arXiv subjects

Haohao Zhang

Publications and source records attributed to Haohao Zhang.

9 recordsLinked to original sources

arXiv2026

Dark Transition Magnetic Moments of Majorana Neutrinos Mediated by a Dark Photon

Standard Model predictions for Majorana neutrino transition magnetic moments (TMMs) are subject to severe chiral and GIM-like suppressions, rendering them vanishingly small. To dynamically generate a macroscopic TMM, we propose a dark sector framework featuring a $U(1)_D$ gauge symmetry, a vector-like lepton doublet, and two complex dark scalars. We demonstrate that while fermion-radiated loop amplitudes identically cancel due to Majorana self-conjugacy, a chirally enhanced dark TMM is successfully generated exclusively through scalar-radiated loops. This mechanism safely shifts the required chirality flip onto the heavy internal fermion line and utilizes a misaligned double-scalar mixing in flavor space to evade the Majorana antisymmetry prohibition. We systematically confront this tensor portal framework with multi-frontier experimental constraints. Since the dark TMM generation is inextricably linked to charged lepton flavor violation, the internal Yukawa couplings are stringently capped by the latest $μ\to e γ$ limits from MEG II. Concurrently, the visible-dark kinetic mixing portal is heavily bottlenecked by missing energy and mono-photon searches at NA64 and BaBar. Our global phenomenological analysis reveals that the synergistic theoretical upper bound dictated by these indirect high-energy probes completely eclipses the direct scattering constraints from Borexino. This establishes a strict phenomenological hierarchy: high-intensity cLFV probes and accelerator-based dark sector searches jointly possess the overwhelmingly dominant exclusionary power over direct solar neutrino limits for such microscopic magnetic moment models.

hep-ph↗

arXiv2026

Movable-Antenna Empowered Backscatter ISAC: Toward Geometry-Adaptive, Low-Power Networks

Backscatter-based integrated sensing and communication (B-ISAC) elevates passive tags into information-bearing scatterers, offering an ultra-low-power path toward dual-function wireless systems. However, this promise is fundamentally undermined by a cascaded backscattering link that suffers from severe double fading and is exquisitely sensitive to geometric misalignment. This article tackles this geometric bottleneck by integrating movable antenna systems (MAS) at the transceiver side. MAS provides real-time, controllable spatial degrees of freedom through sub-wavelength antenna repositioning, enabling active reconfiguration of the cascaded channel without modifying passive tags or consuming additional spectrum. We position this solution within a unified ISAC-backscatter communication-B-ISAC evolution, describe the resulting MAS-assisted B-ISAC architecture and operating principles, and demonstrate its system-level gains through comparative analysis and numerical results. Finally, we showcase the potential of this geometry-adaptive paradigm across key IoT application scenarios, pointing toward future motion-aware wireless networks.

cs.IT↗

arXiv2025

Short geodesics and multiplicities of eigenvalues of hyperbolic surfaces

In this paper, we obtain upper bounds on the multiplicity of Laplacian eigenvalues for closed hyperbolic surfaces in terms of the number of short closed geodesics and the genus $g$. For example, we show that if the number of short closed geodesics is sublinear in $g$, then the multiplicity of the first eigenvalue is also sublinear in $g$. This makes new progress on a conjecture by Colin de Verdière in the mid 1980s.

math.DG↗

arXiv2025

Bridging the genotype-phenotype gap with generative artificial intelligence

The genotype-phenotype gap is a persistent barrier to complex trait genetic dissection, worsened by the explosive growth of genomic data (1.5 billion variants identified in the UK Biobank WGS study) alongside persistently scarce and subjective human-defined phenotypes. Digital phenotyping offers a potential solution, yet existing tools fail to balance scalable non-manual phenotype generation and biological interpretability of these quantitative traits. Here we report AIPheno, the first generative AI-driven "phenotype sequencer" that bridges this gap. It enables high-throughput, unsupervised extraction of digital phenotypes from imaging data and unlocks their biological meaning via generative network analysis. AIPheno transforms imaging modalities into a rich source of quantitative traits, dramatically enhancing cross-species genetic discovery, including novel loci such as CCBE1 (humans), KITLG-TMTC3 (domestic pigeons), and SOD2-IGF2R (swine). Critically, its generative module decodes AI-derived phenotypes by synthesizing variant-specific images to yield actionable biological insights. For example, it clarifies how the OCA2-HERC2 locus pleiotropically links pigmentation to retinal vascular traits via vascular visibility modulation. Integrating scalable non-manual phenotyping, enhanced genetic discovery power, and generative mechanistic decoding, AIPheno establishes a transformative closed-loop paradigm. This work addresses the longstanding genotype-phenotype imbalance, redefines digital phenotype utility, and accelerates translation of genetic associations into actionable understanding with profound implications for human health and agriculture.

q-bio.QM↗

arXiv2025

Spectral gaps on thick part of moduli spaces

In this paper, we study spectral gaps of closed hyperbolic surfaces for large genus. We show that for any fixed $k\geq 1$, as the genus goes to infinity, the maximum of $λ_k-λ_{k-1}$ over any thick part of the moduli space of closed Riemann surfaces approaches the limit $\frac{1}{4}$.

math.DG↗

arXiv2024

Revisiting for maximal flavor violating $Z^{'}_{eμ}$ and its phenomenology constraints

Lepton flavor violation (LFV), observed conclusively in neutrino oscillations, remains a pivotal area of investigation due to its absence in the Standard Model (SM). Beyond the Standard Model (BSM) physics explores charged lepton flavor violation (CLFV), particularly through new particle candidates such as the $Z'$. This article focuses on maximal LFV interactions facilitated by the $Z'$ boson, specifically targeting its off-diagonal interactions with the first and second generations of charged and neutral leptons. In our ultraviolet (UV) model for the origin of the $Z'$, inspired by the work of [R.Foot \textit{et al.,}, Phys.Rev. D50 (1994) 4571-4580], we utilize the discrete $Z_2$ symmetry to investigate the maximal LFV mediated by the $Z'$ between the muon ($μ$) and electron ($e$) arising from the additional scalars. This symmetry prohibits flavor-conserving interactions between $Z'$ and $μ^+μ^-,\, e^+e^-$. In conjunction with collider, $(g-2)_μ, (g-2)_e$, inverse $μ$ decay, Muonium-to-Antimuonium conversion and LFV decay constraints, we provide forecasts for anticipated limits derived from processes such as $ν_μN \to ν_e μ^+ e^- N$ in neutrino trident experiments like the DUNE search at the first time. These limits highlight the prospective scope and significance of LFV investigations within these experimental frameworks. Within the mass range of 0.01 GeV to 10 GeV, the most stringent limit arises from $\it{B} (μ\to e + X + γ)$ when $M_{Z'} < m_μ$, while $Δa_e$ provides effective constraints as $M_{Z'}$ approaches 10 GeV. Looking ahead, the proposed Muonium-to-Antimuonium Conversion Experiment (MACE) is expected to impose the most stringent constraints on Muonium-to-Antimuonium oscillation, improving sensitivity by about one order of magnitude against $Δa_e$.

hep-ph↗

arXiv2024

Fast Explicit Machine Learning-Based Model Predictive Control of Nonlinear Processes Using Input Convex Neural Networks

Explicit machine learning-based model predictive control (explicit ML-MPC) has been developed to reduce the real-time computational demands of traditional ML-MPC. However, the evaluation of candidate control actions in explicit ML-MPC can be time-consuming due to the non-convex nature of machine learning models. To address this issue, we leverage Input Convex Neural Networks (ICNN) to develop explicit ICNN-MPC, which is formulated as a convex optimization problem. Specifically, ICNN is employed to capture nonlinear system dynamics and incorporated into MPC, with sufficient conditions provided to ensure the convexity of ICNN-based MPC. We then formulate mixed-integer quadratic programming (MIQP) problems based on the candidate control actions derived from the solutions of multi-parametric quadratic programming (mpQP) problems within the explicit ML-MPC framework. Optimal control actions are obtained by solving real-time convex MIQP problems. The effectiveness of the proposed method is demonstrated through two case studies, including a chemical reactor example, and a chemical process network simulated by Aspen Plus Dynamics, where explicit ML-MPC written in Python is integrated with Aspen dynamic simulation through a programmable interface.

math.OC↗

arXiv2024

An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding

Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis. The predominant approaches in GS currently revolve around employing statistical methods for prediction. However, statistical methods often come with two main limitations: strong statistical priors and linear assumptions. A recent trend is to capture the non-linear relationships between markers by deep learning. However, as crop datasets are commonly long sequences with limited samples, the robustness of deep learning models, especially Transformers, remains a challenge. In this work, to unleash the unexplored potential of attention mechanism for the task of interest, we propose a simple yet effective Transformer-based framework that enables end-to-end training of the whole sequence. Via experiments on rice3k and wheat3k datasets, we show that, with simple tricks such as k-mer tokenization and random masking, Transformer can achieve overall superior performance against seminal methods on GS tasks of interest.

cs.LG↗

arXiv2022

Degenerating hyperbolic surfaces and spectral gaps for large genus

In this article we study the differences of two consecutive eigenvalues $λ_{i}-λ_{i-1}$ up to $i=2g-2$ for the Laplacian on hyperbolic surfaces of genus $g$, and show that the supremum of such spectral gaps over the moduli space has infimum limit at least $\frac{1}{4}$ as genus goes to infinity. A min-max principle for eigenvalues on degenerating hyperbolic surfaces is also established.

math.DG↗