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  1. Abstract Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D 0 D 0 π + mass spectrum just below the D *+ D 0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalarmore »$${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + tetraquark with a quark content of $${{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}\overline{{{{{{\rm{u}}}}}}}\overline{{{{{{\rm{d}}}}}}}$$ c c u ¯ d ¯ and spin-parity quantum numbers J P  = 1 + . Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D *+ mesons is consistent with the observed D 0 π + mass distribution. To analyse the mass of the resonance and its coupling to the D * D system, a dedicated model is developed under the assumption of an isoscalar axial-vector $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state decaying to the D * D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state. In addition, an unexpected dependence of the production rate on track multiplicity is observed.« less
    Free, publicly-accessible full text available December 1, 2023
  2. The increased focus on computational thinking (CT) has grown in recent years for various reasons, such as a general concern about (a) a lack of global competitiveness among American students and general literacy in science, technology, engineering, and math (STEM) fields (Hsu & Cardella, 2013), (b) maintaining the economic competitiveness of the U.S. (Yadav, Hong, & Stephenson, 2016), and (c) preparing students adequately for a society that is increasingly technological (NRC, 2011). CT can help individuals analyze and understand multiple dimensions of a complex problem and identify and apply appropriate tools or techniques to address a complex problem (Wing, 2010).more »Furthermore, children can benefit from improved technological literacy, content knowledge, and problem-solving skills (Hsu & Cardella, 2013) while practicing CT.« less
  3. Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is in- formative and important to understand motion mechanisms of body regions. Modeling such in- formation into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN that unitizes deep neural networks with motion in- formation to improve reconstruction quality. The central idea is to decomposemore »the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruc- tion, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.« less
  4. We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can prac- tically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dom- inate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise fea- tures equally, which results in degraded representation ability of the neural network. To solve this problem, we propose amore »new model called MRI Cascaded Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a variant of U-net with Channel-wise Attention (UCA) mod- ule, (ii) a long skip connection and (iii) a combined loss. Our model is able to attend to salient information by filtering irrelevant features and also concentrate on high-frequency in- formation by enforcing low-frequency information bypassed to the final output. We conduct both quantitative evaluation and qualitative analysis of our method on a cardiac dataset. The experiment shows that our method achieves very promis- ing results in terms of three common metrics on the MRI reconstruction with low undersampled k-space data. Code is public available« less
  5. This work in progress describes the design of a project-based, STEM +C (Computing) curriculum for 4th to 6th grade students in an afterschool setting, which is part of a large NSF-funded STEM+C project. The paper reports the preliminary outcome of the implementation of the first two STEM+C projects that focuses on student attitudes toward STEM and the computational thinking revealed during students’ scientific inquiry and problem solving processes.
  6. The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been inves- tigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mix- ture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel ap- proach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, amore »2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi- component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, ro- bustness, and efficiency of our approach for the slices ac- quired at different locations and different cardiac phases.« less
  7. Abstract Conventional, hadronic matter consists of baryons and mesons made of three quarks and a quark–antiquark pair, respectively 1,2 . Here, we report the observation of a hadronic state containing four quarks in the Large Hadron Collider beauty experiment. This so-called tetraquark contains two charm quarks, a $$\overline{{{{{u}}}}}$$ u ¯ and a $$\overline{{{{{d}}}}}$$ d ¯ quark. This exotic state has a mass of approximately 3,875 MeV and manifests as a narrow peak in the mass spectrum of D 0 D 0 π + mesons just below the D *+ D 0 mass threshold. The near-threshold mass together with the narrow widthmore »reveals the resonance nature of the state.« less
    Free, publicly-accessible full text available July 1, 2023
  8. A bstract A precision measurement of the Z boson production cross-section at $$ \sqrt{\mathrm{s}} $$ s = 13 TeV in the forward region is presented, using pp collision data collected by the LHCb detector, corresponding to an integrated luminosity of 5.1 fb − 1 . The production cross-section is measured using Z → μ + μ − events within the fiducial region defined as pseudorapidity 2 . 0 < η < 4 . 5 and transverse momentum p T > 20 GeV /c for both muons and dimuon invariant mass 60 < M μμ < 120 GeV /c 2 .more »The integrated cross-section is determined to be $$ \sigma \left(Z\to {\mu}^{+}{\mu}^{-}\right)=196.4\pm 0.2\pm 1.6\pm 3.9\ \mathrm{pb}, $$ σ Z → μ + μ − = 196.4 ± 0.2 ± 1.6 ± 3.9 pb , where the first uncertainty is statistical, the second is systematic, and the third is due to the luminosity determination. The measured results are in agreement with theoretical predictions within uncertainties.« less
    Free, publicly-accessible full text available July 1, 2023