Search

To address the longstanding tension between the Constrained Minimal Supersymmetric Standard Model (CMSSM) and recent experimental data, we investigate non-universal gaugino masses within an SU(5) Grand Unified Theory (GUT) framework, focusing on the $\tilde{g}$-SUGRA scenario where $\lvert M_{3} \rvert \gg \lvert M_{1} \rvert, \lvert M_{2} \rvert$. This hierarchy enables a heavier gluino, thereby evading current experimental bounds on supersymmetric particles. Our analysis reveals that precise Higgs measurements place stringent constraints on the model, requiring $\tan\beta \gtrsim 5$ and $ M_{0} \gtrsim 20 \, \tan\beta \,\text{GeV}$. Although the $\tilde{g}$-SUGRA scenario can help reconcile the persistent $(g-2)_\mu$ anomaly, the Higgs constraints significantly restrict its parameter space, making a large contribution to $(g-2)_{\mu}$ challenging. We also assess the discovery prospects in upcoming dark matter direct detection experiments, including PandaX-xT (200 t.y.), LZ (projected), and XENONnT (20 t.y.), which may not fully cover the viable parameter space. In contrast, future collider experiments$-$such as the High-Luminosity LHC at $3\,\mathrm{ab}^{-1}$ and $\mathrm{CLIC}_{1500}$ at $2.5\,\mathrm{ab}^{-1}$$-$can comprehensively probe the remaining regions. These findings highlight $\tilde{g}$-SUGRA as a promising solution to the CMSSM tension and offer clear, testable predictions for upcoming collider searches., Comment: 28 pages, 7 figures, 2 tables

In this study, we investigate the Type-I Two-Higgs-Doublet Model (2HDM-I) as a potential explanation for the 95 GeV diphoton excess reported at the LHC and evaluate the feasibility of discovering a 95 GeV Higgs boson at future hadron colliders. By analyzing the dependence of Higgs boson couplings and decay branching ratios on model parameters, we identify regions of parameter space that are consistent with current theoretical and experimental constraints. Our analysis indicates that direct Higgs search data place stringent constraints on the Higgs-mixing angle parameter $\alpha$, excluding samples within the range $-0.9 \lesssim \alpha \lesssim 1.3$. Subsequent collider simulations focus on the $pp \to t(\to W^+b)\bar{t}(\to W^-\bar{b})h(\to \gamma\gamma)$ process and its relevant backgrounds. These simulations reveal that at the 14 TeV High-Luminosity LHC (HL-LHC), statistical significances of $2\sigma$ and $5\sigma$ can be achieved at integrated luminosities of 137 fb$^{-1}$ and 859 fb$^{-1}$, respectively. Comparable statistical significance can be achieved more efficiently at the 27 TeV High-Energy LHC (HE-LHC) with integrated luminosities of $L=69 \text{fb}^{-1}$ and $L=430\ \text{fb}^{-1}$, and at the 100 TeV Future Circular Collider (FCC-hh) with $L=6.8\ \text{fb}^{-1}$ and $L=42\ \text{fb}^{-1}$. However, achieving comprehensive coverage of the parameters with $\alpha + \beta\approx 0$ at the $2\sigma$ level via the diphoton channel remains challenging, even with higher collision energies or increased integrated luminosities., Comment: 30 pages, 7 figures, 4 tables, typos corrected

Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks., Comment: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to empirically explore the learning behavior of 1D-CNNs from the perspective of the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to distract the focus from the low-frequency components leading to the accuracy degradation phenomenon, and the disturbing convolution is the driving factor. Then, we leverage our findings to the practical application and propose a regulatory framework, which can easily be integrated into existing 1D-CNNs. It aims to rectify the suboptimal learning behavior by enabling the network to selectively bypass the specified disturbing convolutions. Finally, through comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our regulatory framework enables state-of-the-art 1D-CNNs to get improved performances with less consumption of memory and computational overhead., Comment: Accepted as CIKM'23 Long Paper