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.

Z∗Z^*: Zero-shot Style Transfer via Attention Rearrangement

Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging. In contrast to existing learning/tuning methods, this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content image without retraining. Specifically, we adopt dual denoising paths to represent content/style references in latent space and then guide the content image denoising process with style latent codes. We further reveal that the cross-attention mechanism in latent diffusion models tends to blend the content and style images, resulting in stylized outputs that deviate from the original content image. To overcome this limitation, we introduce a cross-attention rearrangement strategy. Through theoretical analysis and experiments, we demonstrate the effectiveness and superiority of the diffusion-based $\underline{Z}$ero-shot $\underline{S}$tyle $\underline{T}$ransfer via $\underline{A}$ttention $\underline{R}$earrangement, Z-STAR

Dynamic Inference in Probabilistic Graphical Models

Probabilistic graphical models, such as Markov random fields (MRFs), are useful for describing high-dimensional distributions in terms of local dependence structures. The probabilistic inference is a fundamental problem related to graphical models, and sampling is a main approach for the problem. In this paper, we study probabilistic inference problems when the graphical model itself is changing dynamically with time. Such dynamic inference problems arise naturally in today's application, e.g.~multivariate time-series data analysis and practical learning procedures. We give a dynamic algorithm for sampling-based probabilistic inferences in MRFs, where each dynamic update can change the underlying graph and all parameters of the MRF simultaneously, as long as the total amount of changes is bounded. More precisely, suppose that the MRF has $n$ variables and polylogarithmic-bounded maximum degree, and $N(n)$ independent samples are sufficient for the inference for a polynomial function $N(\cdot)$. Our algorithm dynamically maintains an answer to the inference problem using $\widetilde{O}(n N(n))$ space cost, and $\widetilde{O}(N(n) + n)$ incremental time cost upon each update to the MRF, as long as the well-known Dobrushin-Shlosman condition is satisfied by the MRFs. Compared to the static case, which requires $\Omega(n N(n))$ time cost for redrawing all $N(n)$ samples whenever the MRF changes, our dynamic algorithm gives a $\widetilde\Omega(\min\{n, N(n)\})$-factor speedup. Our approach relies on a novel dynamic sampling technique, which transforms local Markov chains (a.k.a. single-site dynamics) to dynamic sampling algorithms, and an "algorithmic Lipschitz" condition that we establish for sampling from graphical models, namely, when the MRF changes by a small difference, samples can be modified to reflect the new distribution, with cost proportional to the difference on MRF

Spectrum of malignancies among the population of adults living with HIV infection in China: A nationwide follow-up study, 2008-2011.

BackgroundAlthough increasingly studied in high-income countries, there is a paucity of data from the Chinese population on the patterns of cancer among people living with HIV (PLHIV).MethodsWe conducted a nationwide follow-up study using routinely collected data for adult PLHIV diagnosed on or before 31 December 2011 and alive and in care as of 1 January 2008. Participants were observed from 1 January 2008 (study start) to 30 June 2012 (study end). Main outcome measures were gender-stratified age-standardized incidence rates for China (ASIRC) and standardized incidence ratios (SIR) for all malignancy types/sites observed.ResultsAmong 399,451 subjects, a majority was aged 30-44 years (49.3%), male (69.8%), and Han Chinese (67.9%). A total of 3,819 reports of cancer were identified. Overall, ASIRC was 776.4 per 100,000 for males and 486.5 per 100,000 for females. Malignancy sites/types with highest ASIRC among males were lung (226.0 per 100,000), liver (145.7 per 100,000), and lymphoma (63.1 per 100,000), and among females were lung (66.8 per 100,000), lymphoma (48.0 per 100,000), stomach (47.8 per 100,000), and cervix (47.6 per 100,000). Overall SIR for males was 3.4 and for females was 2.6. Highest SIR was observed for Kaposi sarcoma (2,639.8 for males, 1,593.5 for females) and lymphoma (13.9 for males, 16.0 for females).ConclusionsThese results provide evidence of substantial AIDS-defining and non-AIDS-defining cancer burden among adult Chinese PLHIV between 2008 and 2011. Although further study is warranted, China should take action to improve cancer screening, diagnosis, and treatment for this vulnerable population

Go Beyond Point Pairs: A General and Accurate Sim2Real Object Pose Voting Method with Efficient Online Synthetic Training

Object pose estimation is an important topic in 3D vision. Though most current state-of-the-art method that trains on real-world pose annotations achieve good results, the cost of such real-world training data is too high. In this paper, we propose a novel method for sim-to-real pose estimation, which is effective on both instance-level and category-level settings. The proposed method is based on the point-pair voting scheme from CPPF to vote for object centers, orientations, and scales. Unlike naive point pairs, to enrich the context provided by each voting unit, we introduce N-point tuples to fuse features from more than two points. Besides, a novel vote selection module is leveraged in order to discard those `bad' votes. Experiments show that our proposed method greatly advances the performance on both instance-level and category-level scenarios. Our method further narrows the gap between sim-to-real and real-training methods by generating synthetic training data online efficiently, while all previous sim-to-real methods need to generate data offline, because of their complex background synthesizing or photo-realistic rendering. Code repository: https://github.com/qq456cvb/BeyondPPF

Energy Efficient Robust Beamforming for Vehicular ISAC with Imperfect Channel Estimation

This paper investigates robust beamforming for system-centric energy efficiency (EE) optimization in the vehicular integrated sensing and communication (ISAC) system, where the mobility of vehicles poses significant challenges to channel estimation. To obtain the optimal beamforming under channel uncertainty, we first formulate an optimization problem for maximizing the system EE under bounded channel estimation errors. Next, fractional programming and semidefinite relaxation (SDR) are utilized to relax the rank-1 constraints. We further use Schur complement and S-Procedure to transform Cramer-Rao bound (CRB) and channel estimation error constraints into convex forms, respectively. Based on the Lagrangian dual function and Karush-Kuhn-Tucker (KKT) conditions, it is proved that the optimal beamforming solution is rank-1. Finally, we present comprehensive simulation results to demonstrate two key findings: 1) the proposed algorithm exhibits a favorable convergence rate, and 2) the approach effectively mitigates the impact of channel estimation errors.Comment: Submitted to IEEE for future publicatio