arXiv subjects

Yong Ge

Publications and source records attributed to Yong Ge.

At least 19 recordsLinked to original sources

arXiv2026

Fairness Attacks on Recommender Systems

The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works have shown the effectiveness of attacks on the performance of recommender systems (e.g., promotion and demotion attack), the study of fairness attacks on recommender systems remains largely under-explored. To this end, we propose a novel structure-aware reinforcement learning-based fairness attack method designed to exacerbate the unfairness of target recommender systems. Specifically, we first employ a graph-based structure encoder to model the structural dependencies among the generated fake user-item interactions and the original user-item interactions. Then, we model the sequential dependency of the injected fake items using a recurrent neural network. Based on the learned structure-aware and sequence-aware representations of the fake user and item, the item selection policy attentively decides the next injected fake item. Since the target recommender system may employ fairness-aware training and leverage the user's sensitive attribute information, such as gender, we further designed a gender selection policy to decide the gender of the entire fake user profile. Both the item selection and gender selection policy are learned jointly in our proposed method. Finally, experimental results on four types of target recommendation models and two real-world datasets demonstrate the effectiveness of the proposed attack method in exacerbating the unfairness of recommender systems.

cs.IR↗

arXiv2026

Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management

Online reviews have played a pivotal role in consumers' decision-making processes. Existing research has highlighted the significant impact of managerial review responses on customer relationship management and firm performance. However, a large portion of online reviews remains unaddressed due to the considerable human labor required to respond to the rapid growth of online reviews. While generative AI has achieved remarkable success in a range of tasks, they are general-purpose models and may not align well with domain-specific human preferences. To tailor these general generative AI models to domain-specific applications, finetuning is commonly employed. Nevertheless, several challenges persist in finetuning with domain-specific data, including hallucinations, difficulty in representing domain-specific human preferences, and over conservatism in offline policy optimization. To address these challenges, we propose a novel preference finetuning method to align an LLM with domain-specific human preferences for generating online review responses. Specifically, we first identify the source of hallucination and propose an effective context augmentation approach to mitigate the LLM hallucination. To represent human preferences, we propose a novel theory-driven preference finetuning approach that automatically constructs human preference pairs in the online review domain. Additionally, we propose a curriculum learning approach to further enhance preference finetuning. To overcome the challenge of over conservatism in existing offline preference finetuning method, we propose a novel density estimation-based support constraint method to relax the conservatism, and we mathematically prove its superior theoretical guarantees. Extensive evaluations substantiate the superiority of our proposed preference finetuning method.

cs.AI↗

arXiv2026

Interpretable Probability Estimation with LLMs via Shapley Reconstruction

Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability can be applied to support intelligent decision-making across diverse fields, such as financial forecasting and preventive healthcare. However, directly prompting LLMs for probability estimation faces significant challenges: their outputs are often noisy, and the underlying predicting process is opaque. In this paper, we propose PRISM: Probability Reconstruction via Shapley Measures, a framework that brings transparency and precision to LLM-based probability estimation. PRISM decomposes an LLM's prediction by quantifying the marginal contribution of each input factor using Shapley values. These factor-level contributions are then aggregated to reconstruct a calibrated final estimate. In our experiments, we demonstrate PRISM improves predictive accuracy over direct prompting and other baselines, across multiple domains including finance, healthcare, and agriculture. Beyond performance, PRISM provides a transparent prediction pipeline: our case studies visualize how individual factors shape the final estimate, helping build trust in LLM-based decision support systems.

cs.LG↗

arXiv2025

Non-Hermitian edge burst of sound

Non-Hermitian band topology can give rise to phenomena with no counterparts in Hermitian systems. A well-known example is the non-Hermitian skin effect (NHSE), where Bloch eigenstates localize at a boundary, induced by a nontrivial spectrum winding number. In contrast, recent studies on lossy non-Hermitian lattices have uncovered an unexpected boundary-localized loss probability-a phenomenon that requires not only non-Hermitian band topology but also the closure of the imaginary (dissipative) gap. Here, we demonstrate the non-Hermitian edge burst in a classical-wave metamaterial: a lossy nonreciprocal acoustic crystal. We show that, when the imaginary gap remains closed, edge bursts can occur at the right boundary, left boundary, or both boundaries simultaneously, all under the same non-Hermitian band topology; the latter scenario is known as a bipolar edge burst. The occurrence of each scenario depends on the number and location of the imaginary gap closure points in the eigenenergy spectra. These findings generalize the concept of edge burst from quantum to classical wave systems, establish it as an intrinsic material property, and enrich the physics of the complex interplay between non-Hermitian band topology and other physical properties in non-Hermitian systems.

cond-mat.mes-hall↗

arXiv2025

Observation of Embedded Topology in a Trivial Bulk via Projective Crystal Symmetry

Bulk-boundary correspondence is the foundational principle of topological physics, first established in the quantum Hall effect, where a $D$-dimensional topologically nontrivial bulk gives rise to $(D-1)$-dimensional boundary states. The advent of higher-order topology has generalized this principle to a hierarchical chain, enabling topological states to appear at $(D-2)$ or even lower-dimensional boundaries. To date, all known realizations of topological systems must require a topologically nontrivial bulk to initiate the chain of action for bulk-boundary correspondence. Here, in an acoustic crystal platform, we experimentally demonstrate an exception to this paradigm--embedded topology in a trivial bulk--where the bulk-boundary correspondence originates from a trivial bulk. Rather than relying on global symmetries, we employ projective crystal symmetry, which induces nontrivial topology not at the outset in the $D$-dimensional bulk, but midway through the correspondence hierarchy in lower-dimensional boundaries. We further realize a three-dimensional system exhibiting embedded topology that supports zero-dimensional topological states, achieving the longest possible chain of action for such an unconventional bulk-boundary correspondence in physical space. Our work experimentally establishes a new form of bulk-boundary correspondence initiated from a trivial bulk, opening additional degrees of freedom for the design of robust topological devices.

cond-mat.mes-hall↗

arXiv2025

Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach

Survival analysis, a vital tool for predicting the time to event, has been used in many domains such as healthcare, criminal justice, and finance. Like classification tasks, survival analysis can exhibit bias against disadvantaged groups, often due to biases inherent in data or algorithms. Several studies in both the IS and CS communities have attempted to address fairness in survival analysis. However, existing methods often overlook the importance of prediction fairness at pre-defined evaluation time points, which is crucial in real-world applications where decision making often hinges on specific time frames. To address this critical research gap, we introduce a new fairness concept: equalized odds (EO) in survival analysis, which emphasizes prediction fairness at pre-defined time points. To achieve the EO fairness in survival analysis, we propose a Conditional Mutual Information Augmentation (CMIA) approach, which features a novel fairness regularization term based on conditional mutual information and an innovative censored data augmentation technique. Our CMIA approach can effectively balance prediction accuracy and fairness, and it is applicable to various survival models. We evaluate the CMIA approach against several state-of-the-art methods within three different application domains, and the results demonstrate that CMIA consistently reduces prediction disparity while maintaining good accuracy and significantly outperforms the other competing methods across multiple datasets and survival models (e.g., linear COX, deep AFT).

cs.LG↗

arXiv2025

Employee Turnover Prediction: A Cross-component Attention Transformer with Consideration of Competitor Influence and Contagious Effect

Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high turnover rates. Effective prediction of potential employee turnovers benefits multiple stakeholders such as firms and online recruiters. Prior studies have focused on either the turnover prediction within a single firm or the aggregated employee movement among firms. How to predict the individual employees' turnovers among multiple firms has gained little attention in literature, and thus remains a great research challenge. In this study, we propose a novel deep learning approach based on job embeddedness theory to predict the turnovers of individual employees across different firms. Through extensive experimental evaluations using a real-world dataset, our developed method demonstrates superior performance over several state-of-the-art benchmark methods. Additionally, we estimate the cost saving for recruiters by using our turnover prediction solution and interpret the attributions of various driving factors to employee's turnover to showcase its practical business value.

cs.LG↗

arXiv2024

Flatbands from Bound States in the Continuum for Orbital Angular Momentum Localization

A flatband material is a system characterized by energy bands with zero dispersion, allowing for the compact localization of wavefunctions in real space. This compact localization significantly enhances inter-particle correlations and light-matter interactions, leading to notable advancements such as fractional Chern insulators in condensed matter systems and flat-band lasers in photonics. Previous flatband platforms, including twisted bilayer graphene and artificial kagome/Lieb lattices, typically focused on nondegenerate flatbands, lacking access to the high degeneracy that can facilitate the localization of orbital angular momentum (OAM). Here, we propose a general framework to construct highly degenerate flatbands from bound states in the continuum (BICs)--a concept originating from quantum theory but significantly developed in photonics and acoustics in recent years. The degeneracy of flatbands is determined by the number of BICs within each unit cell in a lattice. We experimentally validate this approach in two-dimensional (2D) and three-dimensional (3D) acoustic crystals, demonstrating flatbands with 4-fold and 12-fold degeneracies, respectively. The high degeneracy provides sufficient internal degrees of freedom, enabling the selective excitation of localized OAM at any position in any direction. Our results pave the way for exploring BIC-constructed flatbands and their localization properties.

physics.optics↗

arXiv2024

TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation

Sequential recommendation tasks, which aim to predict the next item a user will interact with, typically rely on models trained solely on historical data. However, in real-world scenarios, user behavior can fluctuate in the long interaction sequences, and training data may be limited to model this dynamics. To address this, Test-Time Training (TTT) offers a novel approach by using self-supervised learning during inference to dynamically update model parameters. This allows the model to adapt to new user interactions in real-time, leading to more accurate recommendations. In this paper, we propose TTT4Rec, a sequential recommendation framework that integrates TTT to better capture dynamic user behavior. By continuously updating model parameters during inference, TTT4Rec is particularly effective in scenarios where user interaction sequences are long, training data is limited, or user behavior is highly variable. We evaluate TTT4Rec on three widely-used recommendation datasets, demonstrating that it achieves performance on par with or exceeding state-of-the-art models. The codes are available at https://github.com/ZhaoqiZachYang/TTT4Rec.

cs.IR↗

arXiv2024

Three-dimensional valley-contrasting sound

Spin and valley are two fundamental properties of electrons in crystals. The similarity between them is well understood in valley-contrasting physics established decades ago in two-dimensional (2D) materials like graphene--with broken inversion symmetry, the two valleys in graphene exhibit opposite orbital magnetic moments, similar to the spin-1/2 behaviors of electrons, and opposite Berry curvature that leads to a half topological charge. However, valley-contrasting physics has never been explored in 3D crystals. Here, we develop a 3D acoustic crystal exhibiting 3D valley-contrasting physics. Unlike spin that is fundamentally binary, valley in 3D can take six different values, each carrying a vortex in a distinct direction. The topological valley transport is generalized from the edge states of 2D materials to the surface states of 3D materials, with interesting features including robust propagation, topological refraction, and valley-cavity localization. Our results open a new route for wave manipulation in 3D space.

cond-mat.mes-hall↗

arXiv2024

Observation of disorder-induced boundary localization

Bloch wavefunctions in crystals experience localization within the bulk when disorder is introduced, a phenomenon commonly known as Anderson localization. This effect is considered universal, being applicable to all types of waves, quantum or classical. However, the interaction between disorder and topology -- a concept that has profoundly transformed many branches of physics -- necessitates revisiting the original Anderson localization picture. For instance, in the recently discovered topological Anderson insulator, the introduction of disorder induces topological boundary states that can resist localization due to protection from line-gap topology. While line-gap topology applies to both Hermitian and non-Hermitian systems, non-Hermitian systems uniquely exhibit point-gap topology, which has no Hermitian counterparts and leads to the non-Hermitian skin effect. Here, we experimentally demonstrate disorder-induced point-gap topology in a non-Hermitian acoustic crystal. This crystal, with non-Hermitian disorder in nearest-neighbor couplings, exhibits the non-Hermitian skin effect, where all eigenstates localize at a boundary. Interestingly, the boundary where localization occurs -- either the left or right -- depends on the strength of the disorder. As the disorder strength increases, the direction of boundary localization can be reversed. Additionally, we observe a ``bipolar" skin effect, where boundary localization occurs at both the left and right boundaries when disorder is introduced in next-nearest-neighbor couplings. These findings experimentally reveal a non-Hermitian mechanism of disorder-induced localization that goes beyond the conventional framework of Anderson localization.

physics.class-ph↗

arXiv2023

Three-dimensional flat Landau levels in an inhomogeneous acoustic crystal

When electrons moving in two-dimensions (2D) are subjected to a strong uniform magnetic field, they form flat bands called Landau levels, which are the basis for the quantum Hall effect. Landau levels can also arise from pseudomagnetic fields (PMFs) induced by lattice distortions; for example, mechanically straining graphene causes its Dirac quasiparticles to form a characteristic set of unequally-spaced Landau levels, including a zeroth Landau level. In three-dimensional (3D) systems, there has thus far been no experimental demonstration of Landau levels or any other type of flat band. For instance, applying a uniform magnetic field to materials hosting Weyl quasiparticles, the 3D generalizations of Dirac quasiparticles, yields bands that are non-flat in the direction of the field. Here, we report on the experimental realization of a flat 3D Landau level in an acoustic crystal. Starting from a lattice whose bandstructure exhibits a nodal ring, we design an inhomogeneous distortion corresponding to a specific pseudomagnetic vector potential (PVP) that causes the nodal ring states to break up into Landau levels, with a zeroth Landau level that is flat along all three directions. These findings point to the possibility of using nodal ring materials to generate 3D flat bands, to access strong interactions and other interesting physical regimes in 3D.

cond-mat.mes-hall↗

arXiv2023

Observation of vortex-string chiral modes in metamaterials

As a hypothetical topological defect in the geometry of spacetime, vortex strings play a crucial role in shaping the clusters of galaxies that exist today, and their distinct features can provide observable clues about the early universe's evolution. A key feature of vortex strings is that they can interact with Weyl fermionic modes and support topological chiral-anomaly states with massless dispersions at the core of strings. To date, despite many attempts to detect vortex strings in astrophysics or to emulate them in artificially created systems, observation of these topological vortex-string chiral modes remains experimentally elusive. Here we report the experimental observation of such vortex-string chiral modes using a metamaterial system. This is implemented by inhomogeneous perturbation of a Yang-monopole phononic metamaterial. The measured linear dispersion and modal profiles confirm the existence of topological modes bound to and propagating along the vortex string with the chiral anomaly. Our work not only provides a platform for studying diverse cosmic topological defects in astrophysics but also offers intriguing device applications as topological fibres in signal processing and communication techniques.

physics.app-ph↗

arXiv2022

Spinful topological phases in acoustic crystals with projective PT symmetry

For the classification of topological phases of matter, an important consideration is whether a system is spinless or spinful, as these two classes have distinct symmetry algebra that gives rise to fundamentally different topological phases. However, only recently has it been realized theoretically that in the presence of gauge symmetry, the algebraic structure of symmetries can be projectively represented, which possibly enables the switch between spinless and spinful topological phases. Here, we report the first experimental demonstration of this idea by realizing spinful topological phases in "spinless" acoustic crystals with projective space-time inversion symmetry. In particular, we realize a DIII-class one-dimensional topologically gapped phase characterized by a 2Z winding number, which features Kramers degenerate bands and Kramers pair of topological boundary modes. Our work thus overcomes a fundamental constraint on topological phases by spin classes.

cond-mat.mes-hall↗

arXiv2022

Boosting Factorization Machines via Saliency-Guided Mixup

Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the parameters corresponding to these features via the inner product of their embeddings. Undeniably, they cannot learn the direct interactions of these features, which limits the model's expressive power. To this end, we first present MixFM, inspired by Mixup, to generate auxiliary training data to boost FMs. Unlike existing augmentation strategies that require labor costs and expertise to collect additional information such as position and fields, these extra data generated by MixFM only by the convex combination of the raw ones without any professional knowledge support. More importantly, if the parent samples to be mixed have non-interactive features, MixFM will establish their direct interactions. Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM). Guided by the customized saliency, SMFM can generate more informative neighbor data. Through theoretical analysis, we prove that the proposed methods minimize the upper bound of the generalization error, which hold a beneficial effect on enhancing FMs. Significantly, we give the first generalization bound of FM, implying the generalization requires more data and a smaller embedding size under the sufficient representation capability. Finally, extensive experiments on five datasets confirm that our approaches are superior to baselines. Besides, the results show that "poisoning" mixed data is likewise beneficial to the FM variants.

cs.IR↗

arXiv2022

Observation of Dirac hierarchy in three-dimensional acoustic topological insulators

Dirac cones (DCs) play a pivotal role in various unique phenomena ranging from massless electrons in graphene to robust surface states in topological insulators (TIs). Recent studies have theoretically revealed a full Dirac hierarchy comprising an eightfold bulk DC, a fourfold surface DC, and a twofold hinge DC, associated with a hierarchy of topological phases including first-order to third-order three-dimensional (3D) topological insulators, using the same 3D base lattice. Here, we report the first experimental observation of the Dirac hierarchy in 3D acoustic TIs. Using acoustic measurements, we unambiguously reveal that lifting of multifold DCs in each hierarchy can induce two-dimensional (2D) topological surface states with a fourfold DC in a first-order 3D TI, one-dimensional (1D) topological hinge states with a twofold DC in a second-order 3D TI, and zero-dimensional (0D) topological corner states in a third-order 3D TI. Our work not only expands the fundamental research scope of Dirac physics, but also opens up a new route for multidimensional robust wave manipulation.

cond-mat.mes-hall↗

arXiv2021

Observation of roton-like dispersion relations in acoustic metamaterials

Roton dispersion relations, displaying a pronounced "roton" minimum at finite momentum, were firstly predicted by Landau and have been extensively explored in correlated quantum systems at low temperatures. Recently, the roton-like dispersion relations were theoretically extended to classical acoustics, which, however, have remained elusive in reality. Here, we report the experimental observation of roton-like dispersions in acoustic metamaterials with beyond-nearest-neighbour interactions at ambient temperatures. The resulting metamaterial supports multiple coexisting modes with different wavevectors and group velocities at the same frequency and broadband backward waves, analogous to the "return flow" termed by Feynman in the context of rotons. Moreover, by increasing the order of long-range interaction, we observe multiple rotons on a single dispersion band, which have never appeared in Landau's prediction or any other condensed matter study. The realization of roton-like dispersions in metamaterials could pave the way to explore novel physics and applications on quantum-inspired phenomena in classical systems.

physics.app-ph↗

arXiv2021

Observation of dislocation-induced topological modes in a three-dimensional acoustic topological insulator

The interplay between real-space topological lattice defects and the reciprocal-space topology of energy bands can give rise to novel phenomena, such as one-dimensional topological modes bound to screw dislocations in three-dimensional topological insulators. We obtain direct experimental observations of dislocation-induced helical modes in an acoustic analog of a weak three-dimensional topological insulator. The spatial distribution of the helical modes is found through spin-resolved field mapping, and verified numerically by tight-binding and finite-element calculations. These one-dimensional helical channels can serve as robust waveguides in three-dimensional media. Our experiment paves the way to studying novel physical modes and functionalities enabled by topological lattice defects in three-dimensional classical topological materials.

cond-mat.mes-hall↗