相关论文: A multi-instance deep neural network classifier: a…
In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed. Multi-label classification is a superset of traditional binary and multi-class…
Studies of Higgs boson properties are crucial for the understanding the Standard Model (SM), as it could couple to new particles and provide hints to physics Beyond the Standard Model (BSM). Different future colliders are proposed, among…
Two simple ways by which the standard signals of the Standard Model Higgs boson can be depleted are: its couplings to fermions and gauge bosons can be suppressed by a universal factor, and part of its branching fraction can be drained into…
In this study, we focus on the bosonic decays of light charged Higgs boson (i.e., with $M_{H^\pm}<m_t$) in the 2-Higgs Doublet Model (2HDM) Type-I. To study the signal of such a charged Higgs state at the Large Hadron Collider (LHC), in a…
This study proposes a Newton based multiple objective optimization algorithm for hyperparameter search. The first order differential (gradient) is calculated using finite difference method and a gradient matrix with vectorization is formed…
The multiple point principle (MPP) is applied to the non--supersymmetric two-Higgs doublet extension of the Standard Model (SM). The existence of a large set of degenerate vacua at some high energy scale caused by the MPP results in a few…
A search for Standard Model Higgs bosons produced in association with a high-energy photon and decaying to $b\bar{b}$ is performed using 133 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collision data collected with the ATLAS detector at the Large…
The differential branching ratio, forward-backward asymmetry, CP asymmetry and lepton polarization for a B-meson to decay to strange hadronic final states and a $\tau^+\tau^-$ pair in a CP softly broken two Higgs doublet model are computed.…
We study a multiclass multiple instance learning (MIL) problem where the labels only suggest whether any instance of a class exists or does not exist in a training sample or example. No further information, e.g., the number of instances of…
An observer-based Hamiltonian identification algorithm for quantum systems is proposed. For the 2-level case an exponential convergence result based on averaging arguments and some relevant transformations is provided. The convergence for…
Higgs boson self-interactions can be investigated via di-Higgs ($pp\to hh+X$) production at the LHC. With a small ${\cal{O}}(30)$ fb Standard Model production cross section, and a large $t\bar t$ background, this measurement has been…
The proposed future $e^- p$ collider provides sufficient energies to produce the Standard Model Higgs Boson ($h$) through $W^\pm$ and $Z$-Boson fusion in charged and neutral current modes, respectively and to measure its properties. We take…
A precise measurement of the Higgs boson couplings to bottom and top quarks is of paramount importance during the upcoming LHC runs. We present a comprehensive analysis for the Higgs production process in association with a…
The Equivalence Theorem is commonly used to calculate perturbatively amplitudes involving gauge bosons at energy scales higher than gauge boson masses. However, when the scalar sector is strongly interacting the theory is non-perturbative.…
We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the…
One of the cleanest signatures of a heavy Higgs boson in models with vectorlike leptons is $H\to e_4^\pm \ell^\mp \to h\ell^+\ell^-$ which, in two Higgs doublet model type-II, can even be the dominant decay mode of heavy Higgses. Among the…
Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An…
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to data from different vendors, their performance tends to drop…
A summary of the methods used to make a precision measurement of the Higgs boson mass is presented. The final mass value for the Higgs boson is measured to be $m_{\rm H} = 125.26 \pm 0.21\ \mathrm{GeV}$. This analysis considers the…
Several performance measures are used to evaluate binary and multiclass classification tasks. But individual observations may often have distinct weights, and none of these measures are sensitive to such varying weights. We propose a new…