arXiv subjects

Yang Shen

Publications and source records attributed to Yang Shen.

At least 55 records · Page 3Linked to original sources

arXiv2026

BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding

Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain's distributed and nonlinear organization. We present BrainStack, a functionally guided neuro-mixture-of-experts (Neuro-MoE) framework that models the brain's modular functional architecture through anatomically partitioned expert networks. Each functional region is represented by a specialized expert that learns localized neural dynamics, while a transformer-based global expert captures cross-regional dependencies. A learnable routing gate adaptively aggregates these heterogeneous experts, enabling context-dependent expert coordination and selective fusion. To promote coherent representation across the hierarchy, we introduce cross-regional distillation, where the global expert provides top-down regularization to the regional experts. We further release SilentSpeech-EEG (SS-EEG), a large-scale benchmark comprising over 120 hours of EEG recordings from 12 subjects performing 24 silent words, the largest dataset of its kind. Experiments demonstrate that BrainStack consistently outperforms state-of-the-art models, achieving superior accuracy and generalization across subjects. Our results establish BrainStack as a functionally modular, neuro-inspired MoE paradigm that unifies neuroscientific priors with adaptive expert routing, paving the way for scalable and interpretable brain-language decoding.

cs.AI↗

arXiv2026

PhageMind: Generalized Strain-level Phage Host Range Prediction via Meta-learning

Bacteriophages (phages) are key regulators of bacterial populations and hold great promise for applications such as phage therapy, biocontrol, and industrial fermentation. The success of these applications depends on accurately determining phage host range, which is often specific at the strain level rather than the species level. However, existing computational approaches face major limitations: many rely on genus-specific features that do not generalize across taxa, while others require large amounts of training data that are unavailable for most bacterial lineages. These challenges create a critical need for methods that can accurately predict strain-level phage-host interactions across diverse bacterial genera, particularly under data-limited conditions. We present PhageMind, a learning framework designed to address this challenge by enabling efficient transfer of knowledge across bacterial genera. PhageMind is trained to identify shared principles of phage-bacterium interactions from well-studied systems and to rapidly adapt these principles to new genera using only a small number of known interactions. To reflect the biological basis of infection, we represent phage-host relationships using a knowledge graph that explicitly incorporates phage tail fiber proteins and bacterial O-antigen biosynthesis gene clusters, and we use this representation to guide interaction prediction. Across four bacterial genera (Escherichia, Klebsiella, Vibrio, and Alteromonas), PhageMind achieves high prediction accuracy and shows strong adaptability to new lineages. In particular, in leave-one-genus-out evaluations, the model maintains robust performance when only limited reference data are available, demonstrating its potential as a scalable and practical tool for studying phage-host interactions across the global phageome.

q-bio.GN↗

arXiv2026

Unveiling pressurized bulk superconductivity in a trilayer nickelate Pr4Ni3O10 single crystal

The recent discovery of superconductivity in pressurized Ruddlesden-Popper (RP) nickelates has provided new perspectives on the mechanism of high-temperature superconductivity. Up to now, most experiments concentrated on the lanthanum-related RP phase, so the discovery of new superconducting RP nickelates is highly desirable to reveal their generality. Here we report that high-quality Pr4Ni3O10 single crystal is grown with an optical floating zone furnace under high oxygen pressure. High-pressure transport measurements show that the superconducting state arises above 10 GPa, and the maximum Tc reaches 39 K without saturation, significantly exceeding the value of 25-30 K of La4Ni3O10. Ultrasensitive d.c. magnetic susceptibility measurements under high pressure indicate bulk superconductivity with appreciable superconducting volume fractions. By performing in situ high-pressure synchrotron X-ray diffraction measurements at 16 K, a structural transition is found from monoclinic to tetragonal. Unlike La4Ni3O10, the electronic structure of the high-pressure phase of Pr4Ni3O10 from density functional theory exhibits a dramatic metallization of the sigma-bonding band consisting of three dz2 orbitals and van Hove singularity of coupled bands of dx2-y2 orbitals near the Fermi level, similar to the bilayer nickelate La3Ni2O7. These findings reveal some generic features of both crystal and electronic structures for high-temperature superconductivity in nickelates and multi-layer cuprates.

cond-mat.supr-con↗

arXiv2026

Weakly anisotropic superconductivity of Pr4Ni3O10 single crystals

Since the discovery of high-temperature superconductivity, studying the upper critical field and its anisotropy has been crucial for understanding superconducting mechanism and guiding applications. Here we perform in situ high-pressure angular-dependent electrical transport measurements on Pr4Ni3O10 single crystals using a custom diamond anvil cell (DAC) rotator and confirming its anisotropic superconductivity. The anisotropy parameter is approximately 1.6, decreasing with increasing temperature and approaches 1 near Tc. Comparing effective mass anisotropy and inter-block distance in cuprates and iron-based superconductors (FeSCs) reveals that Pr4Ni3O10 single crystals superconductors are consistent with a two-band model, where intralayer quantum confinement within the unit cell induces interlayer coherence, thereby leading to three-dimensional (3D) superconductivity. This study not only establishes the existence of anisotropic superconductivity in bulk Ruddlesden-Popper nickelates, but also provide critical insight into the role of dimensionality in high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception

Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on cameras and LiDAR, neglecting 4D Radar, a sensor used in single-vehicle autonomous driving to provide robust perception in adverse weather conditions. In this paper, to bridge the gap created by the absence of 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large-scale, real-world multi-modal dataset featuring 4D Radar. V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data encompasses sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as various typical challenging scenarios. The dataset consists of 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, including 350K annotated boxes across five categories. To support various research domains, we have established V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. Furthermore, we provide comprehensive benchmarks across these three sub-datasets. We will release all datasets and benchmark codebase at https://huggingface.co/datasets/yanglei18/V2X-Radar and https://github.com/yanglei18/V2X-Radar.

cs.CV↗

arXiv2026

Charge disproportionation as a possible mechanism towards polar antiferromagnetic metal in molecular orbital crystal

Polar antiferromagnetic metals have recently garnered increasing interests due to their combined traits of both ferromagnets and antiferromagnets for spintronic applications. However, the inherently incompatible nature of antiferromagnet, metallicity and polarity pose a significant challenge. We propose that charge disproportionation can lead to this novel state in negative charge transfer gap regime in molecular orbital crystal by molecular orbital analyses of first-principles DFT+$U$ electronic band structure for representative Ruddlesden-Popper bilayer perovskite oxides Sr$_3$Co$_2$O$_7$, corroborated by Density Matrix Renormalization Group calculation. Due to the negative charge transfer nature of Co$^{4+}$ and imposed by strong interlayer coupling, localized molecular orbitals stemming from the hybridization of Co $d_{z^2}$ and $d_{xz/yz}$ orbitals through the apical oxygen $p$ orbitals are preferably emergent within each bilayer unit, which develop antiferromagnetic ordering by invoking Hubbard repulsion. Charge disproportionation driven by Hund's physics, makes an occupation imbalance with broken inversion symmetry in the remaining $d_{xy}$ and $d_{x^2-y^2}$ orbitals from distinct Co atoms within the bilayer unit, resulting in the polar metallicity. Meanwhile, this charge disproportionation scenario allows consequent conducting carriers to couple with interlayer local spins via Hund's coupling, giving rise to in-plane double-exchange ferromagnetism. Our molecular orbital formulation further provides a guide towards an effective Hamiltonian for modelling the unconventional synergy of metallicity, polarity and antiferromagnetism in Sr$_3$Co$_2$O$_7$, which may be a unified framework widely applicable to double-layer Ruddlesden-Popper perovskite oxides.

cond-mat.mtrl-sci↗

arXiv2025

Interaction-Driven Chern Insulator at Zero Electric Field in ABCB-Stacked Tetralayer Graphene

ABCB-stacked tetralayer graphene, with intrinsic spontaneous polarization, offers a unique platform to explore electron correlation effects, whose interplay with spin-orbit coupling may engender topological phases. Here, employing a $\mathbf{k}\cdot\mathbf{p}$ model with self-consistent Hartree-Fock calculations, we investigate its electronic ground states. Remarkably, we find that the intrinsic polarization, in conjunction with strong interactions ($U=8 \text{ eV}$) and SOC, is sufficient to drive a $C=3$ quantum anomalous Hall state, obviating the need for an external electric field typical in ABCA stacks. Conversely, at moderate interactions ($U=6 \text{ eV}$), a minimal electric field is necessary. Furthermore, calculations predict other correlation-driven metallic phases such as quarter- and three-quarter-filled states. These results establish that the synergy of intrinsic polarization, correlations, and SOC governs the rich topological phenomena, suggesting ABCB-stacked graphene as a highly tunable platform for exploring emergent topological phenomena.

cond-mat.mes-hall↗

arXiv2025

ResBench: A Comprehensive Framework for Evaluating Database Resilience

Existing database benchmarks primarily focus on performance under ideal running environments. However, in real-world scenarios, databases probably face numerous adverse events. Quantifying the ability to cope with these events from a comprehensive perspective remains an open problem. We provide the definition of database resilience to describe its performance when facing adversity and propose ResBench, a benchmark for evaluating database resilience. This framework achieves automation, standardization, and visualization of the testing process through clear hierarchical decoupling. ResBench simulates adverse events and injects them during normal transaction processing, utilizing a module to gather multiple metrics for the evaluation model. We assess database resilience across eight dimensions: throughput, latency, stability, resistance, recovery, disturbance period, adaptation capability and metric deviation. All the results are presented to users via a user-friendly graphical interface. We demonstrate the execution process and result interpretation of ResBench using two types of adversity datasets.

cs.DB↗

arXiv2025

HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific module may overlook shared traits across multiple roles, hindering commonality modeling. In this paper, we propose a novel HyCoRA: Hyper-Contrastive Role-Adaptive learning framework, which efficiently improves multi-character role-playing ability by balancing the learning of distinct and shared traits. Specifically, we propose a Hyper-Half Low-Rank Adaptation structure, where one half is a role-specific module generated by a lightweight hyper-network, and the other half is a trainable role-shared module. The role-specific module is devised to represent distinct persona signatures, while the role-shared module serves to capture common traits. Moreover, to better reflect distinct personalities across different roles, we design a hyper-contrastive learning mechanism to help the hyper-network distinguish their unique characteristics. Extensive experimental results on both English and Chinese available benchmarks demonstrate the superiority of our framework. Further GPT-4 evaluations and visual analyses also verify the capability of HyCoRA to capture role characteristics.

cs.CL↗

arXiv2025

Continuous-variable Measurement Device Independent MIMO Quantum Key Distribution for THz Communications

Although multiple-input multiple-output (MIMO) terahertz (THz) continuous-variable quantum key distribution (CVQKD) is theoretically secure, practical vulnerabilities may arise due to detector imperfections. This paper explores a CV measurement-device-independent (MDI) QKD system operating at THz frequencies within a MIMO framework. In this system, measurement is delegated to an untrusted third party, Charlie, rather than the receiver, eliminating all detector attacks and significantly enhancing the system's practical security. Using transmit-receive beamforming techniques, the system transforms MIMO channels into multiple parallel lossy quantum channels, enabling robust key distribution between Alice and Bob. This study examines entanglement-based and prepare-and-measure protocols, deriving secret key rates for both asymptotic and finite code scenarios. Simulations reveal the critical role of multiple antenna configurations and efficient homodyne detection in mitigating free-space path loss and maximizing key rates. Results indicate that system performance is optimized at lower THz frequencies for long-range transmissions and higher frequencies for short-range applications. The proposed protocol offers a scalable solution for secure quantum communications in next-generation wireless networks, demonstrating potential for deployment in both indoor and outdoor environments.

quant-ph↗

arXiv2025

A Hierarchical Adaptive Diffusion Model for Flexible Protein-Protein Docking

Structural prediction of protein-protein interactions is important to understand the molecular basis of cellular interactions, but it still faces major challenges when significant conformational changes are present. We propose a generative framework of hierarchical adaptive diffusion to improve accuracy and efficiency in such cases. It is hierarchical in separating global inter-protein rigid-body motions and local intra-protein flexibility in diffusion processes, and the distinct local and global noise schedules are designed to mimic the induced-fit effect. It is adaptive in conditioning the local flexibility schedule on predicted levels of conformational change, allowing faster flexing for larger anticipated conformational changes. Furthermore, it couples the local and global diffusion processes through a common score and confidence network with sequence, evolution, structure, and dynamics features as inputs, and maintains rotational or translational invariance or equivariance in outputs. It builds on our newly curated DIPS-AF dataset of nearly 39,000 examples for pre-training. Experiments on the independent docking benchmark dataset DB5.5 show that our model outperforms an AlphaFold2-like iterative transformer (GeoDock) and a diffusion model (DiffDock-PP) in both rigid and flexible cases, with larger improvements in more flexible cases. Ablation studies prove the importance of adaptive schedules, dynamics features, and pre-training. Additional analyses and case studies reveal remaining gaps in sampling, scoring, and conformational resolution.

cs.CE↗

arXiv2025

Modulation Instability-Induced Multimode Squeezing in Quadratic Frequency Combs

Lithium niobate (LN) microring resonators, characterized by an exceptionally high second-order nonlinear coefficient and superior electro-optic tunability, serve as an outstanding platform for the precise control of integrated quantum frequency combs (QFCs). In this study, we introduce a bipartite entanglement criterion to investigate the pairwise entanglement characteristics of QFCs generated via the spontaneous parametric down-conversion (SPDC) process in lithium niobate microring resonators operating below threshold. Furthermore, we propose a universal framework for analyzing multimode squeezing in quadratic frequency combs, enabling the realization of ultrabroadband and high-degree multimode squeezing. We further reveal the underlying physical mechanism: modulation instability (MI), regulated by temporal walk-off control, not only enables the formation of frequency combs but also induces multimode squeezing in the corresponding resonant modes. This study uncovers the previously unexplored role of on-chip multimode squeezing in quadratic frequency combs while facilitating collective noise suppression across multiple modes, thus holding substantial potential for advancing quantum precision measurement and quantum information processing.

quant-ph↗

arXiv2025

Non-simple systoles on random hyperbolic surfaces for large genus

In this paper, we investigate the asymptotic behavior of the non-simple systole, which is the length of a shortest non-simple closed geodesic, on a random closed hyperbolic surface on the moduli space $\mathcal{M}_g$ of Riemann surfaces of genus $g$ endowed with the Weil-Petersson measure. We show that as the genus $g$ goes to infinity, the non-simple systole of a generic hyperbolic surface in $\mathcal{M}_g$ behaves exactly like $\log g$.

math.GT↗

arXiv2025

Mechanistic Insight into BEOL Thermal Transport via Optical Metrology and Multiphysics Simulation

As integrated circuits continue to scale down and adopt three-dimensional (3D) stacking, thermal management in the back-end-of-line (BEOL) has emerged as a critical design constraint. In this study, we present a combined experimental and simulation framework to quantitatively characterize and mechanistically understand thermal transport in BEOL multilayers. Using the Square-Pulsed Source (SPS) method, a time-resolved optical metrology technique, we measure cross-plane thermal resistance and areal heat capacity in semiconductor chips at nanometer resolution. Two fabricated chip samples, polished to the M4 and M6 interconnection layers, are analyzed to extract thermal properties of distinct multilayer stacks. Results show that thermal resistance follows a series model, while areal heat capacity scales linearly with metal content. To uncover the underlying physical mechanisms, we perform finite element simulations using COMSOL Multiphysics, examining the influence of via connectivity and dielectric thermal conductivity on effective cross-plane heat transport. The simulations reveal that dielectric materials, due to their large volume fraction, are the primary limiting factor in BEOL thermal conduction, while the via structure plays a secondary but significant role. This combined experimental-simulation approach provides mechanistic insight into heat transport in advanced IC architectures and offers practical guidance for optimizing thermal pathways in future high-performance 3D-stacked devices.

cond-mat.mes-hall↗

arXiv2025

Enhancement of quantum coherence in solid-state qubits via interface engineering

Shallow nitrogen-vacancy (NV) centers in diamond are promising quantum sensors but suffer from noise-induced short coherence times due to bulk and surface impurities. We present interfacial engineering via oxygen termination and graphene patching, extending shallow NV coherence to over 1 ms, approaching the T1 limit. Raman spectroscopy and density-functional theory reveal surface termination-driven graphene charge transfer reduces spin noise by pairing surface electrons, supported by double electron-electron resonance spectroscopy showing fewer unpaired spins. Enhanced sensitivity enables detection of single weakly coupled 13C nuclear spins and external 11B spins from a hexagonal boron nitride (h-BN) layer, achieving nanoscale nuclear magnetic resonance. A protective h-BN top layer stabilizes the platform, ensuring robustness against harsh treatments and compatibility with target materials. This integrated approach advances practical quantum sensing by combining extended coherence, improved sensitivity, and device durability.

cond-mat.mes-hall↗

arXiv2025

ASC-Hook: fast and transparent system call hook for Arm

Intercepting system calls is crucial for tools that aim to modify or monitor application behavior. However, existing system call interception tools on the ARM platform still suffer from limitations in terms of performance and completeness. This paper presents an efficient and comprehensive binary rewriting framework, ASC-Hook, specifically designed for intercepting system calls on the ARM platform. ASC-Hook addresses two key challenges on the ARM architecture: the misalignment of the target address caused by directly replacing the SVC instruction with br x8, and the return to the original control flow after system call interception. This is achieved through a hybrid replacement strategy and our specially designed trampoline mechanism. By implementing multiple completeness strategies specifically for system calls, we ensured comprehensive and thorough interception. Experimental results show that ASC-Hook reduces overhead to at least 1/29 of that of existing system call interception tools. We conducted extensive performance evaluations of ASC-Hook, and the average performance loss for system call-intensive applications is 3.7\% .

cs.AR↗

arXiv2025

Arbitrarily small spectral gaps for random hyperbolic surfaces with many cusps

Let $\mathcal{M}_{g,n(g)}$ be the moduli space of hyperbolic surfaces of genus $g$ with $n(g)$ punctures endowed with the Weil-Petersson metric. In this paper we study the asymptotic behavior of the Cheeger constants and spectral gaps of random hyperbolic surfaces in $\mathcal{M}_{g,n(g)}$, when $n(g)$ grows slower than $g$ as $g\to \infty$.

math.DG↗

arXiv2025

Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we explore the idea that CV adopts discrete and terminological task definitions (\eg, ``image segmentation''), which may be a key barrier to zero-shot task generalization. Our hypothesis is that without truly understanding previously-seen tasks--due to these terminological definitions--deep models struggle to generalize to novel tasks. To verify this, we introduce Explanatory Instructions, which provide an intuitive way to define CV task objectives through detailed linguistic transformations from input images to outputs. We create a large-scale dataset comprising 12 million ``image input $\to$ explanatory instruction $\to$ output'' triplets, and train an auto-regressive-based vision-language model (AR-based VLM) that takes both images and explanatory instructions as input. By learning to follow these instructions, the AR-based VLM achieves instruction-level zero-shot capabilities for previously-seen tasks and demonstrates strong zero-shot generalization for unseen CV tasks. Code and dataset will be openly available on our GitHub repository.

cs.CV↗