189 research outputs found

    Measurement of the Ratio of the Neutron to Proton Structure Functions, and the Three-Nucleon EMC Effect in Deep Inelastic Electron Scattering Off Tritium and Helium-3 Mirror Nuclei

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    The proton and neutron structure functions F2p and F2n, respectively are fundamental to understanding many studies in nuclear physics. They provide important information about quark distributions. For example, the ratio F2n/F2p is one of the best measurements to find the ratio of d quark over u quark distribution inside the proton. While the calculations of structure functions and quark distributions are non-perturbative, they can be determined by the parameterization of experimental data. The understanding of F2n/F2p and d/u as x → 1 has a large influence on global fits and parameterization, and can be used to distinguish the non-perturbative models which give different predictions. However, F2n/F2p measured using deuteron and hydrogen targets has large nuclear uncertainties at large x, because the nuclear effects in the deuteron become significant at large x. The MARATHON experiment, which ran in spring 2018 using the upgraded 11 GeV Jefferson Lab electron beam, employs a novel method. It performed deep inelastic scattering off tritium and helium-3 mirror nuclei to measure F2n/F2p over the range x = 0.17 to x = 0.82. Since tritium and helium-3 are mirror nuclei, theoretical uncertainties largely cancel out in the ratio. The extracted F2n/F2p has much smaller uncertainties compared with previous experiments at large x. The MARATHON experiment also provided results on the EMC effect for tritium and helium-3 nuclei. The results are considered essential for understanding the EMC effect. This thesis describes the MARATHON experiment, and presents results for F2n/F2p, and the EMC effect for tritium and helium-3

    Visual SLAM based on dynamic object removal

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    Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car in the real dynamic environment. In this paper, a dynamic object removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods

    Simultaneous monocular visual odometry and depth reconstruction with scale recovery

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    In this paper, we propose a deep neural net-work that can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments

    Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

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    Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle \emph{domain-invariant} crowd and \emph{domain-specific} background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table

    The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning

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    In recent years, short Text Matching tasks have been widely applied in the fields ofadvertising search and recommendation. The difficulty lies in the lack of semantic information and word ambiguity caused by the short length of the text. Previous works have introduced complement sentences or knowledge bases to provide additional feature information. However, these methods have not fully interacted between the original sentence and the complement sentence, and have not considered the noise issue that may arise from the introduction of external knowledge bases. Therefore, this paper proposes a short Text Matching model that combines contrastive learning and external knowledge. The model uses a generative model to generate corresponding complement sentences and uses the contrastive learning method to guide the model to obtain more semantically meaningful encoding of the original sentence. In addition, to avoid noise, we use keywords as the main semantics of the original sentence to retrieve corresponding knowledge words in the knowledge base, and construct a knowledge graph. The graph encoding model is used to integrate the knowledge base information into the model. Our designed model achieves state-of-the-art performance on two publicly available Chinese Text Matching datasets, demonstrating the effectiveness of our model.Comment: 11 pages,2 figure

    Explainability for Large Language Models: A Survey

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    Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional machine learning models

    Ba6RE2Ti4O17 (RE= Nd, Sm,Gd, Dy-Yb): A family of quasi-two-dimensional triangular lattice magnets

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    Rare-earth-based triangular-lattice magnets provide the fertile ground to explore the exotic quantum magnetic state. Herein, we report a new family of RE-based triangular-lattice magnets Ba6RE2Ti4O17(RE= rare earth ions) crystallized into the hexagonal structure with space group of P63 mmc, where magnetic rare earth ions form an ideal triangular lattice within the ab-plane and stack in an AA -type fashion along the c-axis. The low-temperature magnetic susceptibility results reveal all the serial compounds have the dominant antiferromagnetic interactions and an absence of magnetic ordering down to 1.8 K. The magnetization and electron spin resonance results indicate distinct magnetic anisotropy for the compounds with different RE ions. Moreover, Ba6Nd2Ti4O17 single crystal is successfully grown and it exhibits strong Ising like anisotropy with magnetic easy-axis perpendicular to the triangle-lattice plane, being a candidate to explore quantum spin liquid state with dominant Ising-type interaction.Comment: 18 pages, 8 figure

    Reduced mouse allergen is associated with epigenetic changes in regulatory genes, but not mouse sensitization, in asthmatic children

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    Chronic exposure to mouse allergen may contribute greatly to the inner-city asthma burden. We hypothesized that reducing mouse allergen exposure may modulate the immunopathology underlying symptomatic pediatric allergic asthma, and that this occurs through epigenetic regulation. To test this hypothesis, we studied a cohort of mouse sensitized, persistent asthmatic inner-city children undergoing mouse allergen-targeted integrated pest management (IPM) vs education in a randomized controlled intervention trial. We found that decreasing mouse allergen exposure, but not cockroach, was associated with reduced FOXP3 buccal DNA promoter methylation, but this was unrelated to mouse specific IgE production. This finding suggests that the environmental epigenetic regulation of an immunomodulatory gene may occur following changing allergen exposures in some highly exposed cohorts. Given the clinical and public health importance of inner-city pediatric asthma and the potential impact of environmental interventions, further studies will be needed to corroborate changes in epigenetic regulation following changing exposures over time, and determine their impact on asthma morbidity in susceptible children

    Single crystal growth and superconductivity in RbNi2_2Se2_2

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    We report the synthesis and characterization of RbNi2_2Se2_2, an analog of the iron chalcogenide superconductor Rbx_xFe2_2Se2_2, via transport, angle resolved photoemission spectroscopy, and density functional theory calculations. A superconducting transition at TcT_{c} = 1.20 K is identified. In normal state, RbNi2_2Se2_2 shows paramagnetic and Fermi liquid behaviors. A large Sommerfeld coefficient yields a heavy effective electron mass of m6mem^{*}\approx6m_{e}. In the superconducting state, zero-field electronic specific-heat data CesC_{es} can be described by a two-gap BCS model, indicating that RbNi2_2Se2_2 is a multi-gap superconductor. Our density functional theory calculations and angle resolved photoemission spectroscopy measurements demonstrate that RbNi2_2Se2_2 exhibits relatively weak correlations and multi-band characteristics, consistent with the multi-gap superconductivity.Comment: 7 pages, 4 figure

    3D bioprinted hydrogel/polymer scaffold with factor delivery and mechanical support for growth plate injury repair

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    Introduction: Growth plate injury is a significant challenge in clinical practice, as it could severely affect the limb development of children, leading to limb deformity. Tissue engineering and 3D bioprinting technology have great potential in the repair and regeneration of injured growth plate, but there are still challenges associated with achieving successful repair outcomes.Methods: In this study, GelMA hydrogel containing PLGA microspheres loaded with chondrogenic factor PTH(1–34) was combined with BMSCs and Polycaprolactone (PCL) to develop the PTH(1–34)@PLGA/BMSCs/GelMA-PCL scaffold using bio-3D printing technology.Results: The scaffold exhibited a three-dimensional interconnected porous network structure, good mechanical properties, biocompatibility, and was suitable for cellchondrogenic differentiation. And a rabbit model of growth plate injury was appliedto validate the effect of scaffold on the repair of injured growth plate. The resultsshowed that the scaffold was more effective than injectable hydrogel in promotingcartilage regeneration and reducing bone bridge formation. Moreover, the addition ofPCL to the scaffold provided good mechanical support, significantly reducing limbdeformities after growth plate injury compared with directly injected hydrogel.Discussion: Accordingly, our study demonstrates the feasibility of using 3D printed scaffolds for treating growth plate injuries and could offer a new strategy for the development of growth plate tissue engineering therapy
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