H. Qu

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a ``particle cloud.'' Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

A bstract The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.

Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving the performance of deep learning in many applications, Lorentz group equivariance - a fundamental spacetime symmetry for elementary particles - has recently been incorporated into a deep learning model for jet tagging. However, the design is computationally costly due to the analytic construction of high-order tensors. In this article, we introduce LorentzNet, a new symmetry-preserving deep learning model for jet tagging. The message passing of LorentzNet relies on an efficient Minkowski dot product attention. Experiments on two representative jet tagging benchmarks show that LorentzNet achieves the best tagging performance and improves significantly over existing state-of-the-art algorithms. The preservation of Lorentz symmetry also greatly improves the efficiency and generalization power of the model, allowing LorentzNet to reach highly competitive performance when trained on only a few thousand jets. Code and models are available at \url{https://github.com/sdogsq/LorentzNet-release}.

Abstract Background work alienation is receiving increasing attention as a psychological risk at work, and little is known about the mechanisms of role ambiguity and work alienation in nurses in the context of the COVID-19 pandemic. This article aims to examine how role ambiguity affects work alienation among Chinese nurses during the two years after COVID-19 pandemic and verify emotional exhaustion as mediators. Methods A cross-sectional study design was used to recruit 281 Chinese nurses. Nurses completed online questionnaires containing demographic characteristics, role ambiguity, emotional exhaustion, and work alienation, and SPSS 26.0 and AMOS 24.0 were used for data analysis and structural equation modelling. Results work alienation scores were (34.64 ± 10.09), work alienation was correlated with role ambiguity and emotional exhaustion ( r 1 = 0.521, r 2 = 0.755; p < .01), and role ambiguity was positively correlated with emotional exhaustion ( r = 0.512; p < .01). A mediating effect of emotional exhaustion between role ambiguity and work alienation held (mediating effect of 0.288, 95% CI: 0.221–0.369, accounting for 74.8% of the total effect). Conclusion Role ambiguity has a significant direct effect on nurses’ feelings of alienation and exacerbates alienation through emotional exhaustion. Clarifying roles at work and being less emotionally drained are effective ways to reduce nurses’ feelings of alienation.

We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z <$1.13, which is the largest sample of supernovae from any single survey and increases the number of known $z>0.5$ supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are $\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=$0.017 in a flat $\Lambda$CDM model, and $(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.

The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast event and detector simulation in high energy physics have shown exceptional performance, providing a viable solution to generate sufficient statistics within a constrained computational budget in preparation for the High Luminosity LHC. However, many of these applications suffer from slow generation with large sampling steps and face challenges in finding the optimal balance between sample quality and speed. The study focuses on the latest benchmark developments in efficient ODE/SDE-based samplers, schedulers, and fast convergence training techniques. We test on the public CaloChallenge and JetNet datasets with the designs implemented on the existing architecture, the performance of the generated classes surpass previous models, achieving significant speedup via various evaluation metrics.

Abstract Quarks (except top quarks) and gluons produced in collider experiments hadronize and fragment into sprays of stable particles, called jets. Identification of quark flavor is desired for collider experiments in high-energy physics, relying on flavor tagging algorithms. In this study, using a full simulation of the Circular Electron Positron Collider (CEPC), we investigate the flavor tagging performance of two different algorithms: ParticleNet, based on a Graph Neural Network, and LCFIPlus, based on the Gradient Booted Decision Tree. Compared to LCFIPlus, ParticleNet significantly enhances flavor tagging performance, resulting in a significant improvement in benchmark measurement accuracy, i.e., a 36% improvement for $$\sigma (ZH)\cdot Br(Z\rightarrow \nu \bar{\nu }, H\rightarrow c\bar{c})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>Z</mml:mi> <mml:mi>H</mml:mi> <mml:mo>)</mml:mo> <mml:mo>·</mml:mo> <mml:mi>B</mml:mi> <mml:mi>r</mml:mi> <mml:mo>(</mml:mo> <mml:mi>Z</mml:mi> <mml:mo>→</mml:mo> <mml:mi>ν</mml:mi> <mml:mover> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> <mml:mo>,</mml:mo> <mml:mi>H</mml:mi> <mml:mo>→</mml:mo> <mml:mi>c</mml:mi> <mml:mover> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> measurement and a 75% improvement for $$|V_{cb}|$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>|</mml:mo> </mml:mrow> <mml:msub> <mml:mi>V</mml:mi> <mml:mrow> <mml:mi>cb</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>|</mml:mo> </mml:mrow> </mml:mrow> </mml:math> measurement via W boson decay, respectively, when the CEPC operates as a Higgs factory at the center-of-mass energy of 240 GeV and collects an integrated luminosity of 5.6 ab $$^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> . We compare the performance of ParticleNet and LCFIPlus at different vertex detector configurations, observing that the inner radius is the most sensitive parameter, followed by material budget and spatial resolution.

In the deep learning era, improving the neural network performance in jet physics is a rewarding task, as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking it into account. We conduct a detailed study on the Lorentz-symmetric design. We propose two generalized approaches for modifying a network---these methods are experimented on Particle Flow Network, ParticleNet, and LorentzNet and exhibit a general performance gain. We also reveal that the notable improvement attributed to the ``pairwise mass'' feature in the network is due to its introduction of a structure that fully complies with Lorentz symmetry. We confirm that Lorentz-symmetry preservation serves as a strong inductive bias of jet physics, hence calling for attention to such general recipes in future network designs.

Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD). With advancements in deep learning, particularly in Convolutional Neural Networks (CNNs) and the Xception network architecture, we are now able to analyze and classify vast amounts of MRI data with unprecedented accuracy. The progress of this technology not only enhances our understanding of brain structural changes but also opens up new avenues for monitoring disease progression through non-invasive means and potentially allows for precise diagnosis in the early stages of the disease. This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease through a series of innovative data processing and model construction steps. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task, demonstrating its potential application value in assistive diagnosis. Future research will focus on expanding the dataset, improving model interpretability, and clinical validation to further promote the application of deep learning technology in the medical field, with the hope of bringing earlier diagnosis and more personalized treatment plans to Alzheimer Disease patients.

Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimensional tabular data and accurately capturing their correlations are often quite challenging, even with the most advanced architectures. Based on the findings that tree-based models surpass the performance of deep learning models for tasks specific to tabular data, we adopt the very recent generative modeling class named conditional flow matching and employ different techniques to integrate the usage of Gradient Boosted Trees. The performances are evaluated for various tasks on different analysis level with several public datasets. We demonstrate the training and inference time of most high-level simulation tasks can achieve speedup by orders of magnitude. The application can be extended to low-level feature simulation and conditioned generations with competitive performance.

Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude larger than existing public datasets. A total of 10 types of jets are simulated, including several types unexplored for tagging so far. Based on the large dataset, we propose a new Transformer-based architecture for jet tagging, called Particle Transformer (ParT). By incorporating pairwise particle interactions in the attention mechanism, ParT achieves higher tagging performance than a plain Transformer and surpasses the previous state-of-the-art, ParticleNet, by a large margin. The pre-trained ParT models, once fine-tuned, also substantially enhance the performance on two widely adopted jet tagging benchmarks. The dataset, code and models are publicly available at https://github.com/jet-universe/particle_transformer.

Triple gauge boson production at the LHC can be used to test the robustness of the Standard Model and provide useful information for VBF di-boson scattering measurement. Especially, any derivations from SM prediction will indicate possible new physics. In this paper we present a detailed Monte Carlo study on measuring WWW production in pure leptonic and semileptonic decays, and probing anomalous quartic gauge WWWW couplings at the CERN LHC and future hadron collider, with parton shower and detector simulation effects taken into account. Apart from cut-based method, multivariate boosted decision tree method has been exploited for possible improvement. For the leptonic decay channel, our results show that at the sqrt{s}=8(14)[100] TeV pp collider with integrated luminosity of 20(100)[3000] fb-1, one can reach a significance of 0.4(1.2)[10]sigma to observe the SM WWW production. For the semileptonic decay channel, one can have 0.5(2)[14]sigma to observe the SM WWW production. We also give constraints on relevant Dim-8 anomalous WWWW coupling parameters.

We propose a new mixing pattern for neutrinos with a nonzero mixing angle $\theta_{13}$. Under a simple form, it agrees well with current neutrino oscillation data and displays a number of intriguing features including the $\mu$-$\tau$ interchange symmetry $|U_{\mu i}|=|U_{\tau i}|$, $(i=1,2,3)$, the trimaximal mixing $|U_{\e 2}|=|U_{\mu 2}|=|U_{\tau 2}|=1/\sqrt{3}$, the self-complementarity relation $\theta_1+\theta_3=45\deg$, together with the maximal Dirac CP violation as a prediction.

Landslide disasters are extremely destructive. Accurate identification of landslides plays an important role in disaster assessment, loss control and post-disaster reconstruction. This paper proposes a semantic segmentation landslide identification method based on improved U-Net. The deep convolution neural network and jump connection method is used for end-to-end semantic segmentation to achieve deep feature extraction and fusion of different receptive fields, thus enriching feature information. SENet modules are adopted to enhance the ability of the model to extract important features, so as to further improve the accuracy of model recognition. Extensive experiments show that our improved U-Net achieves better performance than the original algorithm on our landslide datasets. The results of Iou are improved by 4.12% which demonstrates our work is of great significance for the research of landslide area identification. Finally, the model is deployed to the web and applied to the geological hazard intelligent monitoring system to realize the landslide identification task.

Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the flavor tagging algorithm. In this study, using a full simulation of the Circular Electron Positron Collider (CEPC), we investigated the flavor tagging performance of two different algorithms: ParticleNet, originally developed at CMS, and LCFIPlus, the current flavor tagging algorithm employed at CEPC. Compared to LCFIPlus, ParticleNet significantly enhances flavor tagging performance, resulting in a significant improvement in benchmark measurement accuracy, i.e., a 36% improvement for $\nu\bar{\nu}H\to c\bar{c}$ measurement and a 75% improvement for $|V_{cb}|$ measurement via W boson decay when CEPC operates as a Higgs factory at the center-of-mass energy of 240 GeV and integrated luminosity of 5.6 $ab^{-1}$. We compared the performance of ParticleNet and LCFIPlus at different vertex detector configurations, observing that the inner radius is the most sensitive parameter, followed by material budget and spatial resolution.

To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet origin identification that categorizes jets into 5 quark species $(b,c,s,u,d)$, 5 anti-quarks $(\bar{b},\bar{c},\bar{s},\bar{u},\bar{d})$, and gluon. Using state-of-the-art algorithms and simulated $\nu\bar{\nu}H, H\rightarrow jj$ events at 240~GeV center-of-mass energy at the electron-positron Higgs factory, the jet origin identification simultaneously reaches jet flavor tagging efficiencies of 92\%, 79\%, 67\%, 37\%, and 41\% and jet charge flip rates of 18\%, 7\%, 15\%, 15\%, and 19\% for $b$, $c$, $s$, $u$, and $d$ quarks, respectively. We apply the jet origin identification to Higgs rare and exotic decay measurements at the nominal luminosity of the Circular Electron Positron Collider (CEPC), and conclude that the upper limits on the branching ratios of $H\rightarrow s \bar{s}, u\bar{u}, d\bar{d}$, and $H\rightarrow sb, db, uc, ds$ can be determined to $2\!\!\times\!\!10^{-4}$ to $1\!\!\times\!\!10^{-3}$ at 95\% confidence level. The derived upper limit for $H\rightarrow s \bar{s}$ decay is approximately three times the prediction of the Standard Model.

We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach---which, unlike competing algorithms, is trivial to implement---improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.

With the changes in the global macroeconomic situation and the adjustment of the industrial structure, it is necessary to carry out the research of the benchmark price prediction technology forecasting technology. This article uses long -term memory models to predict the price of materials to adapt to the new industrial structure and improve the price predictive ability of material prices. After that, the material benchmark price prediction experiments were conducted. The experimental results showed that the prediction results of the long and short memory model could meet the ability to improve the benchmark price prediction.

Apalutamide is a novel competitive inhibitor of the androgen receptor for the treatment of non-metastatic castration-resistant prostate cancer or metastatic castration-sensitive prostate cancer. Rash is the most common skin adverse reaction of apalutamide. If rash is paid insufficient attention, further developing life-threatening Stevens–Johnson Syndrome/Toxic Epidermal Necrolysis. Here, we reported a case of toxic epidermal necrolysis caused by apalutamide. An 86-year-old male patient developed a Nikolsky-positive maculopapular rash, skin exfoliation and mucosal erosion involving 60% body surface area 24 days after treatment of oral apalutamide (180mg/d) for prostate cancer with bone metastases. The skin manifestations aggravated after discontinuation of apalutamide and treatment of methylprednisolone (0.8mg/kg/d) and immunoglobulin (400mg/kg/d) for 2 days. Then, clinician increased the dose of methylprednisolone and immunoglobulin. The skin symptoms improved after treatment of methylprednisolone (1.2mg/kg/d) and immunoglobulin (600mg/kg/d) for 5 days. This is the first to report a dose-dependent response to methylprednisolone and immunoglobulin in the treatment of apalutamide-caused toxic epidermal necrolysis. Since the number of prostate cancer patients treated with apalutamide increases, it is necessary to summarize and analyze the clinical characteristics and treatment experience in cases of severe skin adverse reactions caused by apalutamide.

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.

To solve the expression difficulties of the basic principle of asynchronous motor, and the faults to understand the relevant knowledge not fully, the asynchronous motor intuitive to express the basic principle of the experiment device was designed by analyzing the existing mechanical structures of asynchronous motor.The three-dimensional modeling was finished for the mechanical structure of the asynchronous motor principle experiment device.The experimental device is mainly divided into the base part design, speed display design and the basic principle of rotating demonstration part design.Through asynchronous motor principle experiment device can verify and demonstrate the rotation principle of the asynchronous motor, thereby facilitating scholars to understand.

The thesis put forward a new global variable prediction model whose theories, structure of modeling and operation were discussed.Applying BP neural network in solid waste dry fermentation garage environment control, the reliability and stability of the new control method was proved.

Cardiac troponin I-interacting kinase (TNNI3K) is a cardiac-specific kinase that has been identified as a diagnostic marker and a therapeutic target in cardiovascular diseases. However, the biological function of TNNI3K in cardiac dysfunction and remodelling remains elusive. In the present study, a Tnni3k cardiomyocyte-specific knockout (Tnni3k-cKO) mouse model was established. Echocardiography was used to evaluate cardiac function in mice. Heart failure markers were detected using enzyme-linked immunosorbent assay. Haematoxylin and eosin staining, wheat germ agglutinin staining, Masson's trichrome staining, Sirius red staining and terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) staining were used to assess histopathological changes, cardiac hypertrophy, collagen deposition and myocardial apoptosis, respectively. Expression levels of TNNI3K, apoptosis-related proteins, and p38 mitogen-activated protein kinase were measured using Western blot analysis. Compared to wild-type controls, cardiac dysfunction and cardiac remodelling of Tnni3k-cKO mice increased gradually with age. Tnni3k-cKO mice exhibited cardiac hypertrophy, cardiac fibrosis and cardiomyocyte apoptosis. Upregulation of cleaved caspase-3 in Tnni3k-cKO mice appeared to be related to phosphorylation and activation of the p38 mitogen-activated protein kinase signalling pathway. In conclusion, this study shows that TNNI3K is essential for cardiac development and function, providing new insights into the development of novel therapeutic strategies for cardiac diseases.

In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking it into account. We conduct a detailed study on the Lorentz-symmetric design. We propose two generalized approaches for modifying a network - these methods are experimented on Particle Flow Network, ParticleNet, and LorentzNet, and exhibit a general performance gain. We also reveal that the notable improvement attributed to the "pairwise mass" feature in the network is due to its introduction of a structure that fully complies with Lorentz symmetry. We confirm that Lorentz-symmetry preservation serves as a strong inductive bias of jet physics, hence calling for attention to such general recipes in future network designs.

We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach - which, unlike competing algorithms, is trivial to implement - improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.

JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper Particle Transformer for Jet Tagging. An interface to use the dataset is provided in https://github.com/jet-universe/particle_transformer.

JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper Particle Transformer for Jet Tagging. An interface to use the dataset is provided in https://github.com/jet-universe/particle_transformer.

Author(s): Qu, Huilin | Advisor(s): Incandela, Joseph R. | Abstract: Measurement of the decay of the Higgs boson to charm quarks provides a direct probe of the Higgs coupling to second-generation quarks. Therefore, it is crucial for understanding the structure of Yukawa couplings. In this thesis, a search for the Higgs boson decaying to charm quarks with the CMS experiment is presented. The search is designed for Lorentz-boosted Higgs bosons produced in association with vector (V) bosons (W or Z bosons). A novel approach that reconstructs both quarks from the Higgs boson decay with a single large-radius jet is adopted. The charm quark pair is identified with an advanced deep learning--based algorithm. This approach leads to a highly competitive result: Using proton-proton collision data corresponding to an integrated luminosity of 35.9 fb-1, an observed (expected) upper limit on the VH cross section times the H-gcc branching fraction of 71 (49) times the standard model expectation at 95% confidence level is obtained.A detailed description of the deep learning--based boosted object identification algorithm is also presented in this thesis. It is a versatile algorithm designed to identify and classify hadronic decays of highly Lorentz-boosted top quarks and W, Z, Higgs bosons. Using deep neural networks to directly access and process the raw information of all constituent particle-flow candidates of a jet, this advanced algorithm has achieved significant performance improvements compared to traditional approaches.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.

Backgrounds: Dihydroartemisinin-piperaquine is a current frontline drug recommended in global by WHO for the treatment of Plasmodium falciparum malaria (WHO, 2015), but is now failing in Vietnam provinces where border Cambodia, and has emerged and spread.The purpose of this study was to evaluate efficacy and molecular markers of dihydroartemisinin-piperaquine failures in Dak Lal province. Methods:A study design of non-randomized controlled study design for the 42 day-course follow-up in vivo test, and the molecular markers analysis. Findings:The data showed that adequate clinical and parasitological response was sharply declined of 12,1%, late clinical failure of 51.5%, late parasitological failure of 36.4%, proportion of positive parasitemia at D3 is 37%, slope half-life of 5.36 hrs, and progressive parasite clearance PC50, PC75, PC 90, PC95, and PC99 were 13.24; 19.29; 25.69; 29.97 and 39.15 hrs, respectively.Molecular markers of C580Y Kelch mutation observed 100% (50/50) in the patients, increased of Plasmepsine 2 CNV of 72% (36/50), and both K13 and Plasmepsine 2 of 72% (36/50). Conclusions:The DHA-PPQ efficacy severely decreased of 12.1%, overall treatment failure of 87.9% with the prominent C580Y mutant plus increased Plasmepsine 2 copy number variation in delayed asexual P. falciparum parasite clearance.These obvious data need to urgently change antimalarial policy in DHA-PPQ resistance zones.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.