arXiv subjects

Xin Lu

Publications and source records attributed to Xin Lu.

At least 109 records · Page 6Linked to original sources

arXiv2025

Flux Already Knows -- Activating Subject-Driven Image Generation without Training

We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply replicating the subject image(s) in a mosaic layout, we activate strong identity-preserving capabilities without any additional data, training, or inference-time fine-tuning. This "free lunch" approach is further strengthened by a novel cascade attention design and meta prompting technique, boosting fidelity and versatility. Experimental results show that our method outperforms baselines across multiple key metrics in benchmarks and human preference studies, with trade-offs in certain aspects. Additionally, it supports diverse edits, including logo insertion, virtual try-on, and subject replacement or insertion. These results demonstrate that a pre-trained foundational text-to-image model can enable high-quality, resource-efficient subject-driven generation, opening new possibilities for lightweight customization in downstream applications.

cs.CV↗

arXiv2025

NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results

This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.

cs.CV↗

arXiv2025

Phase transitions of the Erdős-Gyárfás function

Given positive integers $p,q$. For any integer $k\ge2$, an edge coloring of the complete $k$-graph $K_n^{(k)}$ is said to be a $(p,q)$-coloring if every copy of $K_p^{(k)}$ receives at least $q$ colors. The Erdős-Gyárfás function $f_k(n,p,q)$ is the minimum number of colors that are needed for $K_n^{(k)}$ to have a $(p,q)$-coloring. Conlon, Fox, Lee and Sudakov (\emph{IMRN, 2015}) conjectured that for any positive integers $p, k$ and $i$ with $k\ge3$ and $1\le i<k$, $f_k(n,p,{{p-i}\choose{k-i}})=(\log_{(i-1)}n)^{o(1)}$, where $\log_{(i)}n$ is an iterated $i$-fold logarithm in $n$. It has been verified to be true for $k=3, p=4, i=1$ by Conlon et. al (\emph{IMRN, 2015}), for $k=3, p=5, i=2$ by Mubayi (\emph{JGT, 2016}), and for all $k\ge 4, p=k+1,i=1$ by B. Janzer and O. Janzer (\emph{JCTB, 2024}). In this paper, we give new constructions and show that this conjecture holds for infinitely many new cases, i.e., it holds for all $k\ge4$, $p=k+2$ and $i=k-1$.

math.CO↗

arXiv2025

Fractional topological states in rhombohedral multilayer graphene modulated by kagome superlattice

Fractional quantum anomalous Hall effects realized in twisted bilayer MoTe$_2$ and multilayer-graphene-based moiré heterostructures have captured a tremendous growth of interest. In this work, we propose that rhombohedral multilayer graphene coupled with an artificial kagome superlattice potential is a new platform to realize various fractional topological phases. Taking Bernal bilayer graphene as the simplest example, when it is placed on top of a prepatterned SiO$_2$ substrate with periodic arrays of holes arranged into kagome lattice, the system would be subject to a tunable kagome superlattice potential once an electrostatic voltage drop between the top and bottom gates is applied. Then, we theoretically study the electronic band structures, topological properties, and quantum geometric properties of the Bloch states of Bernal bilayer graphene coupled with a realistic kagome superlattice potential, which is well benchmarked by transport measurements in the weak superlattice-potential regime. We find that the system may exhibit nearly ideal topological flat bands in a substantial region of the parameter space spanned by superlattice constant and electrostatic potential strength. When these topological flat bands are fractionally filled, exact diagonalization calculations suggest that the system would exhibit rich fractional topological phases at 1/3, 2/3, 2/5, 3/5 and 1/2 fillings including both fractional Chern insulators and anomalous composite Fermi liquids under zero magnetic field.

cond-mat.mes-hall↗

arXiv2025

Orientation-dependent superconductivity and electronic structure of the rare-earth metal/KTaO3 interfaces

The recent discovery of orientation-dependent superconductivity in KTaO3-based interfaces has attracted considerable interest, while the underlying origin remains an open question. Here we report a different approach to tune the interfacial electron gas and superconductivity by forming interfaces between rare-earth (RE) metals (RE being La, Ce, Eu) and KTaO3 substrates with different orientations. We found that the interfacial superconductivity is strongest for the Eu/KTaO3 interfaces, becomes weaker in La/KTaO3 and is absent in Ce/KTaO3. Using in-situ photoemission, we observed distinct valence bands associated with RE metals, as well as a pronounced orientation dependence in the interfacial electronic structure, which can be linked to the orientation-dependent superconductivity. The photoemission spectra show similar double-peak structures for the (111) and (110) oriented interfaces, with an energy separation close to the LO4 phonon of KTaO3. Detailed analyses suggest that this double-peak structure could be attributed to electron-phonon coupling, which might be important for the interfacial superconductivity.

cond-mat.supr-con↗

arXiv2025

SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters

The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release, LLMs remain vulnerable to various malicious attacks. In this paper, we adopt a red-teaming strategy to enhance LLM safety and introduce SeqAR, a simple yet effective framework to design jailbreak prompts automatically. The SeqAR framework generates and optimizes multiple jailbreak characters and then applies sequential jailbreak characters in a single query to bypass the guardrails of the target LLM. Different from previous work which relies on proprietary LLMs or seed jailbreak templates crafted by human expertise, SeqAR can generate and optimize the jailbreak prompt in a cold-start scenario using open-sourced LLMs without any seed jailbreak templates. Experimental results show that SeqAR achieves attack success rates of 88% and 60% in bypassing the safety alignment of GPT-3.5-1106 and GPT-4, respectively. Furthermore, we extensively evaluate the transferability of the generated templates across different LLMs and held-out malicious requests, while also exploring defense strategies against the jailbreak attack designed by SeqAR.

cs.CR↗

arXiv2025

Orbital magnetoelectric coupling of three dimensional Chern insulators

Orbital magnetoelectric effect is closely related to the band topology of bulk crystalline insulators. Typical examples include the half quantized Chern-Simons orbital magnetoelectric coupling in three dimensional (3D) axion insulators and topological insulators, which are the hallmarks of their nontrivial bulk band topology. While the Chern-Simons coupling is well defined only for insulators with zero Chern number, the orbital magnetoelectric effects in 3D Chern insulators with nonzero (layer) Chern numbers are still open questions. In this work, we propose a never-mentioned quantization rule for the orbital magnetoelectric response in 3D Chern insulators, the spatial gradient of which is exactly quantized in unit of $e^2/h$. By theoretical analysis and numerical simulations, we demonstrate that such quantized orbital magnetoelectric response is exact for various types of interlayer hoppings and stackings, and remains robust even against disorder and lack of crystalline symmetries. We argue that the exact quantization has a topological origin and is protected by Chern number. Furthermore, we propose two promising material platforms to observe the proposed quantized orbital magnetoelectric response thanks to recent experimental developments in detecting spatial magnetic-field distributions in device systems.

cond-mat.mes-hall↗

arXiv2025

Tunable Fractional Chern Insulators in Rhombohedral Graphene Superlattices

Fractional Chern insulators (FCIs) showing a transport effect with fractionally quantized Hall plateaus emerging under zero magnetic field, provide a radically new opportunity to engineer topological quantum electronics. By construction of topological flat band with moire engineering, intrinsic FCIs have been observed in twisted MoTe2 system and rhombohedral pentalayer graphene/hBN moire superlattices with anomalous Hall resistivity quantization number C <= 2/3 including the gapless composite Fermi-liquid state with C = 1/2. Here, we experimentally demonstrate a new system of rhombohedral hexalayer graphene (RHG)/hBN moire superlattices, which exhibit both integer and fractional quantum anomalous Hall effects with rich tunability including electric displacement field, perpendicular magnetic field and in-plane magnetic field. By tuning the electrical and magnetic fields at 0 < v < 1, we have observed a quantum phase transition showing a sign reversal of the Hall resistivity at finite magnetic fields. Surprisingly, the FCI state at v = 2/3 survives in the phase transitions, exhibiting a robust quantized Hall resistivity across both phases. Finally we have further demonstrated the indispensable role moire potential plays in the formation of the flat Chern band from a theoretical perspective. Our work has established RHG/hBN moire superlattices as a promising platform for exploring quasi-particles with fractional charge and non-Abelian anyons at zero magnetic field.

cond-mat.mes-hall↗

arXiv2025

Tuning competition between charge order and superconductivity in the square-lattice $t$-$t'$-$J$ model

Recently, a flurry of works have found strong competition between charge density wave (CDW) and superconductivity (SC) in the doped Hubbard and $t$-$J$ models on the square lattice. Interestingly, some recent results suggest that the electron-phonon coupling may suppress CDW order and enhance SC. In this work, we consider the square-lattice Hubbard model with the Holstein or Su-Schrieffer-Heeger electron-phonon coupling at the large-$U$ and antiadiabatic (infinite phonon frequency) limit, which gives an effective $t$-$J$ model with either a density attractive interaction $V$ or a $J_P$ term that contributes a larger spin exchange and a density repulsive interaction. To explore how these effective couplings may suppress CDW and give a SC, we implement the density matrix renormalization group simulation on the $t$-$t'$-$J$ model with $V$ or $J_P$ coupling. We focus on the {\it six-leg} cylinder system with the next-nearest-neighbor hopping $t'$, which hosts partially filled stripe and $d$-wave SC in phase diagram. By tuning $t'/t > 0$ and $V$ or $J_P$, we establish two quantum phase diagrams. In the SC phases, the increased $V$ or $J_P$ coupling can enhance the quasi-long-range SC order, consistent with some previous findings. Nonetheless, no SC emerges when the partially filled stripe phase disappears with increased $V$ or $J_P$. Instead, the system has a transition to either a phase-separation-like regime or a filled stripe phase. On the other hand, with increased $t'/t$, not only the partially filled stripe but the phase separation and filled stripe can also be tuned to SC phase. Our results suggest that although $V$ and $J_P$ couplings may strengthen hole binding, the hole dynamics controlled by $t'/t$ appears to play more crucial role for obtaining a SC in $t$-$J$ model.

cond-mat.str-el↗

arXiv2025

Promoting Shared Energy Storage Aggregation among High Price-Tolerance Prosumer: An Incentive Deposit and Withdrawal Service

Many residential prosumers exhibit a high price-tolerance for household electricity bills and a low response to price incentives. This is because the household electricity bills are not inherently high, and the potential for saving on electricity bills through participation in conventional Shared Energy Storage (SES) is limited, which diminishes their motivation to actively engage in SES. Additionally, existing SES models often require prosumers to take additional actions, such as optimizing rental capacity and bidding prices, which happen to be capabilities that typical household prosumers do not possess. To incentivize these high price-tolerance residential prosumers to participate in SES, a novel SES aggregation framework is proposed, which does not require prosumers to take additional actions and allows them to maintain existing energy storage patterns. Compared to conventional long-term operation of SES, the proposed framework introduces an additional short-term construction step during which the energy service provider (ESP) acquires control of the energy storage systems (ESS) and offers electricity deposit and withdrawal services (DWS) with dynamic coefficients, enabling prosumers to withdraw more electricity than they deposit without additional actions. Additionally, a matching mechanism is proposed to align prosumers' electricity consumption behaviors with ESP's optimization strategies. Finally, the dynamic coefficients in DWS and trading strategies are optimized by an improved deep reinforcement learning (DRL) algorithm. Case studies are conducted to verify the effectiveness of the proposed SES aggregation framework with DWS and the matching mechanism.

eess.SY↗

arXiv2025

Prediction Interval Construction Method for Electricity Prices

Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.

cs.LG↗

arXiv2025

Strong superconducting pairing strength and pseudogap features in a putative multiphase heavy-fermion superconductor CeRh2As2 by soft point-contact spectroscopy

CeRh2As2 is a newly discovered candidate of multiphase heavy-fermion superconductor (Tc=0.3 K) with intriguing physical properties. Here, we employ soft point-contact spectroscopy to investigate its energy gap behaviors in both the normal and superconducting states. The differential conductance below Tc reveals an estimated superconducting energy gap of 2ΔSC=0.24 meV and thus an extremely strong superconducting pairing strength 2ΔSC/kBTc=8.8, which is comparable to those of cuprates and iron-based high-Tc superconductors as well as infinite-layer nickelates. Above Tc, a well-defined pseudogap feature is manifested as a V-shaped dip in the differential conductance spanning an energy scale of 2Δg=0.95-3.0 meV. The pseudogap feature persists to the highest characteristic temperature of Tg=8-9 K and is gradually suppressed by magnetic field of Bg=9.0T regardless of its direction relative to the crystallographic axes. The observation of pseudogap features prior to the superconducting phase transition enriches the phase diagram of CeRh2As2 and provides a novel platform to study the interplay of unconventional superconductivity and pseudogap phenomena.

cond-mat.supr-con↗

arXiv2024

Enhanced Polarizability and Tunable Diamagnetic Shifts from Charged Localized Emitters in WSe2 on a Relaxor Ferroelectric

Strain modulation is a crucial way in engineering nanoscale materials. It is even more important for single photon emitters in layered materials, where strain can create quantum emitters and control their energies. Here we report the localized, charge-enhanced coupling between the charged localized emitters in monolayer tungsten diselenide (WSe2) to the piezoelectric relaxor ferroelectric substrate. In addition to the strain effect, we observe a gigantic polarizability volume with the enhancement factor up to 1010. The enormous polarizability leads to a large Quantum-confined Stark shift under a small variation of electric field, indicating the potential of integrating layered materials with functional substrates for quantum sensing. We further demonstrate the tunable diamagnetic shift and g-factor with strain varying by ~0.05%, which confirms the existence of enhanced interaction between the localized oscillating dipoles and the ferroelectric domains. Our results signify the prospect of charged quantum emitters in layered materials for quantum sciences and technology.

cond-mat.mes-hall↗

arXiv2024

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation

We tackle the challenge of open-vocabulary segmentation, where we need to identify objects from a wide range of categories in different environments, using text prompts as our input. To overcome this challenge, existing methods often use multi-modal models like CLIP, which combine image and text features in a shared embedding space to bridge the gap between limited and extensive vocabulary recognition, resulting in a two-stage approach: In the first stage, a mask generator takes an input image to generate mask proposals, and the in the second stage the target mask is picked based on the query. However, the expected target mask may not exist in the generated mask proposals, which leads to an unexpected output mask. In our work, we propose a novel approach named Prompt-guided Mask Proposal (PMP) where the mask generator takes the input text prompts and generates masks guided by these prompts. Compared with mask proposals generated without input prompts, masks generated by PMP are better aligned with the input prompts. To realize PMP, we designed a cross-attention mechanism between text tokens and query tokens which is capable of generating prompt-guided mask proposals after each decoding. We combined our PMP with several existing works employing a query-based segmentation backbone and the experiments on five benchmark datasets demonstrate the effectiveness of this approach, showcasing significant improvements over the current two-stage models (1% ~ 3% absolute performance gain in terms of mIOU). The steady improvement in performance across these benchmarks indicates the effective generalization of our proposed lightweight prompt-aware method.

cs.CV↗

arXiv2024

Adjusting auxiliary variables under approximate neighborhood interference

Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-world settings where interference between units is present. Network interference, in particular, has garnered significant attention. Structural models, like the linear-in-means model, are commonly used to describe interference; but they rely on the correct specification of the model, which can be restrictive. Recent advancements in the literature, such as the Approximate Neighborhood Interference (ANI) framework, offer more flexible approaches by assuming negligible interference from distant units. In this paper, we introduce a general framework for regression adjustment for the network experiments under the ANI assumption. This framework expands traditional regression adjustment by accounting for imbalances in network-based covariates, ensuring precision improvement, and providing shorter confidence intervals. We establish the validity of our approach using a design-based inference framework, which relies solely on randomization of treatment assignments for inference without requiring correctly specified outcome models.

stat.ME↗

arXiv2024

How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers

Pre-trained language models have been proven to possess strong base capabilities, which not only excel in in-distribution language modeling but also show powerful abilities in out-of-distribution language modeling, transfer learning and few-shot learning. Unlike existing work focusing on the influence of scale on base capabilities, our work examines the influence of architecture on those. Specifically, our concern is: How does architecture influence the base capabilities of pre-trained language models? In this work, we attempt to explain and reverse the decline in base capabilities caused by the architecture of FFN-Wider Transformers, seeking to provide some insights. Through analysis, we found the contribution ratio of Multi-Head Attention (a combination function) to pre-trained language modeling is a key factor affecting base capabilities. FFN-Wider Transformers reduce the contribution ratio of this combination function, leading to a decline in base capabilities. We confirmed this by experiments and proposed Combination Enhanced Architecture (CEA) to address the decline in base capabilities of such models. Significantly, we extended our explanation and CEA to Mixture of Experts (MoE) Transformers. We successfully achieved significant improvements in base capabilities on a 14B parameter MoE model, demonstrating the practical application value of our work. This also indicates that our analysis has a certain guiding significance for architecture analysis, architecture improvement and architecture design.

cs.CL↗

arXiv2024

New bounds of two hypergraph Ramsey problems

We focus on two hypergraph Ramsey problems. First, we consider the Erdős-Hajnal function $r_k(k+1,t;n)$. In 1972, Erdős and Hajnal conjectured that the tower growth rate of $r_k(k+1,t;n)$ is $t-1$ for each $2\le t\le k$. To finish this conjecture, it remains to show that the tower growth rate of $r_4(5,4;n)$ is three. We prove a superexponential lower bound for $r_4(5,4;n)$, which improves the previous best lower bound $r_4(5,4;n)\geq 2^{Ω(n^2)}$ from Mubayi and Suk (\emph{J. Eur. Math. Soc., 2020}). Second, we prove an upper bound for the hypergraph Erdős-Rogers function $f^{(k)}_{k+1,k+2}(N)$ that is an iterated $(k-3)$-fold logarithm in $N$ for each $k\geq 5$. This improves the previous upper bound that is an iterated $(k-13)$-fold logarithm in $N$ for $k\ge14$ due to Mubayi and Suk (\emph{J. London Math. Soc., 2018}), in which they conjectured that $f^{(k)}_{k+1,k+2}(N)$ is an iterated $(k-2)$-fold logarithm in $N$ for each $k\ge3$.

math.CO↗

arXiv2024

Sign structure of the $t$-$t^\prime$-$J$ model and its physical consequences

Understanding the doped Mott insulator is a central challenge in condensed matter physics. In this work, we first explicitly identify a new sign structure in the $t$-$t'$-$J$ model on the square lattice that replaces the conventional Fermi statistics for weakly interacting electrons. Then we show that the singular, i.e., the phase-string part of the sign structure in the partition function can be precisely turned off in a modified model. The density matrix renormalization group method is then employed to study these two models comparatively on finite-size systems, which is designed to unveil the consequences of the phase-string component. We find that the hole pairing is present not only in the quasi-long-range superconducting phase but also in the stripe phase of the $t$-$t'$-$J$ model. However, once the phase-string is switched off, both the superconducting and stripe orders together with the underlying hole pairing disappear. The corresponding ground state reduces to a trivial Fermi-liquid-like state with small hole Fermi pockets that is decoupled from the antiferromagnetic spin background. It is in sharp contrast to the original $t$-$t'$-$J$ model where large Fermi surfaces can be restored in the stripe phase found at $t'/t<0$ or the superconducting phase at $t'/t>0$ in the six-leg ladder calculation. Our study clearly demonstrates that the strong correlation effect in doped Mott insulator should be mainly attributed to the long-range quantum entanglement between the spin and charge, which is, non-perturbatively, beyond a simple spin-charge separation under the no double occupancy constraint.

cond-mat.str-el↗