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Chun Chen

Chun Chen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.supr-conComputer Visioncond-mat.str-elArtificial IntelligenceComputation and Languagecond-mat.quant-gasMachine Learningastro-ph.HEastro-ph.SRBiological PhysicsBiomoleculescond-mat.softCryptography and Securityeess.ASeess.IVGenomicsGraphicsInformation Retrievalphysics.chem-phphysics.opticsPopulations and EvolutionSound

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Published work

18 published item(s)

preprint2026arXiv

Direct Detection of Type II-P Supernova Progenitors with the Euclid and CSST Surveys

A central goal in supernova (SN) research is to identify and characterize their progenitors. However, this is very difficult due to the limited archival images with sufficient depth and spatial resolution required for direct progenitor detection and due to the circumstellar dust which often biases the estimate of their intrinsic parameters. This field will be revolutionized by Euclid and the upcoming Chinese Space Station Survey Telescope (CSST), which conduct deep, wide-field, high-resolution and multi-band imaging surveys. We analyze their detection capability by comparing the model magnitudes of red supergiant (RSG) progenitors with the detection limits under different conditions, and we estimate the annual detection rates with Monte-Carlo simulations. We explore how to recover the intrinsic properties of SN progenitors with the help of radiation transfer calculations in circumstellar dust. We find the optical and near-infrared filters of the Euclid and CSST are highly effective for detecting RSG progenitors. We predict that archival images from the completed 2 surveys will enable $\lesssim13$ (or 24) progenitor detections per year within the mass range of 8--16 (or 8--25)M_\odot, an order of magnitude higher than the current detection rate of $\sim1$ detection per year. In the presence of circumstellar dust, the emerging spectral energy distribution (SED) of the progenitor is mainly affected by the optical depth and is almost independent of dust temperature in the Euclid and CSST filters. Our mock tests demonstrate that one can derive the progenitor mass and dust optical depth simultaneously by fitting the observed SED over the 11 filters of the 2 surveys while fixing the dust temperature to a typical value. Euclid and CSST will significantly enlarge the sample of direct progenitor detections with accurate mass measurements, which is crucial to resolve the long-standing RSG problem.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LoopTrap: Termination Poisoning Attacks on LLM Agents

Modern LLM agents solve complex tasks by operating in iterative execution loops, where they repeatedly reason, act, and self-evaluate progress to determine when a task is complete. In this work, we show that while this self-directed loop facilitates autonomy, it also introduces a critical risk: by injecting malicious prompts into the agent's context, an adversary can distort the agent's termination judgment, making it believe the task remains incomplete and leading to unbounded computation.To understand this threat, we define and systematically characterize it as Termination Poisoning and design 10 representative attack strategies. Through a empirical study spanning 8 LLM agents and 60 tasks, we demonstrate that different LLM agents exhibit distinct behavioral signatures that determine which strategies succeed. These transferable patterns can serve as principled guidance for crafting effective attacks against previously unseen agents and tasks, enabling scalable red-teaming beyond manually designed templates. Building on these insights, we introduce LoopTrap, an automated red-teaming framework that synthesizes target-specific malicious prompts by exploiting agent behavioral tendencies. LoopTrap first constructs a behavioral profile of the target agent along four vulnerability dimensions via lightweight probing. It then performs adaptive trap synthesis, routing to the most effective strategy and selecting optimal injections via a self-scoring mechanism. Finally, successful traps are abstracted into a reusable skill library, while failed attempts are refined through self-reflection, ensuring continuous improvement. Extensive evaluation shows that LoopTrap achieves an average of 3.57$\times$ step amplification across 8 mainstream agents, with a peak of 25$\times$.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-contrast, speckle-free, true 3D holography via binary CGH optimization

Holography is a promising approach to implement the three-dimensional (3D) projection beyond the present two-dimensional technology. True 3D holography requires abilities of arbitrary 3D volume projection with high-axial resolution and independent control of all 3D voxels. However, it has been challenging to implement the true 3D holography with high-reconstruction quality due to the speckle. Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization. We adopt the random phase for the true 3D implementation to achieve the maximum axial resolution with fully independent control of the 3D voxels. We develop the high-performance binary hologram optimization framework to minimize the binary quantization noise, which provides accurate and high-contrast reconstructions for 2D as well as 3D cases. Utilizing the fast operation of binary modulation, the full-color high-framerate holographic video projection is realized while the speckle noise of random phase is overcome by temporal multiplexing. Our high-quality true 3D holography is experimentally verified by projecting multiple arbitrary dense images simultaneously. The proposed method can be adopted in various applications of holography, where we show additional demonstration that realistic true 3D hologram in VR and AR near-eye displays. The realization will open a new path towards the next generation of holography.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Knowledge Distillation with the Reused Teacher Classifier

Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various approaches have been proposed over the past few years, generally with elaborately designed knowledge representations, which in turn increase the difficulty of model development and interpretation. In contrast, we empirically show that a simple knowledge distillation technique is enough to significantly narrow down the teacher-student performance gap. We directly reuse the discriminative classifier from the pre-trained teacher model for student inference and train a student encoder through feature alignment with a single $\ell_2$ loss. In this way, the student model is able to achieve exactly the same performance as the teacher model provided that their extracted features are perfectly aligned. An additional projector is developed to help the student encoder match with the teacher classifier, which renders our technique applicable to various teacher and student architectures. Extensive experiments demonstrate that our technique achieves state-of-the-art results at the modest cost of compression ratio due to the added projector.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Majorana corner flat bands in two-dimensional second-order topological superconductors

In this paper we find that confining a second-order topological superconductor with a harmonic potential leads to a proliferation of Majorana corner modes. As a consequence, this results in the formation of Majorana corner flat bands which have a fundamentally different origin from that of the conventional mechanism. This is due to the fact that they arise solely from the one-dimensional gapped boundary states of the hybrid system that become gapless without the bulk gap closing under the increase of the trapping potential magnitude. The Majorana corner states are found to be robust against the strength of the harmonic trap and the transition from Majorana corner states to Majorana flat bands is merely a smooth crossover. As a harmonic trap can potentially be realized in heterostructures, this proposal paves a way to observe these Majorana corner flat bands in an experimental context.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Inhomogeneous Topological Superfluidity in One-Dimensional Spin-Orbit-Coupled Fermi Gases

We theoretically predict an exotic topological superfluid state with spatially modulated pairing gap in one-dimensional spin-orbit-coupled Fermi gases. This inhomogeneous topological superfluidity is induced by applying simultaneously a perpendicular Zeeman magnetic field and an equally weighted Rashba and Dresselhaus spin-orbit coupling in one-dimensional optical lattices. Based on the self-consistent Bogoliubov--de Gennes theory, we confirm that this novel topological phase is a unique condensation of Cooper pairs, which manifests the interplay between the inhomogeneity of superfluid and its nontrivial topological structure. The properties of the emergent Majorana bound states are investigated in detail by examining the associated $\mathbb{Z}_{2}$ topological number, the eigenenergy and density of states spectra, as well as the wave functions of the localized Majorana end modes. Experimental feasibility of observing this new topological state of matter is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Relational Multi-Manifold Co-Clustering

Co-clustering targets on grouping the samples (e.g., documents, users) and the features (e.g., words, ratings) simultaneously. It employs the dual relation and the bilateral information between the samples and features. In many realworld applications, data usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifold of the data space in a principled way. In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel co-clustering algorithm called Relational Multi-manifold Co-clustering (RMC) based on symmetric nonnegative matrix tri-factorization, which decomposes the relational data matrix into three submatrices. This method considers the intertype relationship revealed by the relational data matrix, and also the intra-type information reflected by the affinity matrices encoded on the sample and feature data distributions. Specifically, we assume the intrinsic manifold of the sample or feature space lies in a convex hull of some pre-defined candidate manifolds. We want to learn a convex combination of them to maximally approach the desired intrinsic manifold. To optimize the objective function, the multiplicative rules are utilized to update the submatrices alternatively. Besides, both the entropic mirror descent algorithm and the coordinate descent algorithm are exploited to learn the manifold coefficient vector. Extensive experiments on documents, images and gene expression data sets have demonstrated the superiority of the proposed algorithm compared to other well-established methods.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Spectral Graph Cut from a Filtering Point of View

Spectral graph theory is well known and widely used in computer vision. In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e.g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering. Based on this connection we show that the normalized cut algorithm is equivalent to repeated iterations of bilateral filtering. Then, using this equivalence we present and implement a fast normalized cut algorithm for image segmentation. Experiments show that our implementation can solve the original optimization problem in the normalized cut algorithm 10 to 100 times faster. Furthermore, we present a new algorithm called conditioned normalized cut for image segmentation that can easily incorporate color image patches and demonstrate how this segmentation problem can be solved with edge preserving filtering.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Tunable Splitting of the Ground-State Degeneracy in 1D Parafermionic Wires

Systems with topologically protected ground-state degeneracies are currently of great interest due to their potential applications in quantum computing. In practise this degeneracy is never exact, and the magnitude of the ground-state degeneracy splitting imposes constraints on the timescales over which information is topologically protected. In this Letter we use an instanton approach to evaluate the splitting of topological ground-state degeneracy in quasi-1D systems with parafermion zero modes, in the specific case where parafermions are realized by inducing a superconducting gap in pairs of fractional quantum Hall edges. We show that, like 1D topological superconducting wires, this splitting has an oscillatory dependence on the chemical potential, which arises from an intrinsic Berry phase that produces interference between distinct instanton tunneling events. These Berry phases can be mapped to chiral phases in a (dual) quantum clock model using a Fradkin-Kadanoff transformation. Comparing our low-energy spectrum to that of phenomenological parafermion models allows us to evaluate the real and imaginary parts of the hopping integral between adjacent parafermionic zero modes as a function of the chemical potential.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Type-II Topological Meissner States

We study the \emph{orbital effects} of the synthetic magnetic fields in an interacting square lattice two-leg fermionic ladder model with the number-conserving pair hopping that hosting the Majorana bound states. By utilizing density matrix renormalization group and exact diagonalization, we identify a novel type-II topological Meissner $($\emph{topo}-Meissner$)$ phase $($as distinguished from the low-field type-I \emph{topo}-Meissner state$)$ when threading a \emph{high} magnetic flux through the plaquette of the ladder, which not only exhibits a large uniformly circulating chiral current along the legs, the characteristics of the celebrated Meissner state, but also accommodates a topologically protected ground-state manifold due to the \emph{reentrant} emergence of the Majorana end modes. Our work reveals some interesting interference effects resulting from the interplay between the gauge fields and the strong interactions in establishing the intrinsic topological states of matter in low-dimensional quantum systems.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Agriculture driving male expansion in Neolithic Time

The emergence of agriculture is suggested to have driven extensive human population growths. However, genetic evidence from maternal mitochondrial genomes suggests major population expansions began before the emergence of agriculture. Therefore, role of agriculture that played in initial population expansions still remains controversial. Here, we analyzed a set of globally distributed whole Y chromosome and mitochondrial genomes of 526 male samples from 1000 Genome Project. We found that most major paternal lineage expansions coalesced in Neolithic Time. The estimated effective population sizes through time revealed strong evidence for 10- to 100-fold increase in population growth of males with the advent of agriculture. This sex-biased Neolithic expansion might result from the reduction in hunting-related mortality of males.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Coexistence of Enhanced Superconductivity and Antiferromagnetism: Possible Correlated Phase Transitions in Trilayer High-Tc Cuprates

Based on a hybrid interlayer coupling mechanism, we study the coexistence of superconductivity (SC) and antiferromagnetism (AFM) in trilayer cuprates. By introducing an interlayer magnetic scattering term, we solve the multilayer $t{-}J$ model with Josephson coupling under the framework of Gutzwiller projection. We show that both the SC and AFM orders in the multilayered system are enhanced and the range of AFM order is extended. The layer configuration of d-wave pairing gap and AFM order further plays an essential role in determining the interlayer magnetic and superconducting coupling phase diagram of such multilayered systems. Abrupt phase transitions between correlated states carrying distinct configurational symmetries are unveiled by tuning the doping level and/or the tunneling strengths.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Unfolding mechanism and the free energy landscape of a single stranded DNA i-motif

We present Molecular Dynamics simulations of a single stranded unprotonated DNA i-motif in explicit solvent. Our results indicate that the native structure in non-acidic solution at 300 K is unstable and completely vanishes on a time scale up to 10 ns. Two unfolding mechanisms with decreasing connectivity between the initially interacting nucleobases can be identified where one pathway is characterized as entropically more favorable. The entropic preference can be mainly explained by strong water ordering effects due to hydrogen bonds for several occurring structures along the pathways. Finally we are able to indicate via free energy calculations the most stable configurations belonging to distinct hairpin structures in good agreement to experimental results.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Vortex states in hole-doped iron-pnictide superconductors

Based on a phenomenological model with competing spin-density-wave (SDW) and extended $s-$wave superconductivity, the vortex states in Ba$_{1-x}$K$_{x}$Fe$_{2}$As$_{2}$ are investigated by solving Bogoliubov-de Gennes equations. Our result for the optimally doped compound without induced SDW is in qualitative agreement with recent scanning tunneling microscopy experiment. We also propose that the main effect of the SDW on the vortex states is to reduce the intensity of the in-gap peak in the local density of states and transfer the spectral weight to form additional peaks outside the gap.

0 citations0 reviewsNo code linkedOpen access