptic transmission as part of the dendrosomatic computation rather than as part of axonal information transmission. Using such a model of neural computation and matching the information gathered by dendritic summation to the axonal information transmitted,H(p*), conditions are defined that guarantee synaptic failures can improve the energetic efficiency of neurons. Further development provides a general expression relating optimal failure rate, f, to average firing rate, p*, and is consistent with physiologically observed values. The expression providing this relationship, f ≈ 4−H(p*), generalizes across activity levels and is independent of the number of inputs to a neuron.
Spatial Variation and Pattern of Daily Rainfall Intensity in the Middle Belt Region of Nigeria

The study analyzed the spatial variability of daily rainfall intensity in the Middle Belt region (MBR) of Nigeria for 46-year period (1961-2006). Daily rainfall (≥0.3mm) data were collected from eight synoptic weather stations in the study area. Daily rainfall intensity were classified into six categories using percentiles (P) namely; extremely light (≤ P10), light (P11 - P20), moderately light (P21 – P50), moderately heavy (P51 – P80), heavy (P81 – P90) and extremely heavy (> P90). To compute the percentile threshold values of the intensity categories, the population of rain days was first extracted and then ranked. The threshold values of the percentiles were extracted for each of the six categories among the eight stations and the annual number of daily rainfall for each station were then determined. The result showed a zonal pattern of increase in annual number of days for extremely heavy and heavy events from eastern to western parts of the MBR. There are enclaves of high annual rain days for the light and moderate categories in Jos plateau. The study concludes that the spatial variation will have tremendous impact on water resources and crop production as well as hazards of erosion and flooding.
Distributed cognition models for human factors failures in operating and design processes

A set of about 60 offshore incident reports was obtained and combined with a small set of reports of inquiries into full scale offshore accidents and then developed into cause-effect analyses. In each case the task was to identify how people's problem solving had been distributed, how this distribution had failed, and the assumptions that they had been making, by implication. It was these implied but flawed assumptions that were then carried over into the development of the workbooks. A further step was then taken to identify general design principles that would help make systems less vulnerable to each assumption type. Computer based workbooks were developed, which allow users to consult and reason about the flawed assumptions discovered in the accident analyses. They also allow users to consult and apply the design rules that were identified in the preceding analysis. The work also included an investigation of design error, which involved analysing a set of reports of error in the design process of firms designing both onshore and offshore installations, in combination with two group elicitation sessions. These reports were then analysed in a similar way to the accidents-by identifying how cognition had been distributed, how this distribution had failed, and the flawed assumptions that were implicated in these failures. Appropriate workbooks, similar to those developed for the accident analysis, werer also developed to help designers and managers of the design process anticipate flawed assumptions in the future by consulting assumption types known to be flawed in the past. This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the prior written permission of the copyright owner. iii iv 1 1 INTRODUCTION Background The proposal originated in previous work undertaken on making inferences from accidents and failures, which were failures both in the engineering process and in engineered systems. Most of the failures that had been studied involved people's use of designed objects and could not be attributed to the person or to the design exclusively, despite the fact that most classifications did simply classify them as being one or the …
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries

Reinforcement learning (RL) algorithms have become indispensable tools in artificial intelligence, empowering agents to acquire optimal decision-making policies through interactions with their environment and feedback mechanisms. This study explores the performance of RL agents in both two-dimensional (2D) and three-dimensional (3D) environments, aiming to research the dynamics of learning across different spatial dimensions. A key aspect of this investigation is the absence of pre-made libraries for learning, with the algorithm developed exclusively through computational mathematics. The methodological framework centers on RL principles, employing a Q-learning agent class and distinct environment classes tailored to each spatial dimension. The research aims to address the question: How do reinforcement learning agents adapt and perform in environments of varying spatial dimensions, particularly in 2D and 3D settings? Through empirical analysis, the study evaluates agents' learning trajectories and adaptation processes, revealing insights into the efficacy of RL algorithms in navigating complex, multi-dimensional spaces. Reflections on the findings prompt considerations for future research, particularly in understanding the dynamics of learning in higher-dimensional environments.
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation

This paper proposes a new knowledge distillation method tailored for image semantic segmentation, termed Intra- and Inter-Class Knowledge Distillation (I2CKD). The focus of this method is on capturing and transferring knowledge between the intermediate layers of teacher (cumbersome model) and student (compact model). For knowledge extraction, we exploit class prototypes derived from feature maps. To facilitate knowledge transfer, we employ a triplet loss in order to minimize intra-class variances and maximize inter-class variances between teacher and student prototypes. Consequently, I2CKD enables the student to better mimic the feature representation of the teacher for each class, thereby enhancing the segmentation performance of the compact network. Extensive experiments on three segmentation datasets, i.e., Cityscapes, Pascal VOC and CamVid, using various teacher-student network pairs demonstrate the effectiveness of the proposed method.
$L^\infty$-error bounds for approximations of the Koopman operator by kernel extended dynamic mode decomposition

Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dynamical systems. Recently, kernel EDMD (kEDMD) has gained popularity due to its ability to resolve the challenging task of choosing a suitable dictionary by defining data-based observables. In this paper, we provide the first pointwise bounds on the approximation error of kEDMD. The main idea consists of two steps. First, we show that the reproducing kernel Hilbert spaces of Wendland functions are invariant under the Koopman operator. Second, exploiting that the learning problem given by regression in the native norm can be recast as an interpolation problem, we prove our novel error bounds by using interpolation estimates. Finally, we validate our findings with numerical experiments.
New Graph and Hypergraph Container Lemmas with Applications in Property Testing

The graph and hypergraph container methods are powerful tools with a wide range of applications across combinatorics. Recently, Blais and Seth (FOCS 2023) showed that the graph container method is particularly well-suited for the analysis of the natural canonical tester for two fundamental graph properties: having a large independent set and $k$-colorability. In this work, we show that the connection between the container method and property testing extends further along two different directions. First, we show that the container method can be used to analyze the canonical tester for many other properties of graphs and hypergraphs. We introduce a new hypergraph container lemma and use it to give an upper bound of $\widetilde{O}(kq^3/\epsilon)$ on the sample complexity of $\epsilon$-testing satisfiability, where $q$ is the number of variables per constraint and $k$ is the size of the alphabet. This is the first upper bound for the problem that is polynomial in all of $k$, $q$ and $1/\epsilon$. As a corollary, we get new upper bounds on the sample complexity of the canonical testers for hypergraph colorability and for every semi-homogeneous graph partition property. Second, we show that the container method can also be used to study the query complexity of (non-canonical) graph property testers. This result is obtained by introducing a new container lemma for the class of all independent set stars, a strict superset of the class of all independent sets. We use this container lemma to give a new upper bound of $\widetilde{O}(\rho^5/\epsilon^{7/2})$ on the query complexity of $\epsilon$-testing the $\rho$-independent set property. This establishes for the first time the non-optimality of the canonical tester for a non-homogeneous graph partition property.
Large Language Models for Education: A Survey and Outlook

The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education. This survey paper summarizes the various technologies of LLMs in educational settings from multifaceted perspectives, encompassing student and teacher assistance, adaptive learning, and commercial tools. We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education. Furthermore, we outline future research opportunities, highlighting the potential promising directions. Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users

Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by combining both item-based and user-item collaborative signals. A common trend in these approaches focuses on improving ranking performance at the cost of escalating model complexity, reducing diversity, and complicating the task. It is essential to provide recommendations that are both personalized and diverse, rather than solely relying on achieving high rank-based performance, such as Click-through Rate, Recall, etc. In this paper, we propose a hybrid multi-task learning approach, training on user-item and item-item interactions. We apply item-based contrastive learning on descriptive text, sampling positive and negative pairs based on item metadata. Our approach allows the model to better understand the relationships between entities within the knowledge graph by utilizing semantic information from text. It leads to more accurate, relevant, and diverse user recommendations and a benefit that extends even to cold-start users who have few interactions with items. We perform extensive experiments on two widely used datasets to validate the effectiveness of our approach. Our findings demonstrate that jointly training user-item interactions and item-based signals using synopsis text is highly effective. Furthermore, our results provide evidence that item-based contrastive learning enhances the quality of entity embeddings, as indicated by metrics such as uniformity and alignment.
Measuring the Lense-Thirring precession and the neutron star moment of inertia with pulsars

Neutron stars (NSs) are compact objects that host the densest forms of matter in the observable universe, providing unique opportunities to study the behaviour of matter at extreme densities. While precision measurements of NS masses through pulsar timing have imposed effective constraints on the equation of state (EoS) of dense matter, accurately determining the radius or moment of inertia (MoI) of a NS remains a major challenge. This article presents a detailed review on measuring the Lense-Thirring (LT) precession effect in the orbit of binary pulsars, which would give access to the MoI of NSs and offer further constraints on the EoS. We discuss the suitability of certain classes of binary pulsars for measuring the LT precession from the perspective of binary star evolution, and highlight five pulsars that exhibit properties promising to realise these goals in the near future. Finally, discoveries of compact binaries with shorter orbital periods hold the potential to greatly enhance measurements of the MoI of NSs. The MoI measurements of binary pulsars are pivotal to advancing our understanding of matter at supranuclear densities as well as improving the precision of gravity tests, such as the orbital decay due to gravitational wave emission and of tests of alternative gravity theories.
Learning Inclusion Matching for Animation Paint Bucket Colorization

Colorizing line art is a pivotal task in the production of hand-drawn cel animation. This typically involves digital painters using a paint bucket tool to manually color each segment enclosed by lines, based on RGB values predetermined by a color designer. This frame-by-frame process is both arduous and time-intensive. Current automated methods mainly focus on segment matching. This technique migrates colors from a reference to the target frame by aligning features within line-enclosed segments across frames. However, issues like occlusion and wrinkles in animations often disrupt these direct correspondences, leading to mismatches. In this work, we introduce a new learning-based inclusion matching pipeline, which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences. Our method features a two-stage pipeline that integrates a coarse color warping module with an inclusion matching module, enabling more nuanced and accurate colorization. To facilitate the training of our network, we also develope a unique dataset, referred to as PaintBucket-Character. This dataset includes rendered line arts alongside their colorized counterparts, featuring various 3D characters. Extensive experiments demonstrate the effectiveness and superiority of our method over existing techniques.
Study of Load Shedding Procedure for Power System Voltage Stability

Compared with other measures, load shedding procedure is an efficiency method to make the power system voltage stable when the system is nearly voltage collapse because of some faults occurred. In this paper, the methods of load shedding procedures were studied for the power system voltage stability and a new method was analyzed and calculated. Through the singular value decomposition to the Jacobian matrix in the power flow calculation, the diagonal matrix of singular values and its left and right singular matrix can be solved. The maximum element in the right singular column vector of the minimum singular value corresponds the key node, and that is the best node for load shedding. By using the power system analysis software, a regional power network is simulated. The power system voltage stability margin after load shedding in this method is higher than in others. This method provides a foundation for the load shedding procedure in the critical states or the process of voltage collapse.
Cell Elasticity Is Regulated by the Tropomyosin Isoform Composition of the Actin Cytoskeleton

The actin cytoskeleton is the primary polymer system within cells responsible for regulating cellular stiffness. While various actin binding proteins regulate the organization and dynamics of the actin cytoskeleton, the proteins responsible for regulating the mechanical properties of cells are still not fully understood. In the present study, we have addressed the significance of the actin associated protein, tropomyosin (Tpm), in influencing the mechanical properties of cells. Tpms belong to a multi-gene family that form a co-polymer with actin filaments and differentially regulate actin filament stability, function and organization. Tpm isoform expression is highly regulated and together with the ability to sort to specific intracellular sites, result in the generation of distinct Tpm isoform-containing actin filament populations. Nanomechanical measurements conducted with an Atomic Force Microscope using indentation in Peak Force Tapping in indentation/ramping mode, demonstrated that Tpm impacts on cell stiffness and the observed effect occurred in a Tpm isoform-specific manner. Quantitative analysis of the cellular filamentous actin (F-actin) pool conducted both biochemically and with the use of a linear detection algorithm to evaluate actin structures revealed that an altered F-actin pool does not absolutely predict changes in cell stiffness. Inhibition of non-muscle myosin II revealed that intracellular tension generated by myosin II is required for the observed increase in cell stiffness. Lastly, we show that the observed increase in cell stiffness is partially recapitulated in vivo as detected in epididymal fat pads isolated from a Tpm3.1 transgenic mouse line. Together these data are consistent with a role for Tpm in regulating cell stiffness via the generation of specific populations of Tpm isoform-containing actin filaments.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.