Probing triple-Higgs productions via 4b2γ4b2\gamma decay channel at a 100 TeV hadron collider

The quartic self-coupling of the Standard Model Higgs boson can only be measured by observing the triple-Higgs production process, but it is challenging for the Large Hadron Collider (LHC) Run 2 or International Linear Collider (ILC) at a few TeV because of its extremely small production rate. In this paper, we present a detailed Monte Carlo simulation study of the triple-Higgs production through gluon fusion at a 100 TeV hadron collider and explore the feasibility of observing this production mode. We focus on the decay channel $HHH\rightarrow b\bar{b}b\bar{b}\gamma\gamma$, investigating detector effects and optimizing the kinematic cuts to discriminate the signal from the backgrounds. Our study shows that, in order to observe the Standard Model triple-Higgs signal, the integrated luminosity of a 100 TeV hadron collider should be greater than $1.8\times 10^4$ ab$^{-1}$. We also explore the dependence of the cross section upon the trilinear ($\lambda_3$) and quartic ($\lambda_4$) self-couplings of the Higgs. We find that, through a search in the triple-Higgs production, the parameters $\lambda_3$ and $\lambda_4$ can be restricted to the ranges $[-1, 5]$ and $[-20, 30]$, respectively. We also examine how new physics can change the production rate of triple-Higgs events. For example, in the singlet extension of the Standard Model, we find that the triple-Higgs production rate can be increased by a factor of $\mathcal{O}(10)$.Comment: 33 pages, 11 figures, added references, corrected typos, improved text, affiliation is changed. This is the publication versio

Exploring Leptophilic Dark Matter with NA64-μ\mu

We investigate the prospects for detecting light leptophilic dark sectors with a missing-momentum experiment at NA64 running in muon mode. In particular, we consider models in which dark matter connects to the visible sector through a lepton- or muon-specific scalar mediator. These scalars can also account for the $\sim 3.5\sigma$ discrepancy between the measured and predicted values of $(g-2)_{\mu}$ . We emphasize the complementarity between NA64-$\mu$ and other terrestrial and astrophysical probes.Comment: v2: figure added, references added, typo fixed, as published in JHE

Schwannomas of the Left Adrenal Gland and Posterior Mediastinum

Schwannoma is a rare tumor of neural crest cell origin. Most schwannomas occur in the head, neck, stomach or limbs, with a few cases occurring in the retroperitoneal space. A 30-year-old Taiwanese woman presented with a 1-week history of left anterior chest discomfort and left flank pain. The laboratory findings and endocrine studies were all within normal limits. Chest X-ray revealed masses in the posterior mediastinum. Chest computed tomography and magnetic resonance imaging showed several masses in the left paraspinal region and in the left adrenal region. The patient underwent total excision of the left paraspinal tumors and laparoscopic left adrenalectomy. Pathologic studies showed a picture of benign schwannoma. In conclusion, preoperative differentiation of benign schwannoma from malignant peripheral nerve sheath tumor or other tumors is important for good prognosis. Total excision of benign schwannoma is associated with favorable outcome in patients

Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy

Missing link in community psychiatry: When a patient with schizophrenia was expelled from her home

Treatment and disposition of homeless patients with schizophrenia represent a great challenge in clinical practice. We report a case of this special population, and discuss the development of homelessness, the difficulty in disposition, their utilization of health services, and possible applications of mandatory community treatment in this group of patients. A 51-year-old homeless female was brought to an emergency department for left femur fracture caused by an assault. She was diagnosed with schizophrenia about 20 years ago but received little help from mental health services over the decades. During hospitalization, her psychotic symptoms were only partially responsive to treatment. Her family refused to handle caretaking duties. The social welfare system was mobilized for long-term disposition. Homeless patients with schizophrenia are characterized by family disruption, poor adherence to health care, and multiple emergency visits and hospitalization. We hope this article can provide information about the current mental health policy to medical personnel. It is possible that earlier intervention and better outcome can be achieved by utilizing mandatory community treatment in the future, as well as preventing patients with schizophrenia from losing shelters

Calculation of Weighted Geometric Dilution of Precision

To achieve high accuracy in wireless positioning systems, both accurate measurements and good geometric relationship between the mobile device and the measurement units are required. Geometric dilution of precision (GDOP) is widely used as a criterion for selecting measurement units, since it represents the geometric effect on the relationship between measurement error and positioning determination error. In the calculation of GDOP value, the maximum volume method does not necessarily guarantee the selection of the optimal four measurement units with minimum GDOP. The conventional matrix inversion method for GDOP calculation demands a large amount of operation and causes high power consumption. To select the subset of the most appropriate location measurement units which give the minimum positioning error, we need to consider not only the GDOP effect but also the error statistics property. In this paper, we employ the weighted GDOP (WGDOP), instead of GDOP, to select measurement units so as to improve the accuracy of location. The handheld global positioning system (GPS) devices and mobile phones with GPS chips can merely provide limited calculation ability and power capacity. Therefore, it is very imperative to obtain WGDOP accurately and efficiently. This paper proposed two formations of WGDOP with less computation when four measurements are available for location purposes. The proposed formulae can reduce the computational complexity required for computing the matrix inversion. The simpler WGDOP formulae for both the 2D and the 3D location estimation, without inverting a matrix, can be applied not only to GPS but also to wireless sensor networks (WSN) and cellular communication systems. Furthermore, the proposed formulae are able to provide precise solution of WGDOP calculation without incurring any approximation error