arXiv subjects

Ji Dai

Publications and source records attributed to Ji Dai.

15 recordsLinked to original sources

arXiv2026

The Hidden Cost of Alloying: Disorder-Driven Transport Collapse in TMDs

Alloying in two dimensional semiconductors is widely used to tune bandgaps, yet its implications for charge and energy transport remain poorly understood. Here, we investigate MoS2xSe2(1-x) alloys as a model system to study the interplay between composition, thickness, and disorder. Optical transitions and valence band dispersions evolve continuously with both stoichiometry and number of layers, with negligible bandgap bowing and a composition dependent attenuation of thickness driven renormalization. In contrast, time resolved spatial mapping of photoexcited carriers reveals a pronounced and asymmetric collapse of carrier diffusivity at intermediate compositions, which cannot be accounted for by changes in effective mass or band alignment, and instead emerges from strong real space fluctuations in the local energetic landscape generated by random chalcogen substitution. Microscopic simulations reproduce the experimental trends and show that the character of disorder depends critically on the direction of alloying, producing either scattering barriers or deep trapping sites. Together, these results demonstrate that transport in TMD alloys is governed by disorder physics, overlooked by conventional optical and photoemission probes at equilibrium. Our findings establish transport as a stringent metric of electronic quality and high-light intrinsic limitations in the usage of TMD alloys for layered semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2026

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks

Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compressed recurrent state can be dominated by repetitive behavior patterns. We identify this phenomenon as semantic state sink, where recurring semantics over-occupy the recurrent state and bias subsequent readouts. To mitigate semantic state sink, we propose SinkRec, a hybrid memory-transition looped architecture that decouples collaborative behavioral pattern storage from dynamic transition modeling. SinkRec externalizes recurring local patterns into a learnable conditional memory through residual vector quantization, reinjects the retrieved codes, and exposes memory key-value pairs to the attention block. It further introduces Temporal-Aware State-Relation Differential Gated DeltaNet (TDGD), which uses memory to purify recurrent writing and reading by suppressing memory-covered updates and removing memory-aligned readout responses. This design turns recurring semantics from state-competing signals into memory-retrievable patterns, allowing the recurrent state to focus on dynamic transitions and alleviating semantic state sink with linear-time efficiency. Experiments on public and industrial datasets demonstrate the effectiveness and efficiency of SinkRec.

cs.LG↗

arXiv2026

Photostationary Lifshitz transition in High Tc superconductor Bi2Sr2CaCu2O8+{\delta}

To date, controlling the steady-state electronic band structure in high-Tc cuprate superconductors has been achieved primarily through chemical doping or magnetic fields. Here, we present that ultrafast optical excitation can instead drive the electronic band structure of Bi2Sr2CaCu2O8+{\delta} into a photostationary, long-lived excited state. At sufficiently high pump fluences, this state undergoes a Lifshitz transition of the Fermi surface, characterized by a change in topology from hole-like to electron-like. Time- and angle-resolved photoemission spectroscopy, supported by single-band tight-binding calculations, reveals that 1.6 eV photoexcitation induces band-structure evolutions closely analogous to those produced by chemical doping. These results point to an efficient photodoping mechanism involving cooperative effects, including charge transfer, renormalization of effective electronic correlations, and defect-assisted charge trapping. Our findings raise fundamental questions regarding thermalization processes occurring on timescales comparable to the laser repetition period in cuprates. More broadly, ultrafast optical control enables access to otherwise inaccessible regions of the phase diagram by tuning the pump fluence.

cond-mat.str-el↗

arXiv2026

Discovery of d-orbital order in Tb2CoAl4Ge2

Orbital order describes a quantum state where occupied orbitals line up in a periodic pattern. While orbital physics plays a fundamental and universal role in strongly correlated electron systems, the existence and particularly the band structure fingerprint of orbital order remain a long-standing mystery. Here, we report the discovery of rare earth 5d-orbital order developed by the surface states of intermetallic compound Tb2CoAl4Ge2. Angle-resolved photoemission spectroscopy reveals characteristic nematic features like Fermi surface deformation and band split. These experimental observations can be described by a ferro-orbital order term in the mean-field Hamiltonian. The structural and magnetic origin of such order is excluded by systematic high-resolution neutron powder diffraction and scanning tunnelling microscopy measurements. Our results provide strong evidence for a pure surface orbital order scenario avoiding complications from structural distortion as in colossal magnetoresistance manganites, magnetic order as in iron-based superconductors, and charge transfer p-orbital order in cuprates.

cond-mat.str-el↗

arXiv2026

Pressure-Induced Superconducting-like Transition in the $\it d$-wave Altermagnet Candidate CsV$_2$Se$_2$O

Altermagnetism generates exchange-type spin splitting without net magnetization and, in its $\it d$-wave form, resembles the angular symmetry of unconventional $\it d$-wave superconductivity. Whether this correspondence bears directly on superconducting instabilities in real correlated materials remains open. Here we study the quasi-two-dimensional vanadium oxychalcogenide CsV$_2$Se$_2$O (CVSO), a square-net $\it d$-wave altermagnet candidate, through combined experimental and theoretical investigation of its lattice structure, electronic structure and transport properties. At ambient pressure, CVSO is a weakly insulating parent state with a density-wave-like anomaly near 100 K, and its bulk properties are most consistent with a G-type compensated antiferromagnetic background. Under compression, the density-wave-like feature is suppressed, the magnetoresistance evolves from predominantly negative to positive, and a superconducting-like resistive downturn emerges below about 3 K. This low-temperature anomaly is reproducible across samples and pressure media, and is suppressed by magnetic field. Room-temperature X-ray diffraction reveals no symmetry lowering, whereas does show a pronounced compressibility anomaly over the same pressure range. CVSO thus reveals a pressure-tuned phase diagram in which a reconstructed weakly insulating parent state gives way to strange-metal-like transport and superconducting-like behavior, echoing broader phenomenology associated with unconventional superconductors, including cuprates and nickelates.

cond-mat.supr-con↗

arXiv2026

Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation

Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inherently heterogeneous and can conflict in specific contexts, making effective fusion both crucial and challenging. Existing approaches often rely on shared fusion pathways, leading to entangled representations and modality imbalance. To address these issues, we propose MAGNET, a Modality-Guided Mixture of Adaptive Graph Experts Network with Progressive Entropy-Triggered Routing for Multimodal Recommendation, designed to enhance controllability, stability, and interpretability in multimodal fusion. MAGNET couples interaction-conditioned expert routing with structure-aware graph augmentation, so that both what to fuse and how to fuse are explicitly controlled and interpretable. At the representation level, a dual-view graph learning module augments the interaction graph with content-induced edges, improving coverage for sparse and long-tail items while preserving collaborative structure via parallel encoding and lightweight fusion. At the fusion level, MAGNET employs structured experts with explicit modality roles-dominant, balanced, and complementary-enabling a more interpretable and adaptive combination of behavioral, visual, and textual cues. To further stabilize sparse routing and prevent expert collapse, we introduce a two-stage entropy-weighting mechanism that monitors routing entropy. This mechanism automatically transitions training from an early coverage-oriented regime to a later specialization-oriented regime, progressively balancing expert utilization and routing confidence. Extensive experiments on public benchmarks demonstrate consistent improvements over strong baselines.

cs.AI↗

arXiv2026

Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation

Multimedia recommendation systems leverage user-item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. Despite notable advancements, existing approaches still face two critical limitations: first, shallow modality fusion often relies on simple concatenation, failing to exploit rich synergic intra- and inter-modal relationships; second, asymmetric feature treatment-where users are only characterized by interaction IDs while items benefit from rich multimodal content-hinders the learning of a shared semantic space. To address these issues, we propose a Cross-modal Recursive Attention Network with dual graph Embedding (CRANE). To tackle shallow fusion, we design a core Recursive Cross-Modal Attention (RCA) mechanism that iteratively refines modality features based on cross-correlations in a joint latent space, effectively capturing high-order intra- and inter-modal dependencies. For symmetric multimodal learning, we explicitly construct users' multimodal profiles by aggregating features of their interacted items. Furthermore, CRANE integrates a symmetric dual-graph framework-comprising a heterogeneous user-item interaction graph and a homogeneous item-item semantic graph-unified by a self-supervised contrastive learning objective to fuse behavioral and semantic signals. Despite these complex modeling capabilities, CRANE maintains high computational efficiency. Theoretical and empirical analyses confirm its scalability and high practical efficiency, achieving faster convergence on small datasets and superior performance ceilings on large-scale ones. Comprehensive experiments on four public real-world datasets validate an average 5% improvement in key metrics over state-of-the-art baselines.

cs.IR↗

arXiv2025

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair, generating structurally valid molecular alternatives with reduced toxicity, has not yet been systematically defined or benchmarked. To fill this gap, we introduce ToxiMol, the first benchmark task for general-purpose Multimodal Large Language Models (MLLMs) focused on molecular toxicity repair. We construct a standardized dataset covering 11 primary tasks and 660 representative toxic molecules spanning diverse mechanisms and granularities. We design a prompt annotation pipeline with mechanism-aware and task-adaptive capabilities, informed by expert toxicological knowledge. In parallel, we propose an automated evaluation framework, ToxiEval, which integrates toxicity endpoint prediction, synthetic accessibility, drug-likeness, and structural similarity into a high-throughput evaluation chain for repair success. We systematically assess 43 mainstream general-purpose MLLMs and conduct multiple ablation studies to analyze key issues, including evaluation metrics, candidate diversity, and failure attribution. Experimental results show that although current MLLMs still face significant challenges on this task, they begin to demonstrate promising capabilities in toxicity understanding, semantic constraint adherence, and structure-aware editing.

cs.AI↗

arXiv2025

Observation of multiple surface states in naturally cleavable chiral crystal PdSbSe

Chiral multifold fermions in solids exhibit unique band structures and topological properties, making them ideal for exploring fundamental physical phenomena related to nontrivial topology, chirality, and symmetry breaking. However, the challenge of obtaining clean, flat surfaces through cleavage has hindered the investigation of their unique electronic states. In this study, we utilize high-resolution angle-resolved photoemission spectroscopy and density functional theory calculations to investigate the low-energy electronic structure of the cleavable single-crystal PdSbSe. Our combined experimental and theoretical analysis reveals the presence of multifold degenerate fermions within this chiral crystal. We also observe multiple chiral Fermi arc surface states and spin-splitting behavior in the associated bulk bands. These findings provide unique insights into chiral, multifold fermionic states in easily cleavable crystals and offer a robust platform for further research into their unique electronic properties and potential applications in novel electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Darkness in interlayer and charge density wave states of 2H-TaS2

The wave-like nature of electrons is evident from quantum interference effects observed during the photoemission process. When there are different nuclei in the unit cell of a crystal and/or structural distortions, photo-electron wavefunctions can interfere, giving rise to peculiar intensity modulation of the spectrum, which can also hide energy states in a photoemission experiment. The 2H phase of transition metal dichalcogenides, with two nonequivalent layers per unit cell and charge density wave distortion, is an optimal platform for such effects to be observed. Here, we discover undetectable states in 2H-TaS2, interpreting high-resolution angular resolved photoemission spectroscopy considering interference effects of the correlated electron wave functions. In addition, phase mismatching induced by the charge density wave distortion, results in evident signature of the phase transition in the photoemission spectrum. Our results highlight the importance of quantum interference, electronic correlations and structural distortion to understand the physics of layered materials.

cond-mat.str-el↗

arXiv2023

Discovery of a topological exciton insulator with tunable momentum order

Correlated topological materials often maintain a delicate balance among physical symmetries: many topological orders are symmetry protected, while most correlated phenomena arise from spontaneous symmetry breaking. It is rare to find cases where symmetry breaking induces a non-trivial topological phase. Here, we present the discovery of such a phase in Ta2Pd3Te5, where Coulomb interactions form excitons, which condense below 100 K, opening a topological gap and creating a topological excitonic insulator. Our spectroscopy reveals the full spectral bulk gap stemming from exciton condensation. This excitonic insulator state spontaneously breaks mirror symmetries but involves a very weak structural coupling, as indicated by photoemission spectroscopy, thermodynamic measurements, and a detailed structural analysis. Notably, scanning tunneling microscopy uncovers gapless boundary modes in the bulk insulating phase. Their magnetic field response, together with theoretical modeling, suggests a topological origin. These observations establish Ta2Pd3Te5 as the first confirmed topological excitonic insulator in a three-dimensional crystal. This allows to access the associated physics through bulk-sensitive techniques. Furthermore, we uncover another surprising aspect of the topological excitonic insulator, a secondary excitonic instability near 5 K that breaks the translational symmetry. The wavevector of this state shows an unprecedented magnetic field tunability. Thus, we unveil a unique sequence of topological exciton condensations in a bulk crystal, offering new opportunities to study critical behavior and excitations.

cond-mat.str-el↗

arXiv2023

A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces

Reading emotions precisely from segments of neural activity is crucial for the development of emotional brain-computer interfaces. Among all neural decoding algorithms, deep learning (DL) holds the potential to become the most promising one, yet progress has been limited in recent years. One possible reason is that the efficacy of DL strongly relies on training samples, yet the neural data used for training are often from non-human primates and mixed with plenty of noise, which in turn mislead the training of DL models. Given it is difficult to accurately determine animals' emotions from humans' perspective, we assume the dominant noise in neural data representing different emotions is the labeling error. Here, we report the development and application of a neural decoding framework called Emo-Net that consists of a confidence learning (CL) component and a DL component. The framework is fully data-driven and is capable of decoding emotions from multiple datasets obtained from behaving monkeys. In addition to improving the decoding ability, Emo-Net significantly improves the performance of the base DL models, making emotion recognition in animal models possible. In summary, this framework may inspire novel understandings of the neural basis of emotion and drive the realization of close-loop emotional brain-computer interfaces.

cs.HC↗

arXiv2023

Experimental demonstration of a magnetically induced warping transition in a topological insulator mediated by rare-earth surface dopants

Magnetic topological insulators (MTI) constitute a novel class of materials where the topologically protected band structure coexists with long-range ferromagnetic order, which can lead to the breaking of time-reversal symmetry (TRS), introducing a bandgap in the Dirac cone-shaped topological surface state (TSS). The gap opening in MITs has been predicted to be accompanied by a distortion in the TSS, evolving its warped shape from hexagonal to trigonal. In this work, we demonstrate such a transition by means of angle-resolved photoemission spectroscopy after the deposition of low concentrations of magnetic rare earths, namely Er and Dy, on the ternary three-dimensional prototypical topological insulator Bi$_2$Se$_2$Te. Signatures of the gap opening occurring as a consequence of the TRS breaking have also been observed, whose existence is supported by the observation of the aforementioned transition. Moreover, increasing the Er coverage results in a tunable p-type doping of the TSS. As a consequence, the Fermi level (E$_{\textrm{F}}$) of our Bi$_2$Se$_2$Te crystals can be gradually tuned towards the TSS Dirac point, and therefore to the magnetically induced bandgap; thus fulfilling two of the necessary prerequisites for the realization of the quantum anomalous Hall effect (QAHE) in this system. The experimental results are rationalized by a theoretical model where a magnetic Zeeman out-of-plane term is introduced in the hamiltonian governing the TSS band dispersion. Our results offer new strategies to control magnetic interactions with TSSs based on a simple approach and open up viable routes for the realization of the QAHE.

cond-mat.mes-hall↗

arXiv2021

From hidden-order to antiferromagnetism: electronic structure changes in Fe-doped URu$_{2}$Si$_{2}$

In matter, any spontaneous symmetry breaking induces a phase transition characterized by an order parameter, such as the magnetization vector in ferromagnets, or a macroscopic many-electron wave-function in superconductors. Phase transitions with unknown order parameter are rare but extremely appealing, as they may lead to novel physics. An emblematic, and still unsolved, example is the transition of the heavy fermion compound URu$_2$Si$_2$ (URS) into the so-called hidden-order (HO) phase when the temperature drops below $T_0 = 17.5$K. Here we show that the interaction between the heavy fermion and the conduction band states near the Fermi level has a key role in the emergence of the HO phase. Using angle resolved photoemission spectroscopy, we find that while the Fermi surfaces of the HO and of a neighboring antiferromagnetic (AFM) phase of well-defined order parameter have the same topography, they differ in the size of some, but not all, of their electron pockets. Such a non-rigid change of the electronic structure indicates that a change in the interaction strength between states near the Fermi level is a crucial ingredient for the HO-to-AFM phase transition.

cond-mat.str-el↗

arXiv2018

Accurate and efficient video de-fencing using convolutional neural networks and temporal information

De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers and improving the performance of computer vision algorithms such as object detection and recognition. However, the state-of-the-art de-fencing methods have limited performance caused by the difficulty of fence segmentation and also suffer from the motion of the camera or objects. To overcome these problems, we propose a novel method consisting of segmentation using convolutional neural networks and a fast/robust recovery algorithm. The segmentation algorithm using convolutional neural network achieves significant improvement in the accuracy of fence segmentation. The recovery algorithm using optical flow produces plausible de-fenced images and videos. The proposed method is experimented on both our diverse and complex dataset and publicly available datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance for both segmentation and content recovery.

cs.CV↗