arXiv subjects

Jiajun Ma

Publications and source records attributed to Jiajun Ma.

At least 19 recordsLinked to original sources

arXiv2026

Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning

While Multimodal Large Language Models (MLLMs) have advanced video understanding, achieving precise temporal and cross-modal alignment in audiovisual video captioning remains a formidable challenge. Most existing approaches suffer from modality detachment and temporal incoherence, failing to accurately bind auditory events to visual entities or capture complex causal dynamics. To address these deficiencies, we propose TCA-Captioner, a framework specifically engineered to enhance Temporal and Cross-Modal Alignment for audiovisual video captioning. We first introduce the Observer-Checker-Corrector (OCC) framework, an iterative refinement strategy that generates high-fidelity, meticulously grounded training data. Leveraging a curated high-density human interaction dataset, TCA-Captioner is optimized to model sophisticated audiovisual interactions. Furthermore, we present TCA-Bench, a diagnostic benchmark utilizing a Decoupled Evaluation Protocol to isolate and quantify model proficiency in audiovisual binding and temporal relational reasoning. Extensive experiments demonstrate that TCA-Captioner sets a new standard for temporally-coherent and synchronized audiovisual narratives.

cs.CV↗

arXiv2026

EconCSLib: A Lean Library for Computational Economics and AI-Assisted Research

Mathematical formalization uses interactive theorem provers to turn informal mathematical statements into machine-checkable artifacts. The success of mathlib, a large collaborative library for Lean, illustrates the potential of this approach. Recent progress in AI-assisted programming and theorem proving is also making large-scale formalization more practical. This paper presents EconCSLib, an early Lean 4 library for computational economics, as both infrastructure and a case study for AI-assisted formalization. The library aims to provide reusable definitions and theorems for game theory, mechanism design, social choice, and related areas. Beyond verified proofs of existing results, the library also aims to host machine-checked open problems and formalization of modern research papers. We discuss the design principles behind the library, the lessons learned from its development, and future directions for AI-assisted formalization in computational economics.

cs.GT↗

arXiv2026

Nonvolatile electric switching of critical current in cross-bar superconducting junctions

Superconducting (SC) diodes are key passive building blocks for future SC electronics. However, realizing their active counterparts is essential for functional logic. Here, we demonstrate deterministic nonvolatile electrical switching of the critical current ($I_\text{c}$) in overlap crossbar SC junctions. By applying a minimal perpendicular magnetic field ($H_\text{z}$), $I_\text{c}$ is modulated by a factor of four with a large switching efficiency of 60\%, achieved at a significantly reduced excitation current density of $5\times10^5$~A/cm$^2$. We also uncover anomalous behaviors: an electrically switchable critical temperature and a non-monotonic $I_\text{c}$-$H_\textit{z}$ response. These observations are interpreted in terms of unique asymmetry involving isolated vortex injection, configuration and repulsion inherent to the junction geometry. Our device provides a scalable, low-power alternative to complex SQUID-based architectures, paving the way for high-density SC integrated circuits.

cond-mat.supr-con↗

arXiv2026

Large room temperature anomalous Nernst effect coupled with topological Nernst effect from incommensurate spin structure in a Kagome antiferromagnet

Kagome magnets exhibit a range of novel and nontrivial topological properties due to the strong interplay between topology and magnetism, which also extends to their thermoelectric applications. Recent advances in the study of magnetic topological materials have highlighted their intriguing anomalous Hall and thermoelectric effects, arising primarily from large intrinsic Berry curvature. Here, we report observation of a large room-temperature (RT) anomalous Nernst effects (ANE) of S_xy^A ~ 1.3 {\mu}V K^(-1) in the kagome antiferromagnet (AFM) ErMn6Sn6, which is comparable to the largest signals observed in known magnetic materials. Surprisingly, we further found that a significant topological Nernst signal at RT and peaking a maximum of approximately 0.2 {\mu}V K^(-1) at 180 K, exactly coupling with ANE in the spiral AFM state, originates from the real-space nonzero spin chirality caused by incommensurate spin structure. This study demonstrates a potential room-temperature thermoelectric application platform based on Nernst effect, and provides insights for discovering significant anomalous and topological transverse transport effects in the incommensurate AFM system.

cond-mat.str-el↗

arXiv2025

Large longitudinal and anomalous transverse Magneto-thermoelectric effect in kagome antiferromagnet FeGe

Topological Kagome magnets, characterized by nontrivial electronic band structures featuring flat band, Dirac cone and van Hove singularities, provide a new avenue for the realization of thermoelectric devices. Unlike the conventional longitudinal Seebeck effect, transverse thermoelectric (TE) effects like the Nernst effect have attracted growing interest due to their unique transverse geometry and potential advantages. Here, we report the observation of a significant transverse thermoelectric conductivity alpha A_zx of 15 A K-1m-1 at low temperatures, together with a pronounced anomalous Nernst effect in the Kagome antiferromagnet FeGe, which exhibits a charge density wave inside the antiferromagnetic (AFM) state. This value is the highest record among known AFM materials. Furthermore, the thermopower at 14 T increases by 102-104% around the canted-AFM (CAFM) transition temperature, Tcant, comparable to that of the well-known AFM thermoelectric materials. These effects are attributed to large Berry curvature arising from the non-collinear spin texture in FeGe, highlighting its potential for enhancing thermoelectric performance and its candidacy for magneto-TE applications in Kagome antiferromagnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain's natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain's modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.

cs.LG↗

arXiv2025

Electric-Magnetic-Switchable Free-Space Skyrmions in Toroidal Light Pulses via a Nonlinear Metasurface

Recent advances reveal that light propagation in free space supports many exotic topological textures, such as skyrmions. Their unique space-time topologies make them promising candidates as next-generation robust information carriers. Hence, the ability of switching different texture modes is highly demanded to serve as a manner of data transfer. However, previous studies focus on generation of one specific mode, lacking integrated devices with externally variable and stable mode generation capability. Here, we experimentally demonstrate the first realization of switchable skyrmions between electric and magnetic modes in toroidal light pulses using a nonlinear metasurface platform in terms of broadband terahertz generation driven by vectorial pulse. The spatial and temporal evolutions of them are also clearly observed. Our work establishes a new paradigm for manipulating and switching topologically structured light.

physics.optics↗

arXiv2025

Anomalous Nernst Effect and Its Implications for Time-Reversal Symmetry Breaking in Kagome Metal ScV6Sn6

The nonmagnetic kagome metal ScV6Sn6 displays an unconventional charge order (CO) accompanied by signatures of an anomalous Hall effect, hidden magnetism, and multiple lattice instabilities. In this study, we report the observation of unconventional anomalous thermoelectric properties. Notably, unexpected anomalous transverse Nernst signals reach a peak value of ~4 {\mu}V/K near the TCDW ~92 K in ScV6Sn6, and these signals persist in the charge-ordered state as the temperature decreases to 10 K. Furthermore, both thermopower and thermal conductivity exhibit significant changes under magnetic fields, even in the nonmagnetic ground state. These observations strongly suggest the emergence of time-reversal symmetry breaking in ScV6Sn6, as supported by muon spin relaxation ({\mu}SR) measurements. While hidden magnetism represents the most plausible origin, alternative mechanisms involving orbital currents and chiral charge order remain possible.

cond-mat.str-el↗

arXiv2025

Anomalous Hall and Nernst effects in the Two-Dimensional ferromagnetic metal FePd2Te2

The transverse thermoelectric effect enables simpler, more flexible thermoelectric devices by generating electricity perpendicular to heat flow, offering promising solutions for waste heat recovery and solid-state cooling applications. Here, we report a striking observation of zero-field anomalous Hall effect (AHE) and anomalous Nernst effect (ANE) below TC in the two-dimensional metallic magnet FePd2Te2. The anomalous Nernst signal Syx^A peaks a maximum value of 0.15 {\mu}V/K at 100 K, much larger than that of conventional FM materials. Remarkably, the derived ratio alpha_ij/sigma_ij in FePd2Te2 approaches the fundamental limit of 86 {\mu}V/K. Our findings suggest a dominant Berry curvature contribution to the ANE. The observed giant zero-field anomalous Nernst response in 2D FePd2Te2 not only advances fundamental understanding of transverse thermoelectricity in layered magnets, but also provides this material as a promising candidate for practical thermoelectric spintronic applications.

cond-mat.str-el↗

arXiv2025

Superconducting Diode Effects: Mechanisms, Materials and Applications

Superconducting diode effects (SDEs) generally emerge in superconducting systems where both time-reversal and inversion symmetries are broken, showing nonreciprocal current characteristics: nondissipative in one direction and ohmic in the opposite. Since the discovery of the SDEs by Ando et al. in the noncentrosymmetric superconductor [Nb/V/Ta]n in 2020, notable progress has been achieved on both the theoretical and experimental fronts. It has been proposed that intrinsic SDEs are closely linked to various exotic superconducting states, such as the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, topological superconductivity, and chiral superconductivity. Recently, SDEs have emerged as important experimental tools for detecting symmetry breaking in exotic superconducting states. This advancement not only enhances our understanding of the fundamental nature of SDEs but also opens new possibilities for their applications in superconducting physics and related fields. This review focuses on the recent experimental progress in the observation of the SDEs and discusses their primary mechanisms from the perspective of material properties and symmetry breaking. Finally, we summarize the observed rectification efficiency of SDE devices and discuss future research directions in this rapidly developing field.

cond-mat.supr-con↗

arXiv2024

Anisotropic transport properties and topological Hall effect in the annealed kagome antiferromagnet FeGe

Electron correlation often gives birth to various orders in quantum materials. Recently, a strongly correlated kagome antiferromagnet FeGe is discovered to undergo a charge density wave transition inside the A-type antiferromagnetic state, providing an opportunity to explore the interplay between charge order and magnetism. Here, we reported the observation of anisotropic resistivity and Hall effect, along with a topological Hall effect, in the annealed FeGe crystals. As the current flows along the \emph{ab}-plane, the temperature dependence of $\rho_{ab}$ exhibits a distinct resistivity loop related to a first-order transition at $T_{cdw}$. The applied magnetic fields do not alter $T_{cdw}$ but can induce a spin-flop transition at $H_{sf}$. Consequently, a field-induced large topological Hall effect is observed in the canting antiferromagnetic (CAFM) state below $T_{cant}$, which is possibly attributed to the non-trivial spin texture during the spin-flop process. Whereas, as current is parallel to \emph{c}-axis, both the field-induced transitions in $\rho_{c}$ and $\chi_{c}$ disappear. Instead, the Hall resistivity in the annealed FeGe significantly exhibits a deviation from the linear field-dependent. These findings provide valuable insight into revealing the interplay among magnetism, charge order and topology in the kagome magnets.

cond-mat.str-el↗

arXiv2024

Photonic Terahertz Phased Array

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime.

physics.optics↗

arXiv2024

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connections between the encoder and decoder blocks. Although skip connections have been shown to improve training stability and model performance, we reveal that such shortcuts can be a limiting factor for the complexity of the transformation. As the sampling steps decrease, the generation process and the role of the UNet get closer to the push-forward transformations from Gaussian distribution to the target, posing a challenge for the network's complexity. To address this challenge, we propose Skip-Tuning, a simple yet surprisingly effective training-free tuning method on the skip connections. Our method can achieve 100% FID improvement for pretrained EDM on ImageNet 64 with only 19 NFEs (1.75), breaking the limit of ODE samplers regardless of sampling steps. Surprisingly, the improvement persists when we increase the number of sampling steps and can even surpass the best result from EDM-2 (1.58) with only 39 NFEs (1.57). Comprehensive exploratory experiments are conducted to shed light on the surprising effectiveness. We observe that while Skip-Tuning increases the score-matching losses in the pixel space, the losses in the feature space are reduced, particularly at intermediate noise levels, which coincide with the most effective range accounting for image quality improvement.

cs.LG↗

arXiv2024

An inevitably aging world -- Analysis on the evolutionary pattern of age structure in 200 countries

Ignoring the differences between countries, human reproductive and dispersal behaviors can be described by some standardized models, so whether there is a universal law of population growth hidden in the abundant and unstructured data from various countries remains unclear. The age-specific population data constitute a three-dimensional tensor containing more comprehensive information. The existing literature often describes the characteristics of global or regional population evolution by subregion aggregation and statistical analysis, which makes it challenging to identify the underlying rules by ignoring national or structural details. Statistical physics can be used to summarize the macro characteristics and evolution laws of complex systems based on the attributes and motions of masses of individuals by decomposing high-dimensional tensors. Specifically, it can be used to assess the evolution of age structure in various countries over the past approximately 70 years, rather than simply focusing on the regions where aging has become apparent. It provides a universal scheme for the growing elderly and working age populations, indicating that the demographics on all continents are inevitably moving towards an aging population, including the current "young" continents of Africa, and Asia, South America with a recent "demographic dividend". It is a force derived from the "life cycle", and most countries have been unable to avoid this universal evolutionary path in the foreseeable future.

physics.soc-ph↗

arXiv2023

Elucidating The Design Space of Classifier-Guided Diffusion Generation

Guidance in conditional diffusion generation is of great importance for sample quality and controllability. However, existing guidance schemes are to be desired. On one hand, mainstream methods such as classifier guidance and classifier-free guidance both require extra training with labeled data, which is time-consuming and unable to adapt to new conditions. On the other hand, training-free methods such as universal guidance, though more flexible, have yet to demonstrate comparable performance. In this work, through a comprehensive investigation into the design space, we show that it is possible to achieve significant performance improvements over existing guidance schemes by leveraging off-the-shelf classifiers in a training-free fashion, enjoying the best of both worlds. Employing calibration as a general guideline, we propose several pre-conditioning techniques to better exploit pretrained off-the-shelf classifiers for guiding diffusion generation. Extensive experiments on ImageNet validate our proposed method, showing that state-of-the-art diffusion models (DDPM, EDM, DiT) can be further improved (up to 20%) using off-the-shelf classifiers with barely any extra computational cost. With the proliferation of publicly available pretrained classifiers, our proposed approach has great potential and can be readily scaled up to text-to-image generation tasks. The code is available at https://github.com/AlexMaOLS/EluCD/tree/main.

cs.LG↗

arXiv2023

Deciphering the Projection Head: Representation Evaluation Self-supervised Learning

Self-supervised learning (SSL) aims to learn intrinsic features without labels. Despite the diverse architectures of SSL methods, the projection head always plays an important role in improving the performance of the downstream task. In this work, we systematically investigate the role of the projection head in SSL. Specifically, the projection head targets the uniformity part of SSL, which pushes the dissimilar samples away from each other, thus enabling the encoder to focus on extracting semantic features. Based on this understanding, we propose a Representation Evaluation Design (RED) in SSL models in which a shortcut connection between the representation and the projection vectors is built. Extensive experiments with different architectures, including SimCLR, MoCo-V2, and SimSiam, on various datasets, demonstrate that the representation evaluation design can consistently improve the baseline models in the downstream tasks. The learned representation from the RED-SSL models shows superior robustness to unseen augmentations and out-of-distribution data.

cs.LG↗

arXiv2021

Atomically sharp interface enabled ultrahigh-speed, nonvolatile memory devices

Development of memory devices with ultimate performance has played a key role in innovation of modern electronics. As a mainstream technology nonvolatile memory devices have manifested high capacity and mechanical reliability, however current major bottlenecks include low extinction ratio and slow operational speed. Although substantial effort has been employed to improve their performance, a typical hundreds of micro- or even milli- second write time remains a few orders of magnitude longer than their volatile counterparts. We have demonstrated nonvolatile, floating-gate memory devices based on van der Waals heterostructures with atomically sharp interfaces between different functional elements, and achieved ultrahigh-speed programming/erasing operations verging on an ultimate theoretical limit of nanoseconds with extinction ratio up to 10^10. This extraordinary performance has allowed new device capabilities such as multi-bit storage, thus opening up unforeseen applications in the realm of modern nanoelectronics and offering future fabrication guidelines for device scale-up.

physics.app-ph↗

arXiv2020

Stable Sparse Subspace Embedding for Dimensionality Reduction

Sparse random projection (RP) is a popular tool for dimensionality reduction that shows promising performance with low computational complexity. However, in the existing sparse RP matrices, the positions of non-zero entries are usually randomly selected. Although they adopt uniform sampling with replacement, due to large sampling variance, the number of non-zeros is uneven among rows of the projection matrix which is generated in one trial, and more data information may be lost after dimension reduction. To break this bottleneck, based on random sampling without replacement in statistics, this paper builds a stable sparse subspace embedded matrix (S-SSE), in which non-zeros are uniformly distributed. It is proved that the S-SSE is stabler than the existing matrix, and it can maintain Euclidean distance between points well after dimension reduction. Our empirical studies corroborate our theoretical findings and demonstrate that our approach can indeed achieve satisfactory performance.

cs.LG↗