LHC Search of New Higgs Boson via Resonant Di-Higgs Production with Decays into 4W

Searching for new Higgs particle beyond the observed light Higgs boson h(125GeV) will unambiguously point to new physics beyond the standard model. We study the resonant production of a CP-even heavy Higgs state $H^0$ in the di-Higgs channel via, $gg\to H^0\to h^0h^0\to WW^*WW^*$, at the LHC Run-2 and the high luminosity LHC (HL-LHC). We analyze two types of the $4W$ decay modes, one with the same-sign di-leptons ($4W\to\ell^\pm\nu\ell^\pm\nu 4q$) and the other with tri-leptons ($4W\to\ell^\pm\nu\ell^\mp\nu\ell^\pm\nu 2q$). We perform a full simulation for the signals and backgrounds, and estimate the discovery potential of the heavy Higgs state at the LHC Run-2 and the HL-LHC, in the context of generical two-Higgs-doublet models (2HDM). We determine the viable parameter space of the 2HDM as allowed by the theoretical constraints and the current experimental limits. We systematically analyze the allowed parameter space of the 2HDM which can be effectively probed by the heavy Higgs searches of the LHC, and further compare this with the viable parameter region under the current theoretical and experimental bounds.Comment: v3: JHEP published version, 34pp, 10 Figs(36 plots) and 9 Tables. Only minor typos fixed, references added. v2: JHEP version. All results and conclusions un-changed, discussions and references added. (This update is much delayed due to author's traveling and flu.

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://github.com/PaddlePaddle/PaddleDetection.Comment: Accepted to CVPR 202

Efficient and Scalable Graph Similarity Joins in MapReduce

Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results

Novel synthetic bisindolylmaleimide alkaloids inhibit STAT3 activation by binding to the SH2 domain and suppress breast xenograft tumor growth

Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in malignant tumors and plays important roles in multiple aspects of cancer aggressiveness. Thus, targeting STAT3 promises to be an attractive strategy for the treatment of advanced metastatic tumors. Bisindolylmaleimide alkaloid (BMA) has been shown to have anti-cancer activities and was thought to suppress tumor cell growth by inhibiting protein kinase C. In this study, we show that a newly synthesized BMA analog, BMA097, is effective in suppressing tumor cell and xenograft growth and in inducing spontaneous apoptosis. We also provide evidence that BMA097 binds directly to the SH2 domain of STAT3 and inhibits STAT3 phosphorylation and activation, leading to reduced expression of STAT3 downstream target genes. Structure activity relationship analysis revealed that the hydroxymethyl group in the 2,5-dihydropyrrole-2,5-dione prohibits STAT3 inhibitory activity of BMA analogs. Altogether, we conclude that the synthetic BMA analogs may be developed as anti-cancer drugs by targeting and binding to the SH2 domain of STAT3 and inhibiting the STAT3 signaling pathway

From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN