arXiv subjects

Ren Zhang

Publications and source records attributed to Ren Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Spin-orbit coupling induced geometric squeezing in rotating Bose-Einstein condensates

Squeezed states play a key role in diverse frontiers of quantum physics. Geometrically squeezed states, a squeezed state in the orbital phase space of rotating Bose-Einstein condensates (BEC), have been conventionally generated by anisotropic trapping potentials. In this work, we propose a different route to generate geometric squeezing via spin-orbit coupling (SOC) in a pseudospin-1/2 BEC. We show that the SOC enables effective two-phonon transitions within the lowest Landau level via virtual spin-flip processes, leading to exponential squeezing dynamics in both spin components. Furthermore, by applying a $\pi/2$ spin rotation, the two spin channels can be coherently coupled to produce two-mode geometric squeezing. We also investigate the influence of interatomic interactions on squeezing performance and identify parameters where robust squeezing can be achieved. Our work provides a viable pathway to realize and manipulate geometric squeezing in spinor quantum gases.

cond-mat.quant-gas↗

arXiv2026

Non-Hermitian Delocalization Realizes Random Dirac Criticality in One Dimension

Non-Hermitian systems can evade Anderson localization and exhibit delocalized states even in one dimension. Here, we show that such non-Hermitian delocalized states under periodic boundary conditions (PBC) are intrinsically critical, realizing the universality class of one-dimensional random Dirac fermions. By linking spectral winding to topological Anderson transitions via Hermitization, we demonstrate that the delocalized PBC states exhibit a Dirac-type criticality with universal algebraic correlations. In contrast to Hermitian systems, where this criticality occurs only at fine-tuned transition points, it emerges generically in non-Hermitian systems as a consequence of spectral topology. These results identify a universal mechanism by which non-Hermiticity promotes criticality, providing a unified description of non-Hermitian delocalization in one dimension.

cond-mat.dis-nn↗

arXiv2026

Temperature-Controlled Resonance in a Heteronuclear Quantum Gas Mixture

Single-channel resonances are fundamental processes in scattering of atoms, yet their occurrence is largely incidental and lacks systematic control. In this Letter, we propose a mechanism to realize a continuously tunable single-channel resonance by controlling the temperature of the heteronuclear mixture. By extending the Casimir-like mediated interaction to finite temperature, we demonstrate that thermal smearing of the Fermi surface reshapes the effective potential between impurities, giving rise to a temperature-controlled resonance (TCR) over a wide parameter range. As a direct consequence, the resonance position shifts systematically with temperature variation, providing a clear experimental signature of this mechanism. We further investigate the quench dynamics of a Bose gas immersed in a Fermi sea and demonstrate that the observed temperature-dependent loss features in recent experiments are consistent with the TCR mechanism. Our results establish temperature as a simple and experimentally accessible control knob for single-channel resonances in ultracold quantum gases.

cond-mat.quant-gas↗

arXiv2026

Order Flow Exclusivity and Value Extraction Mechanisms: An Analysis of Ethereum Builder Centralization

This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.

cs.CR↗

arXiv2026

Boundary-Robust Transmission Asymmetry as a Topological Signature in Open Floquet Lattices

We identify a boundary-robust topological signature of open Floquet lattices: although nonadiabatic boundaries strongly reshape the transmission lineshape, the integrated left--right transmission asymmetry saturates to a plateau set by the bulk Floquet winding number. Its origin is a deep-bulk branch-population principle: in the long-sample limit, each propagating Floquet--Bloch branch is generically populated with unit weight, since true Floquet bound states are nongeneric. The robust observable is therefore the cumulative transmission imbalance rather than the boundary-sensitive transmission profile. We propose direct detection by cold-atom transmission spectroscopy. For electronic transport, the same asymmetry admits contact-model-dependent electrical readouts: a coherent Floquet--Landauer--B\"uttiker interpretation predicts a near-$2ef$ response in weak SAW devices, whereas a blocking-factor post-processing yields a qualitatively different signal.

cond-mat.mes-hall↗

arXiv2026

Efimovian Phonon Production for an Analog Coasting Universe in Bose-Einstein Condensates

Efimov effects arise from scale invariance, a fundamental symmetry with universal implications. While spatial Efimov physics has been extensively studied, realizing its temporal counterpart remains challenging, as it requires a dynamical system that breaks time-translation symmetry yet preserves the essential time-scaling symmetry. Analog cosmology offers a powerful platform to address this challenge, bridging the domains of Efimov physics and cosmology. Here, we predict a temporal Efimov effect in an analog linearly expanding universe realized with a quasi-two-dimensional Bose-Einstein condensate. The invariance of phonon mode equations under time rescaling leads to particle production with two distinct dynamics: power-law growth and log-periodic oscillations, with the latter being the hallmark signature of the Efimov effect. Furthermore, these dynamics map directly onto sub- and super-horizon cosmological modes. Our predictions can be directly verified through time-averaged measurements of the density-fluctuation spectrum $S_{k}(t)$ in current experiments.

cond-mat.quant-gas↗

arXiv2026

Surpassing Quantum Noise Limits with Nonlinear Amplification

Linear quantum amplifiers are indispensable tools for quantum technologies, yet their performance is fundamentally limited by quantum noise, precluding any signal-to-noise ratio (SNR) enhancement unless supplemented by post-selection or non-classical resources. To surpass this limitation, we propose a nonlinear quantum amplification strategy that exploits the interplay between a gain-stabilized bright eigenmode of a coupled two-mode bosonic system and Kerr nonlinearity. We demonstrate that this interplay enables the signal gain to surpass the noise gain in a selected quadrature, leading to a net increase in the SNR beyond the quantum limits of conventional linear amplifiers. Our work thus establishes a novel nonlinear amplification paradigm capable of enhancing the SNR, with promising applications across quantum information processing, quantum communications, and quantum metrology.

quant-ph↗

arXiv2026

Scattering-state theory of open Floquet lattices: transfer matrices, branch openness, and robust asymmetry

We establish a scattering-state theory for open one-dimensional Floquet lattices based on a frequency-domain transfer-matrix formulation. For real quasienergy, the conjugate-symplectic structure of the transfer matrix separates bulk Floquet--Bloch modes into propagating and evanescent sectors, enabling a consistent treatment of interface matching and the shrinking-window smoothing required for long-sample transport. By tracking how incoming states populate deep-bulk propagating branches, we define branch-resolved weights $p_{\mu\alpha}$ and total branch weights $p_\mu$. We prove that $p_\mu$ equals the escape probability of a wave packet initialized on the corresponding branch. In the open geometries considered here, true bound trapping of propagating branches is nongeneric, yielding $p_\mu=1$ for generic parameters. This generic openness implies that long-sample transport is governed by deep-bulk branch populations rather than by boundary-sensitive interference. Consequently, the integrated left--right transmission asymmetry reduces to the net chirality, and hence the winding contribution, of an isolated Floquet band. The robust topological observable is therefore the accumulated asymmetry plateau, not the detailed transmission line shape, which remains strongly reshaped by nonadiabatic boundaries. A spatially adiabatic boundary serves only as a transparent benchmark for resolving the branch structure, not as the origin of the topological response.

cond-mat.mes-hall↗

arXiv2025

DEFT-LLM: Disentangled Expert Feature Tuning for Micro-Expression Recognition

Micro expression recognition (MER) is crucial for inferring genuine emotion. Applying a multimodal large language model (MLLM) to this task enables spatio-temporal analysis of facial motion and provides interpretable descriptions. However, there are still two core challenges: (1) The entanglement of static appearance and dynamic motion cues prevents the model from focusing on subtle motion; (2) Textual labels in existing MER datasets do not fully correspond to underlying facial muscle movements, creating a semantic gap between text supervision and physical motion. To address these issues, we propose DEFT-LLM, which achieves motion semantic alignment by multi-expert disentanglement. We first introduce Uni-MER, a motion-driven instruction dataset designed to align text with local facial motion. Its construction leverages dual constraints from optical flow and Action Unit (AU) labels to ensure spatio-temporal consistency and reasonable correspondence to the movements. We then design an architecture with three experts to decouple facial dynamics into independent and interpretable representations (structure, dynamic textures, and motion-semantics). By integrating the instruction-aligned knowledge from Uni-MER into DEFT-LLM, our method injects effective physical priors for micro expressions while also leveraging the cross modal reasoning ability of large language models, thus enabling precise capture of subtle emotional cues. Experiments on multiple challenging MER benchmarks demonstrate state-of-the-art performance, as well as a particular advantage in interpretable modeling of local facial motion.

cs.CV↗

arXiv2025

CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning

Image-point class incremental learning helps the 3D-points-vision robots continually learn category knowledge from 2D images, improving their perceptual capability in dynamic environments. However, some incremental learning methods address unimodal forgetting but fail in cross-modal cases, while others handle modal differences within training/testing datasets but assume no modal gaps between them. We first explore this cross-modal task, proposing a benchmark CMIP-CIL and relieving the cross-modal catastrophic forgetting problem. It employs masked point clouds and rendered multi-view images within a contrastive learning framework in pre-training, empowering the vision model with the generalizations of image-point correspondence. In the incremental stage, by freezing the backbone and promoting object representations close to their respective prototypes, the model effectively retains and generalizes knowledge across previously seen categories while continuing to learn new ones. We conduct comprehensive experiments on the benchmark datasets. Experiments prove that our method achieves state-of-the-art results, outperforming the baseline methods by a large margin.

cs.CV↗

arXiv2025

Dual Phonon Universe in a Spinor Bose Gas

We show the possibility of simulating a dual universe in a pseudospin-1/2 Bose-Einstein condensate (BEC), wherein two phononic modes experience distinct curved spacetime metrics. Through ramping the interspecies interaction of the BEC, we observe that one universe expands, and in the mean time, the other contracts. These findings can be directly verified in existing cold-atom experimental setups by detecting the correlators of the scaled density contrast (SDC): The sizes of the expanded/contracted universes can be deduced from the SDC's oscillating frequencies, while the expanding/contracting histories are reflected in the SDC's amplitude. Our study demonstrates the great potential of spinor BECs for exploring multiverse-related physics in laboratory settings.

cond-mat.quant-gas↗

arXiv2025

L2HCount:Generalizing Crowd Counting from Low to High Crowd Density via Density Simulation

Since COVID-19, crowd-counting tasks have gained wide applications. While supervised methods are reliable, annotation is more challenging in high-density scenes due to small head sizes and severe occlusion, whereas it's simpler in low-density scenes. Interestingly, can we train the model in low-density scenes and generalize it to high-density scenes? Therefore, we propose a low- to high-density generalization framework (L2HCount) that learns the pattern related to high-density scenes from low-density ones, enabling it to generalize well to high-density scenes. Specifically, we first introduce a High-Density Simulation Module and a Ground-Truth Generation Module to construct fake high-density images along with their corresponding ground-truth crowd annotations respectively by image-shifting technique, effectively simulating high-density crowd patterns. However, the simulated images have two issues: image blurring and loss of low-density image characteristics. Therefore, we second propose a Head Feature Enhancement Module to extract clear features in the simulated high-density scene. Third, we propose a Dual-Density Memory Encoding Module that uses two crowd memories to learn scene-specific patterns from low- and simulated high-density scenes, respectively. Extensive experiments on four challenging datasets have shown the promising performance of L2HCount.

cs.CV↗

arXiv2024

Geometric Optimization of Quantum Control with Minimum Cost

We investigate the optimization of quantum control from a differential geometric perspective. In our approach, optimal control minimizes the cost associated with evolving a quantum state, with the cost quantified by the length of the trajectory on a relevant Riemannian manifold. We demonstrate the optimization protocol in systems with SU(2) and SU(1,1) dynamical symmetries, which encompass a broad range of physical systems. In these systems, the time evolution can be represented by trajectories on a three-dimensional manifold. Given the initial and final states, the minimum-cost quantum control corresponds to a geodesic on the manifold. When the trajectory between the initial and final states is specified, the minimum-cost control corresponds to a geodesic within a submanifold embedded in the three-dimensional space. This framework provides a geometric method for optimizing shortcuts to adiabatic driving.

quant-ph↗

arXiv2024

Micro-expression recognition based on depth map to point cloud

Micro-expressions are nonverbal facial expressions that reveal the covert emotions of individuals, making the micro-expression recognition task receive widespread attention. However, the micro-expression recognition task is challenging due to the subtle facial motion and brevity in duration. Many 2D image-based methods have been developed in recent years to recognize MEs effectively, but, these approaches are restricted by facial texture information and are susceptible to environmental factors, such as lighting. Conversely, depth information can effectively represent motion information related to facial structure changes and is not affected by lighting. Motion information derived from facial structures can describe motion features that pixel textures cannot delineate. We proposed a network for micro-expression recognition based on facial depth information, and our experiments have demonstrated the crucial role of depth maps in the micro-expression recognition task. Initially, we transform the depth map into a point cloud and obtain the motion information for each point by aligning the initiating frame with the apex frame and performing a differential operation. Subsequently, we adjusted all point cloud motion feature input dimensions and used them as inputs for multiple point cloud networks to assess the efficacy of this representation. PointNet++ was chosen as the ultimate outcome for micro-expression recognition due to its superior performance. Our experiments show that our proposed method significantly outperforms the existing deep learning methods, including the baseline, on the $CAS(ME)^3$ dataset, which includes depth information.

eess.IV↗

arXiv2024

Emergent Universal Quench Dynamics in Randomly Interacting Spin Models

Universality often emerges in low-energy equilibrium physics of quantum many-body systems, despite their microscopic complexity and variety. Recently, there has been a growing interest in studying far-from-equilibrium dynamics of quantum many-body systems. Such dynamics usually involves highly excited states beyond the traditional low-energy theory description. Whether universal behaviors can also emerge in such non-equilibrium dynamics is a central issue at the frontier of quantum dynamics. Here we report the experimental observation of universal dynamics by monitoring the spin depolarization process in a solid-state NMR system described by an ensemble of randomly interacting spins. The spin depolarization can be related to temporal spin-spin correlation functions at high temperatures. We discover a remarkable phenomenon that these correlation functions obey a universal functional form. This experimental fact helps us identify the dominant interacting processes in the spin depolarization dynamics that lead to this universality. Our observation demonstrates the existence of universality even in non-equilibrium dynamics at high temperatures, thereby complementing the well-established universality in low-energy physics.

cond-mat.str-el↗

arXiv2024

Scaling Relations of Spectrum Form Factor and Krylov Complexity at Finite Temperature

In the study of quantum chaos diagnostics, considerable attention has been attributed to the Krylov complexity and spectrum form factor (SFF) for systems at infinite temperature. These investigations have unveiled universal properties of quantum chaotic systems. By extending the analysis to include the finite temperature effects on the Krylov complexity and SFF, we demonstrate that the Lanczos coefficients $b_n$, which are associated with the Wightman inner product, display consistency with the universal hypothesis presented in PRX 9, 041017 (2019). This result contrasts with the behavior of Lanczos coefficients associated with the standard inner product. Our results indicate that the slope $\alpha$ of the $b_n$ is bounded by $\pi k_BT$, where $k_B$ is the Boltzmann constant and $T$ the temperature. We also investigate the SFF, which characterizes the two-point correlation of the spectrum and encapsulates an indicator of ergodicity denoted by $g$ in chaotic systems. Our analysis demonstrates that as the temperature decreases, the value of $g$ decreases as well. Considering that $\alpha$ also represents the operator growth rate, we establish a quantitative relationship between ergodicity indicator and Lanczos coefficients slope. To support our findings, we provide evidence using the Gaussian orthogonal ensemble and a random spin model. Our work deepens the understanding of the finite temperature effects on Krylov complexity, SFF, and the connection between ergodicity and operator growth.

cond-mat.stat-mech↗

arXiv2024

Generalized Spectral Form Factor in Random Matrix Theory

The spectral form factor (SFF) plays a crucial role in revealing the statistical properties of energy level distributions in complex systems. It is one of the tools to diagnose quantum chaos and unravel the universal dynamics therein. The definition of SFF in most literature only encapsulates the two-level correlation. In this manuscript, we extend the definition of SSF to include the high-order correlation. Specifically, we introduce the standard deviation of energy levels to define correlation functions, from which the generalized spectral form factor (GSFF) can be obtained by Fourier transforms. GSFF provides a more comprehensive knowledge of the dynamics of chaotic systems. Using random matrices as examples, we demonstrate new dynamics features that are encoded in GSFF. Remarkably, the GSFF is complex, and both the real and imaginary parts exhibit universal dynamics. For instance, in the two-level correlated case, the real part of GSFF shows a dip-ramp-plateau structure akin to the conventional counterpart, and the imaginary part for different system sizes converges in the long time limit. For the two-level GSFF, the closed analytical forms of the real part are obtained and consistent with numerical results. The results of the imaginary part are obtained by numerical calculation. Similar analyses are extended to three-level GSFF.

cond-mat.stat-mech↗

arXiv2023

Crystal: Enhancing Blockchain Mining Transparency with Quorum Certificate

Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule; however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof.

cs.CR↗