arXiv subjects

Wei Du

Publications and source records attributed to Wei Du.

At least 55 records · Page 3Linked to original sources

arXiv2025

Constraining interacting dark energy models with the halo concentration-mass relation

The interacting dark energy (IDE) model is a promising alternative cosmological model which has the potential to solve the fine-tuning and coincidence problems by considering the interaction between dark matter and dark energy. Previous studies have shown that the energy exchange between the dark sectors in this model can significantly affect the dark matter halo properties. In this study, utilising a large set of cosmological $N$-body simulations, we analyse the redshift evolution of the halo concentration-mass ($c$-$M$) relation in the IDE model, and show that the $c$-$M$ relation is a sensitive proxy of the interaction strength parameter $ξ_2$, especially at lower redshifts. Furthermore, we construct parametrized formulae to quantify the dependence of the $c$-$M$ relation on $ξ_2$ at redshifts ranging from $z=0$ to $0.6$. Our parametrized formulae provide a useful tool in constraining $ξ_2$ with the observational $c$-$M$ relation. As a first attempt, we use the data from X-ray, gravitational lensing, and galaxy rotational curve observations and obtain a tight constraint on $ξ_2$, i.e. $ξ_2 = 0.071 \pm 0.034$. Our work demonstrates that the halo $c$-$M$ relation, which reflects the halo assembly history, is a powerful probe to constrain the IDE model.

astro-ph.CO↗

arXiv2025

Neutralizing Optical Defects in GeSn

Reports of photoluminescence from GeSn grown on Ge substrates by molecular beam epitaxy have been limited. We find that one limiting factor to observing photoluminescence is due to localized defect states marked by photoluminescence at 2400 nm and originating from the Ge substrate and buffer layer. In this study, we report on an optical study utilizing doped Ge(001) substrates to effectively suppress defect-related photoluminescence in GeSn layers by filling localized defect trap states. For this experiment, a GeSn layer with Sn content up to 10.5% was grown on a doped Ge(001) substrate. Analysis of the physics of the photoluminescence spectrum collected from the GeSn thin film shows an emission at the expected wavelength of 2300 nm for 10.5% Sn content and the absence of the typically observed defect related signal at 2400 nm. This understanding is further confirmed using short pulse optical excitation of the GeSn grown on undoped Ge substrates.

cond-mat.mtrl-sci↗

arXiv2025

The Challenge of Teaching Reasoning to LLMs Without RL or Distillation

Reasoning-capable language models achieve state-of-the-art performance in diverse complex tasks by generating long, explicit Chain-of-Thought (CoT) traces. While recent works show that base models can acquire such reasoning traces via reinforcement learning or distillation from stronger models like DeepSeek-R1, previous works demonstrate that even short CoT prompting without fine-tuning is able to improve reasoning. We ask whether long CoT can be induced in a base model using only prompting or minimal tuning. Using just 20 long CoT examples from the reasoning model \texttt{QwQ-32B-Preview}, we lightly fine-tune the base model \texttt{Qwen2.5-32B}. The resulting model outperforms the much larger \texttt{Qwen2.5-Math-72B-Instruct}, showing that a handful of high-quality examples can unlock strong reasoning capabilities. We further explore using CoT data from non-reasoning models and human annotators, enhanced with prompt engineering, multi-pass editing, and structural guidance. However, neither matches the performance of reasoning model traces, suggesting that certain latent qualities of expert CoT are difficult to replicate. We analyze key properties of reasoning data, such as problem difficulty, diversity, and answer length, that influence reasoning distillation. While challenges remain, we are optimistic that carefully curated human-written CoT, even in small quantities, can activate reasoning behaviors in base models. We release our human-authored dataset across refinement stages and invite further investigation into what makes small-scale reasoning supervision so effective.

cs.AI↗

arXiv2025

An H$α$ Cloud in the HI Tail: Recent Star Formation in the Outskirts of NGC 4258 Revealed by Nanshan 1-m Telescope

We present first-light deep H$α$ imaging taken with the Nanshan 1-meter wide-field telescope on the local galaxy NGC 4258, alongside archival data from Hubble Space telescope (HST), Westerbork Synthesis Radio Telescope, and The Dark Energy Camera Legacy Survey. The H$α$ image shows ongoing star formation not only inside the galaxy but also in an HI cloud in the eastern HI tail, which is roughly 16 kpc away from the main galaxy. The HST images reveal several ultra-blue compact objects ($\rm F555W - F814W <-0.5 mag,\, FWHM\sim 0.2''$) in the H$α$ bright region, aligned with the HI tail, suggesting the presence of young open cluster candidate in the HI tail. Our results suggest that wide field H$α$ imaging is a valuable tool for investigating recent star formation in the extended regions of NGC 4258. Furthermore, the star formation in diffuse HI tails could highlight an potential aspect of galaxy stellar halo formation, warranting further investigation of the impact of star formation in halos on galaxy evolution.

astro-ph.GA↗

arXiv2025

Ge0.95Sn0.05 on Si avalanche photodiode with Spectral Response Cutoff at 2.14 micrometer

GeSn-based avalanche photodiode (APD) operating in shortwave infrared (SWIR) wavelength was demonstrated in this work. A separate absorption and charge multiplication (SACM) structure was employed to take advantage of long wavelength absorption in GeSn and low impact ionization ratio of Si. Due to lattice mismatch between Si and GeSn that would degrade GeSn material quality if with direct growth, a 240-nm-thick Ge buffer was utilized which simultaneously allows for the transporting photo generated electrons from GeSn absorber to Si multiplication layer. Spectral response showed the cut off wavelength beyond 2.1 μm at room temperature. Dart current and capacitance-voltage measurements indicated a punch-through voltage of -10 V. The measured responsivities were 0.55 A/W and 0.34 A/W under 1.55 μm and 1.9 μm excitation lasers, respectively. The maximum gain was obtained as 3.44 at 77 K under 1.9 μm laser. Even at 250 K, the calculated gain was greater than unity. Simulation of electric field distribution revealed that the GeSn is partially depleted at operating voltages, which can be improved by reducing the background doping levels in GeSn absorber and Ge buffer layer.

physics.app-ph↗

arXiv2025

Study of electronic band alignment in SiGeSn/GeSn quantum well via internal photoemission effect

SiGeSn-based optoelectronic devices, which operate across a broad infrared wavelength range, have attracted significant attention, particularly heterostructures utilizing quantum wells are widely utilized. In these structures, band alignment type and barrier height are crucial for carrier confinement, making them highly desirable information to obtain. This work leverages the internal photoemission effect to extract effective barrier heights from a Si0.024Ge0.892Sn0.084 / Ge0.882Sn0.118 single quantum well structure, which was pseudomorphically grown on Ge0.9Sn0.1 and Ge buffered Si substrate. The extracted effective barrier heights are approximately 22{plus minus}2 and 50{plus minus}2 meV for electrons and holes, respectively. Moreover, we have identified the type-I band alignment between GeSn well and SiGeSn barrier, as indicated by an internal photoemission threshold of 555 {plus minus} 1 meV.

physics.app-ph↗

arXiv2025

Direct Bandgap Photoluminescence of GeSn grown on Si(100) substrate by Molecular Beam Epitaxy Growth

Group IV alloys of GeSn have gained significant attention for electronic and optoelectronic applications on a Si platform due to their compatibility with existing CMOS technology, tunable band structure, and potential for a direct bandgap at high Sn concentrations. However, synthesizing Sn-rich GeSn structures remains challenging due to the low solid solubility of Sn in Ge (less than 1%) and the substantial lattice mismatch ( about 14%) between Sn and Ge. In this work, we demonstrate the successful growth of high-quality, relaxed GeSn layers with Sn contents of 9.2% and 11.4% on Si(100) substrates via molecular beam epitaxy (MBE). As far as we know, this is the first report of direct bandgap photoluminescence observed from MBE-grown GeSn films without post-growth annealing. Structural characterizations including X-ray diffraction (XRD), secondary ion mass spectrometry (SIMS), and transmission electron microscopy (TEM) confirm uniform Sn incorporation with minimal defect formation. Atomic force microscopy (AFM) reveals smooth surfaces with low roughness. Temperature-dependent photoluminescence (PL) measurements further confirm direct bandgap emission, representing a new stage in the development of MBE-grown GeSn.

physics.app-ph↗

arXiv2025

AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset

This paper presents our winning submission to the AI Mathematical Olympiad - Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art mathematical reasoning models relies on three key pillars. First, we create a large-scale dataset comprising 540K unique high-quality math problems, including olympiad-level problems, and their 3.2M long-reasoning solutions. Second, we develop a novel method to integrate code execution with long reasoning models through iterative training, generation, and quality filtering, resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we create a pipeline to train models to select the most promising solution from many candidates. We show that such generative solution selection (GenSelect) can significantly improve upon majority voting baseline. Combining these ideas, we train a series of models that achieve state-of-the-art results on mathematical reasoning benchmarks. To facilitate further research, we release our code, models, and the complete OpenMathReasoning dataset under a commercially permissive license.

cs.AI↗

arXiv2025

Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm

Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of its high efficiency. Although various methods have been developed, template fitting is still adopted as one of the most popular approaches. Its accuracy strongly depends on the quality of the Spectral Energy Distribution (SED) templates, which can be calibrated using broadband photometric data from galaxies with known spectroscopic redshifts. Such calibration is expected to improve photometric redshift accuracy, as the calibrated templates will align with observed photometric data more closely. The upcoming China Space Station Survey Telescope (CSST) is one of the Stage IV surveys, which aiming for high precision cosmological studies. To improve the accuracy of photometric redshift estimation for CSST, we calibrated the CWW+KIN templates using a perturbation algorithm with broadband photometric data from the CSST mock catalog. This calibration used a training set consisting of approximately 4,500 galaxies, which is 10% of the total galaxy sample. The outlier fraction and scatter of the photometric redshifts derived from the calibrated templates are 2.55% and 0.036, respectively. Compared to the CWW+KIN templates, these values are reduced by 34% and 23%, respectively. This demonstrates that SED templates calibrated with a small training set can effectively optimize photometric redshift accuracy for future large-scale surveys like CSST, especially with limited spectral training data.

astro-ph.CO↗

arXiv2025

Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture

Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization.

cs.LG↗

arXiv2025

Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models

This study uses deep-learning models to predict city partition crime counts on specific days. It helps police enhance surveillance, gather intelligence, and proactively prevent crimes. We formulate crime count prediction as a spatiotemporal sequence challenge, where both input data and prediction targets are spatiotemporal sequences. In order to improve the accuracy of crime forecasting, we introduce a new model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. We conducted a comparative analysis to access the effects of various data sequences, including raw and binned data, on the prediction errors of four deep learning forecasting models. Directly inputting raw crime data into the forecasting model causes high prediction errors, making the model unsuitable for real - world use. The findings indicate that the proposed CNN-LSTM model achieves optimal performance when crime data is categorized into 10 or 5 groups. Data binning can enhance forecasting model performance, but poorly defined intervals may reduce map granularity. Compared to dividing into 5 bins, binning into 10 intervals strikes an optimal balance, preserving data characteristics and surpassing raw data in predictive modelling efficacy.

cs.LG↗

arXiv2025

A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network

Speaker verification has been widely used in many authentication scenarios. However, training models for speaker verification requires large amounts of data and computing power, so users often use untrustworthy third-party data or deploy third-party models directly, which may create security risks. In this paper, we propose a backdoor attack for the above scenario. Specifically, for the Siamese network in the speaker verification system, we try to implant a universal identity in the model that can simulate any enrolled speaker and pass the verification. So the attacker does not need to know the victim, which makes the attack more flexible and stealthy. In addition, we design and compare three ways of selecting attacker utterances and two ways of poisoned training for the GE2E loss function in different scenarios. The results on the TIMIT and Voxceleb1 datasets show that our approach can achieve a high attack success rate while guaranteeing the normal verification accuracy. Our work reveals the vulnerability of the speaker verification system and provides a new perspective to further improve the robustness of the system.

cs.CR↗

arXiv2025

Cross-Survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis

This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam survey (HSC). We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to convert low-quality observational images into high-quality counterparts. Our model successfully transformed DECaLS images into pseudo-HSC images, yielding impressive results and significantly enhancing the identification of complex structures, such as galaxy spiral arms and tidal tails, which may have been overlooked in the original DECaLS images. Moreover, Pix2WGAN effectively addresses issues like artifacts, noise, and blurriness in both source and target images. In addition to the basic Pix2WGAN model, we further developed an advanced architecture called Cascaded Pix2WGAN, which incorporates a multi-stage training mechanism designed to bridge the quality gap between SDSS and HSC images, demonstrating similarly promising outcomes. We systematically assessed the similarity between the model-generated pseudo-HSC images and actual HSC images using various metrics, including Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with perceptual metrics such as Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID). The results indicate that images transformed by our model outperform both the original SDSS and DECaLS images across nearly all evaluation metrics. Our research is expected to provide significant technical support for astronomical data analysis, cross-survey image integration, and high-precision astrometry.

astro-ph.IM↗

arXiv2025

Observational evidence of anisotropic changes apparent resistivity before strong earthquakes

Using a method based on normalized monthly variation rate, we studied resistivity data of seven observation stations before the events in the epicenter areas of two strong earthquakes. The relationship between variation of anisotropic apparent resistivity and the azimuth of the maximum principal stress is analyzed. The study shows that significant apparent resistivity variation occurs in the direction that is perpendicular to the azimuth of the maximum principal stress while only small fluctuation are recorded in the direction of the maximum principal stress. We surmise that the variation of anisotropic resistivity occurs in the late stage of the development of a strong earthquake, which can be observed in the epicenter area. If the density of the observation stations is increased and the direction of the observed resistivity is right, the epicenter of an earthquake location may be estimated by the observed resistivity anomaly.

physics.geo-ph↗

arXiv2025

Quantum Twin Interferometers

Quantum-correlated interferometer is a newly emerging tool in quantum technology that offers classical-limit-breaking phase sensitivity. But to date, there exists a configurational bottleneck for its practicability due to the low phase-sensitive photon numbers limited by the current detection strategies. Here we establish an innovative development termed as ``quantum twin interferometer'' with dual pairs of entangled twin beams arranged in the parallel configuration, allowing fully exploits the quantum resource through the new configuration of entangled detection. We observe the distributed phase sensing with 3 dB quantum noise reduction in phase-sensing power at the level of milliwatts, which advances the record of signal-to-noise ratio so far achieved in photon-correlated interferometers by three orders of magnitude. The developed techniques in this work can be used to revolutionize a diversity of quantum devices requiring phase measurement.

physics.atom-ph↗

arXiv2024

AutoGFI: Streamlined Generalized Fiducial Inference for Modern Inference Problems in Models with Additive Errors

The concept of fiducial inference was introduced by R. A. Fisher in the 1930s to address the perceived limitations of Bayesian inference, particularly the need for subjective prior distributions in cases with limited prior information. However, Fisher's fiducial approach lost favor due to complications, especially in multi-parameter problems. With renewed interest in fiducial inference in the 2000s, generalized fiducial inference (GFI) emerged as a promising extension of Fisher's ideas, offering new solutions for complex inference challenges. Despite its potential, GFI's adoption has been hindered by demanding mathematical derivations and complex implementation requirements, such as Markov Chain Monte Carlo (MCMC) algorithms. This paper introduces AutoGFI, a streamlined variant of GFI designed to simplify its application across various inference problems with additive noise. AutoGFI's accessibility lies in its simplicity-requiring only a fitting routine-making it a feasible option for a wider range of researchers and practitioners. To demonstrate its efficacy, AutoGFI is applied to three challenging problems: tensor regression, matrix completion, and network cohesion regression. These case studies showcase AutoGFI's competitive performance against specialized solutions, highlighting its potential to broaden the application of GFI in practical domains, ultimately enriching the statistical inference toolkit.

stat.ME↗

arXiv2024

UOR: Universal Backdoor Attacks on Pre-trained Language Models

Backdoors implanted in pre-trained language models (PLMs) can be transferred to various downstream tasks, which exposes a severe security threat. However, most existing backdoor attacks against PLMs are un-targeted and task-specific. Few targeted and task-agnostic methods use manually pre-defined triggers and output representations, which prevent the attacks from being more effective and general. In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization. Specifically, we define poisoned supervised contrastive learning which can automatically learn the more uniform and universal output representations of triggers for various PLMs. Moreover, we use gradient search to select appropriate trigger words which can be adaptive to different PLMs and vocabularies. Experiments show that our method can achieve better attack performance on various text classification tasks compared to manual methods. Further, we tested our method on PLMs with different architectures, different usage paradigms, and more difficult tasks, which demonstrated the universality of our method.

cs.CL↗

arXiv2024

Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review

Language Models (LMs) are becoming increasingly popular in real-world applications. Outsourcing model training and data hosting to third-party platforms has become a standard method for reducing costs. In such a situation, the attacker can manipulate the training process or data to inject a backdoor into models. Backdoor attacks are a serious threat where malicious behavior is activated when triggers are present, otherwise, the model operates normally. However, there is still no systematic and comprehensive review of LMs from the attacker's capabilities and purposes on different backdoor attack surfaces. Moreover, there is a shortage of analysis and comparison of the diverse emerging backdoor countermeasures. Therefore, this work aims to provide the NLP community with a timely review of backdoor attacks and countermeasures. According to the attackers' capability and affected stage of the LMs, the attack surfaces are formalized into four categorizations: attacking the pre-trained model with fine-tuning (APMF) or parameter-efficient fine-tuning (APMP), attacking the final model with training (AFMT), and attacking Large Language Models (ALLM). Thus, attacks under each categorization are combed. The countermeasures are categorized into two general classes: sample inspection and model inspection. Thus, we review countermeasures and analyze their advantages and disadvantages. Also, we summarize the benchmark datasets and provide comparable evaluations for representative attacks and defenses. Drawing the insights from the review, we point out the crucial areas for future research on the backdoor, especially soliciting more efficient and practical countermeasures.

cs.CR↗