arXiv subjects

Xiang Pan

Publications and source records attributed to Xiang Pan.

At least 19 recordsLinked to original sources

arXiv2026

Review of experimental studies of charmed meson decays at BESIII

Experimental measurements of different decays of charmed mesons have been extensively performed at BESIII. Precision measurements of absolute branching fractions of different decays, the decay constants of $D^+$ and $D^+_s$ mesons, hadronic form factors of $D$ transitions to light hadrons ($K$, $\pi$, $\eta$, $\eta^\prime$, $K^*(892)$, $\rho$, $\omega$, $\phi$, $K_1(1270)$, $f_0(980)$), $c\to s(d)$ Cabibbo-Kobayashi-Maskawa (CKM) matrix elements, tests of lepton flavor universality with various (semi)leptonic $D$ decays, precision measurements of strong phase difference between $D^0$ and $\bar D^0$ decays, amplitude analyses of multibody hadronic $D_{(s)}$ decays, search for rare $D$ decays have been reported. The reported results offer important information to test different theoretical calculations, to test the unitarity of the CKM matrix, and to search for new physics effects beyond the standard model (SM). This paper reviews experimental studies of different decays of $D^0$, $D^+$, and $D^+_s$ as well as their excitations at BESIII as of April 15, 2026. Based on existing results of (semi)leptonic $D$ decays from all experiments, we have presented the most precise averages for the CKM matrix elements $|V_{cs}|=0.9648\pm0.009\pm0.0036$ and $|V_{cd}|=0.2259\pm0.0014\pm0.0013$, the decay constants of $D^+$ and $D^+_s$ $f_{D^+}=(213.1\pm2.0\pm1.5)$ MeV and $f_{D^+_s}=(253.2\pm1.2\pm1.6)$ MeV, as well as the hadronic form factors $f^{D\to K}_+(0)=0.7342\pm0.0007\pm0.0008$, $f^{D\to \pi}_+(0)=0.6337\pm0.0053\pm0.0037$, $f^{D\to \eta}_+(0)=0.351\pm0.009\pm0.005$, $f^{D\to \eta^\prime}_+(0)=0.263\pm0.025\pm0.006$, $f^{D_s\to \eta}_+(0)=0.4653\pm0.0058\pm0.0069$, $f^{D_s\to \eta^\prime}_+(0)=0.535\pm0.020\pm0.011$, and $f^{D_s\to K^0}_+(0)=0.627\pm0.036\pm0.009$, where the first and second uncertainties are statistical and systematic, respectively.

hep-ex↗

arXiv2026

Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening

Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.

q-bio.QM↗

arXiv2025

Antarctic TianMu Staring Observation Project I: Overview and Implementation of the Prototype Telescope

Wide-field rapid sky surveys serve as critical observational methods for time-domain astronomical research. The Antarctic region, with several months of continuous dark nights annually, is an ideal site for time-domain astronomical observations. The Antarctic TianMu Staring Observation Project aims to deploy a fleet of small telescopes, adopting an array observation model to conduct time-domain optical observations in Antarctica, featuring wide-sky coverage, high-cadence sampling, long-period staring, and simultaneous multi-band measurements. Considering the severe challenges optical telescopes face in Antarctica, including extremely low temperatures, unattended operation, and limited power supply and network transmission, we have designed and developed the Antarctic TianMu prototype telescope based on drift-scan charge-coupled device technology. In October 2022, our prototype (with an aperture of 18 cm), named AT-Proto was transported to Zhongshan Station in Antarctica aboard China's 39th Antarctic Research Expedition. It has since operated stably and reliably in the frigid environment for over two years, demonstrating the significant advantages of this technology in polar astronomical observations. The experimental observation results of AT-Proto provide a solid foundation for the subsequent construction of a time-domain astronomy observation array in Antarctica.

astro-ph.IM↗

arXiv2025

Fingerprinting SDKs for Mobile Apps and Where to Find Them: Understanding the Market for Device Fingerprinting

This paper presents a large-scale analysis of fingerprinting-like behavior in the mobile application ecosystem. We take a market-based approach, focusing on third-party tracking as enabled by applications' common use of third-party SDKs. Our dataset consists of over 228,000 SDKs from popular Maven repositories, 178,000 Android applications collected from the Google Play store, and our static analysis pipeline detects exfiltration of over 500 individual signals. To the best of our knowledge, this represents the largest-scale analysis of SDK behavior undertaken to date. We find that Ads SDKs (the ostensible focus of industry efforts such as Apple's App Tracking Transparency and Google's Privacy Sandbox) appear to be the source of only 30.56% of the fingerprinting behaviors. A surprising 23.92% originate from SDKs whose purpose was unknown or unclear. Furthermore, Security and Authentication SDKs are linked to only 11.7% of likely fingerprinting instances. These results suggest that addressing fingerprinting solely in specific market-segment contexts like advertising may offer incomplete benefit. Enforcing anti-fingerprinting policies is also complex, as we observe a sparse distribution of signals and APIs used by likely fingerprinting SDKs. For instance, only 2% of exfiltrated APIs are used by more than 75% of SDKs, making it difficult to rely on user permissions to control fingerprinting behavior.

cs.CR↗

arXiv2025

LOP: Learning Optimal Pruning for Efficient On-Demand MLLMs Scaling

Structural pruning techniques are essential for deploying multimodal large language models (MLLMs) across various hardware platforms, from edge devices to cloud servers. However, current pruning methods typically determine optimal strategies through iterative search processes, resulting in substantial computational overhead for on-demand MLLMs adaptation. To address this challenge, we propose LOP, an efficient neural pruning framework that learns optimal pruning strategies from the target pruning constraint, eliminating the need for computationally expensive search-based methods. LOP approach trains autoregressive neural networks (NNs) to directly predict layer-wise pruning strategies adaptive to the target pruning constraint, eliminating the time-consuming iterative searches. Experimental results across multiple tasks show that LOP outperforms state-of-the-art pruning methods in various metrics while achieving up to three orders of magnitude speedup.

cs.CV↗

arXiv2025

Recent results of (semi)leptonic decays of charm hadrons at BESIII

In this talk, we report the recent measurements of (semi)leptonic decays of charm mesons from the BESIII experiment, including $D^+\to\mu^+\nu_\mu$, $D^+\to\tau^+\nu_\tau$, $D^+\to\eta^\prime\ell^+\nu_\ell$, and $D^{0(+)}\to\bar K\ell^+\nu_\ell$ (where $\ell=e, \mu$). These measurements provide the most precise or first determinations to date of the CKM matrix elements $|V_{cs(d)}|$, the decay constant $f_{D^+}$, and the hadronic form factors $f_+^{D^+\to \eta^\prime}(0)$ and $f_+^{D\to \bar K}(0)$. Lepton flavor universality of $e-\mu$ and $\tau-\mu$ are also tested with these decays. Additionally, we present the first observation of the rare beta decay $\Lambda_c^+\to ne^+\nu_e$ with machine learning of Graph Neural Network.

hep-ex↗

arXiv2025

Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction

Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations. In contrast, BioBridge designs an Inductive-Associative pipeline inspired by the workflow of scientists who base their accumulated expertise on drawing insights into novel drug-target pairs from weakly related references. BioBridge predicts novel drug-target interactions using limited sequence data, incorporating multi-level encoders with adversarial training to accumulate transferable binding principles. On these principles basis, BioBridge employs a dynamic prototype meta-learning framework to associate insights from weakly related annotations, enabling robust predictions for previously unseen drug-target pairs. Extensive experiments demonstrate that BioBridge surpasses existing models, especially for unseen proteins. Notably, when only homologous protein binding data is available, BioBridge proves effective for virtual screening of the epidermal growth factor receptor and adenosine receptor, underscoring its potential in drug discovery.

cs.LG↗

arXiv2024

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness

Deep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in unclear attributions of model behavior. Recent advances in monosemanticity, where neurons correspond to consistent and distinct semantics, have significantly improved interpretability but are commonly believed to compromise accuracy. In this work, we challenge the prevailing belief of the accuracy-interpretability tradeoff, showing that monosemantic features not only enhance interpretability but also bring concrete gains in model performance. Across multiple robust learning scenarios-including input and label noise, few-shot learning, and out-of-domain generalization-our results show that models leveraging monosemantic features significantly outperform those relying on polysemantic features. Furthermore, we provide empirical and theoretical understandings on the robustness gains of feature monosemanticity. Our preliminary analysis suggests that monosemanticity, by promoting better separation of feature representations, leads to more robust decision boundaries. This diverse evidence highlights the generality of monosemanticity in improving model robustness. As a first step in this new direction, we embark on exploring the learning benefits of monosemanticity beyond interpretability, supporting the long-standing hypothesis of linking interpretability and robustness. Code is available at \url{https://github.com/PKU-ML/Beyond_Interpretability}.

cs.LG↗

arXiv2024

Discovery of a Damped Ly$\alpha$ Absorber in the Circumnuclear Zone of the FeLoBAL Quasar SDSS J083942.11+380526.3

SDSS J083942.11+380526.3 ($z=2.315$) is a FeLoBAL quasar that exhibits visible Balmer absorption lines (H$\alpha$), implying a significant $n=2$ population. The quasar also shows an array of absorption lines, including \oi, \niii, \feii, \mgii, \aliii\, to \civ\ and \nv. The high-ionization absorption lines such as \civ\ and \siiv\ are revealed by slightly blueshifted BAL troughs. The resonance doublets such as \mgii\ and \aliii\ are saturated but did not reached zero intensity which indicates that the BLR is partially covered. Overall, however, the absorption is predominantly from low-ionization \feii\ lines, emitted from ground and excited states up to at least 3.814 eV. This implies that the absorbing gas spans the hydrogen ionization front and extends into the partially ionized zone where neutral hydrogen is certainly present. Notably, the hydrogen line spectrum of the quasar shows no signature of expected Ly$\alpha$ absorption. Instead, the line spectrum shows an unusual Ly$\alpha$ emission characterized by a fully filled emission line spectrum which is a composite of a strong narrow core superposed on a weak broad base. Taking into account the effect of partial covering to BLR, we have extracted a strong DLA trough in Ly$\alpha$ emission region. To fit the spectrum, we performed photoionized model calculations and compared them to the observations. We found that photoionization modeling using CLOUDY can successfully reproduce the main characteristics of the quasar spectrum, and the predicted neutral hydrogen column density arising from the clouds responsible for the low-ionization absorption provides a good match to the extracted DLA trough. This indicates that both the DLA and the low-ionization absorption arise from the same medium that is roughly collocated with the dusty torus.

astro-ph.GA↗

arXiv2024

Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning

We revisit data selection in a modern context of finetuning from a fundamental perspective. Extending the classical wisdom of variance minimization in low dimensions to high-dimensional finetuning, our generalization analysis unveils the importance of additionally reducing bias induced by low-rank approximation. Inspired by the variance-bias tradeoff in high dimensions from the theory, we introduce Sketchy Moment Matching (SkMM), a scalable data selection scheme with two stages. (i) First, the bias is controlled using gradient sketching that explores the finetuning parameter space for an informative low-dimensional subspace $\mathcal{S}$; (ii) then the variance is reduced over $\mathcal{S}$ via moment matching between the original and selected datasets. Theoretically, we show that gradient sketching is fast and provably accurate: selecting $n$ samples by reducing variance over $\mathcal{S}$ preserves the fast-rate generalization $O(\dim(\mathcal{S})/n)$, independent of the parameter dimension. Empirically, we concretize the variance-bias balance via synthetic experiments and demonstrate the effectiveness of SkMM for finetuning in real vision tasks.

cs.LG↗

arXiv2024

AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.

cs.CV↗

arXiv2024

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQChallenge-CVPR-NTIRE2024.

eess.IV↗

arXiv2024

Bridging Domains with Approximately Shared Features

Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strategy for feature selection. Existing literature is somewhat paradoxical: some advocate for learning invariant features from source domains, while others favor more diverse features. To address the challenge, we propose a statistical framework that distinguishes the utilities of features based on the variance of their correlation to label $y$ across domains. Under our framework, we design and analyze a learning procedure consisting of learning approximately shared feature representation from source tasks and fine-tuning it on the target task. Our theoretical analysis necessitates the importance of learning approximately shared features instead of only the strictly invariant features and yields an improved population risk compared to previous results on both source and target tasks, thus partly resolving the paradox mentioned above. Inspired by our theory, we proposed a more practical way to isolate the content (invariant+approximately shared) from environmental features and further consolidate our theoretical findings.

stat.ML↗

arXiv2024

Transferable Learned Image Compression-Resistant Adversarial Perturbations

Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are primarily applied to uncompressed images or compressed images by the traditional image compression method, i.e., JPEG, limited studies have investigated the robustness of models for image classification in the context of DNN-based image compression. With the rapid evolution of advanced image compression, DNN-based learned image compression has emerged as the promising approach for transmitting images in many security-critical applications, such as cloud-based face recognition and autonomous driving, due to its superior performance over traditional compression. Therefore, there is a pressing need to fully investigate the robustness of a classification system post-processed by learned image compression. To bridge this research gap, we explore the adversarial attack on a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules. Furthermore, to enhance the transferability of perturbations across various quality levels and architectures of learned image compression models, we introduce a saliency score-based sampling method to enable the fast generation of transferable perturbation. Extensive experiments with popular attack methods demonstrate the enhanced transferability of our proposed method when attacking images that have been post-processed with different learned image compression models.

cs.CV↗

arXiv2023

Corner-to-Center Long-range Context Model for Efficient Learned Image Compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the \textbf{Corner-to-Center transformer-based Context Model (C$^3$M)} designed to enhance context and latent predictions and improve rate-distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored.

eess.IV↗

arXiv2023

PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting

Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the \textbf{PostRainBench}, a comprehensive multi-variable NWP post-processing benchmark, and \textbf{CAMT}, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3\%, 4.7\%, and 26.8\% in rain CSI and improvements of 15.6\%, 17.4\%, and 31.8\% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at https://github.com/yyyujintang/PostRainBench.

cs.LG↗

arXiv2023

Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations

Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where any object in the reconstructed images is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Further, leveraging the insensitivity of high-frequency components to human vision, we introduce Imperceptibility Constraint (IC) to ensure that the perturbations remain inconspicuous. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness. In addition, we provide several findings and suggestions for designing future defenses.

cs.CV↗

arXiv2022

Accelerating Dataset Distillation via Model Augmentation

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two model augmentation techniques, i.e. using early-stage models and parameter perturbation to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20x speedup and comparable performance on par with state-of-the-art methods.

cs.LG↗