346 research outputs found

    Searches for exotic Higgs boson decays with the CMS experiment

    No full text
    Searches for exotic decays of the 125 GeV Higgs boson with the CMS experiment are presented. Three classes of searches are discussed 1) the Higgs boson decays to a Z boson and a light pseudoscalar, 2) the Higgs boson decays to a pair of light pseudoscalars, and 3) invisible decays of the Higgs boson. These searches are based on proton-proton collision data at s=13\sqrt{s}=13 TeV collected in Run 2 of the LHC

    Deep Encoder Cross Network for Estimated Time of Arrival

    No full text
    Estimated time of arrival (ETA) is essential to enable various intelligent transportation services and reduce passenger waiting time. Estimating the time of arrival of public transport in a highly dynamic and uncertain transportation system could be challenging. Many indirect factors beyond the remaining travel distance could dramatically deviate the time of arrival from the original schedule. Existing distance-based estimation methods disregarding those factors usually result in inaccurate estimations. In this paper, we propose a new deep learning model, called Deep Encoder Cross Network (DECN), to improve the ETA prediction based on multiple non-distance-based factors such as weather, road speed and congestion, and traffic composition. Unlike most regression tasks that output the target directly, we predict the ETA residual over the location-based ETA prediction. To effectively learn in the large and sparse input feature space, we use a new neural network structure consisting of three main components. First, a deep neural network is responsible for modeling explicit feature interactions. Second, an encoder network is constructed to reduce the input feature dimensionality. Third, a cross-network is introduced to learn from the implicit feature interactions. We conduct extensive experiments on a large real-world bus ETA dataset of Hong Kong, which contains about 2.95Ă—1082.95\times 10^{8} rows with 27 different features on an 84-dimensional space. The results show that the deep learning approach with the DECN model can improve the ETA error by 11% on average, and 49% for late arrival. The proposed approach can be further improved and extended to estimate other traffic information by incorporating non-distance-based related information

    Symbolic Regression on FPGAs for Fast Machine Learning Inference

    No full text
    The high-energy physics community is investigating the feasibility of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to improve physics sensitivity while meeting data processing latency limitations. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches equation space to discover algebraic relations approximating a dataset. We use PySR (software for uncovering these expressions based on evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimise the top metric by pinning the network size because vast hyperparameter space prevents extensive neural architecture search. Conversely, SR selects a set of models on the Pareto front, which allows for optimising the performance-resource tradeoff directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our procedure on a physics benchmark: multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider, and show that we approximate a 3-layer neural network with an inference model that has as low as 5 ns execution time (a reduction by a factor of 13) and over 90% approximation accuracy

    SRSF5 Regulates the Expression of BQ323636.1 to Modulate Tamoxifen Resistance in ER-Positive Breast Cancer

    No full text
    About 70% of breast cancer patients are oestrogen receptor-positive (ER +ve). Adjuvant endocrine therapy using tamoxifen (TAM) is an effective approach for preventing local recurrence and metastasis. However, around half of the patients will eventually develop resistance. Overexpression of BQ323636.1 (BQ) is one of the mechanisms that confer TAM resistance. BQ is an alternative splice variant of NCOR2. The inclusion of exon 11 generates mRNA for NCOR2, while the exclusion of exon 11 produces mRNA for BQ. The expression of SRSF5 is low in TAM-resistant breast cancer cells. Modulation of SRSF5 can affect the alternative splicing of NCOR2 to produce BQ. In vitro and in vivo studies confirmed that the knockdown of SRSF5 enhanced BQ expression, and conferred TAM resistance; in contrast, SRSF5 overexpression reduced BQ expression and, thus, reversed TAM resistance. Clinical investigation using a tissue microarray confirmed the inverse correlation of SRSF5 and BQ. Low SRSF5 expression was associated with TAM resistance, local recurrence and metastasis. Survival analyses showed that low SRSF5 expression was associated with poorer prognosis. We showed that SRPK1 can interact with SRSF5 to phosphorylate it. Inhibition of SRPK1 by a small inhibitor, SRPKIN-1, suppressed the phosphorylation of SRSF5. This enhanced the proportion of SRSF5 interacting with exon 11 of NCOR2, reducing the production of BQ mRNA. As expected, SRPKIN-1 reduced TAM resistance. Our study confirms that SRSF5 is essential for BQ expression. Modulating the activity of SRSF5 in ER +ve breast cancer will be a potential approach to combating TAM resistance

    Plasma high sensitivity troponin T levels in adult survivors of childhood leukaemias: determinants and associations with cardiac function.

    Get PDF
    We sought to quantify plasma high sensitivity cardiac troponin (hs-cTnT) levels, their determinants, and their associations with left ventricular (LV) myocardial deformation in adult survivors of childhood acute leukaemias.One hundred adult survivors (57 males) of childhood acute leukaemias, aged 24.1 ± 4.2 years, and 42 age-matched controls (26 males) were studied. Plasma cTnT was determined using a highly sensitive assay. Genotyping of NAD(P)H oxidase and multidrug resistance protein polymorphisms was performed. Left ventricular function was assessed by conventional, three-dimensional, and speckle tracking echocardiography. The medians (interquartile range) of hs-cTnT in male and female survivors were 4.9 (4.2 to 7.2) ng/L and 1.0 (1.0 to 3.5) ng/L, respectively. Nineteen survivors (13 males, 6 females) (19%) had elevated hs-cTnT (>95(th) centile of controls). Compared to those without elevated hs-TnT levels, these subjects had received larger cumulative anthracycline dose and were more likely to have leukaemic relapse, stem cell transplant, and cardiac irradiation. Their LV systolic and early diastolic myocardial velocities, isovolumic acceleration, and systolic longitudinal strain rate were significantly lower. Survivors having CT/TT at CYBA rs4673 had higher hs-cTnT levels than those with CC genotype. Functionally, increased hs-cTnT levels were associated with worse LV longitudinal systolic strain and systolic and diastolic strain rates.Increased hs-cTnT levels occur in a significant proportion of adult survivors of childhood acute leukaemias and are associated with larger cumulative anthracycline dose received, history of leukaemic relapse, stem cell transplant, and cardiac irradiation, genetic variants in free radical metabolism, and worse LV myocardial deformation

    Comparisons of echocardiographic findings between survivors with (group I) and without (group II) elevated hs-cTnT levels.

    No full text
    <p>Abbreviations: A, peak mitral inflow velocity at late diastole; a, mitral annular late diastolic myocardial tissue velocity; E, peak mitral inflow velocity at early diastole; e, mitral annual early diastolic myocardial tissue velocity; EF, ejection fraction; FS, fractional shortening; IVA, isovolumic acceleration; LV, left ventricular; LVEDd, left ventricular end diastolic dimension; LVESd, left ventricular end systolic dimension; SR<sub>d</sub>, early diastolic strain rate; SR<sub>s</sub>, systolic strain rate.</p>*<p>statistically significant.</p

    Relationships between high sensitivity cardiac troponin T (hs-cTnT) and indices of left ventricular (LV) deformation.

    No full text
    <p>Correlations between hs-cTnT and LV (a) global longitudinal systolic strain, (b) systolic strain rate, and (c) diastolic strain rate are shown. Solid lines represent the best-fit linear regression and dashed lines represent the 95% confidence interval for each parameter of the regression line.</p

    Comparisons of demographic and echocardiographic findings between survivors and control subjects.

    No full text
    <p>Abbreviations: A, peak mitral inflow velocity at late diastole; a, mitral annular late diastolic myocardial tissue velocity; E, peak mitral inflow velocity at early diastole; e, mitral annual early diastolic myocardial tissue velocity; EF, ejection fraction; FS, fractional shortening; IVA, isovolumic acceleration; LV, left ventricular; LVEDd, left ventricular end diastolic dimension; LVESd, left ventricular end systolic dimension; SR<sub>d</sub>, early diastolic strain rate; SR<sub>s</sub>, systolic strain rate.</p>*<p>statistically significant.</p
    • …
    corecore