arXiv subjects

Jing Feng

Publications and source records attributed to Jing Feng.

At least 19 recordsLinked to original sources

arXiv2026

Seiberg dualities for quiver gauge theories

We study Seiberg duality for quiver gauge theories with fundamental, bifundamental, and rank-two tensor matter. The existence of a Seiberg dual description places strong constraints on the spectrum of undressed mesons, which is naturally encoded in a factorization equation for the matrix Hilbert series. We derive this factorization from a closed-form large-$N$ superconformal index for general quiver gauge theories with these matter contents. We apply the method to two-node quivers and recover known $SU$--$SU$ and $SO$--$USp$ product-group dualities. We also discuss finite-$N$ consistency conditions from anomaly and operator-spectrum matching.

hep-th↗

arXiv2026

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike conventional uncertainty estimates, the GMD employs full covariance matrices to explicitly reveal parameter couplings and multi-modal likelihood structures. The network is trained by minimizing the negative log-likelihood via a surjective parameterization that hard-encodes all GMD constraints, thereby approximating the true likelihood. We validate the method on five numerical examples with increasing complexity, including systems driven by fractional Gaussian and L\'evy noises, oscillators with colored noise, coupled neurons under different observability, and an aeroelastic airfoil with unidentifiable stochastic disturbances. Results demonstrate that PENN-GMD accurately recovers likelihood distributions, captures parameter couplings, and naturally diagnoses non-identifiability through variance broadening or mode splitting. These capabilities establish PENN-GMD as a practical tool for uncertainty-aware parameter identification in complex stochastic systems where conventional likelihood-based methods are infeasible.

stat.ML↗

arXiv2026

Kimi K3: Open Frontier Intelligence

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is built on Kimi Delta Attention and Attention Residuals, which improve information flow across sequence length and model depth. Together with Stable LatentMoE, which effectively activates 16 of 896 routed experts per token, and refined training and data recipes, these advances yield an approximately 2.5x improvement in overall scaling efficiency over Kimi K2. Post-training highlights reinforcement learning across general, agentic, and coding domains and multiple reasoning-effort levels, enabling compositional generalization and robust long-horizon execution. At 2.8T scale, Kimi K3 is supported by infrastructure advances in multiple areas: algorithm-system co-design for KDA, perfectly balanced expert-parallel training with efficient memory management, million-token agentic RL with persistent rollout and sandbox states, and deployment innovations. Extensive evaluations show that Kimi K3 achieves frontier-level performance across long-horizon coding, agentic, knowledge, reasoning, and vision tasks. While its overall performance still trails the most powerful proprietary models, namely Claude Fable 5 and GPT-5.6 Sol, Kimi K3 consistently outperforms other open and proprietary models evaluated in our suite. We release the full Kimi K3 model weights to facilitate future research and accelerate the broader deployment and adoption of frontier intelligence.

cs.CL↗

arXiv2026

A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions

Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems. However, current numerical methods lack parallel computation capabilities across varying conditions, severely limiting comprehensive parameter exploration and transient analysis. This paper introduces a deep learning-based pseudo-analytical probability solution (PAPS) that, via a single training process, simultaneously resolves transient FPE solutions for arbitrary multi-modal initial distributions, system parameters, and time points. The core idea is to unify initial, transient, and stationary distributions via Gaussian mixture distributions (GMDs) and develop a constraint-preserving autoencoder that bijectively maps constrained GMD parameters to unconstrained, low-dimensional latent representations. In this representation space, the panoramic transient dynamics across varying initial conditions and system parameters can be modeled by a single evolution network. Extensive experiments on paradigmatic systems demonstrate that the proposed PAPS maintains high accuracy while achieving inference speeds four orders of magnitude faster than GPU-accelerated Monte Carlo simulations. This efficiency leap enables previously intractable real-time parameter sweeps and systematic investigations of stochastic bifurcations. By decoupling representation learning from physics-informed transient dynamics, our work establishes a scalable paradigm for probabilistic modeling of multi-dimensional, parameterized stochastic systems.

physics.comp-ph↗

arXiv2026

Simultaneous super-resolution and optical sectioning with four-beam interference structured illumination microscopy (4I-SIM)

Structured illumination microscopy (SIM) has emerged as a widely adopted super-resolution fluorescence imaging modality, offering high speed, low phototoxicity, large field-of-view, and compatibility with conventional probes. However, when applied to thick or scattering specimens, conventional two-dimensional SIM (2D-SIM) suffers from the missing cone problem in its optical transfer function, resulting in prominent out-of-focus background and severe reconstruction artifacts that compromise image fidelity. Here, we present four-beam interference structured illumination microscopy (4I-SIM), which introduces additional interference orders to expand lateral frequency support and compensate the axial missing cone simultaneously. This strategy achieves artifact-free super-resolution with intrinsic optical sectioning, effectively overcoming the fundamental limitation of 2D-SIM without additional acquisition overhead. Experimental validation across diverse thick fixed and live specimens demonstrates that 4I-SIM delivers nearly twofold lateral resolution enhancement and substantially improved sectioning compared with its 2D counterpart, achieving lateral and axial resolutions of 103 nm and 336 nm, respectively. In particular, 4I-SIM reveals mitochondrial remodeling and apoptosis under high-glucose stress with millisecond temporal resolution -- features that remain obscured with conventional SIM. With minimal hardware modification, low phototoxicity, and open-source reconstruction tools, 4I-SIM establishes a practical and reproducible platform for simultaneous super-resolution and optical sectioning imaging in complex biological environments.

eess.IV↗

arXiv2025

Heterogeneous back-end-of-line integration of thin-film lithium niobate on active silicon photonics for single-chip optical transceivers

The explosive growth of artificial intelligence, cloud computing, and large-scale machine learning is driving an urgent demand for short-reach optical interconnects featuring large bandwidth, low power consumption, high integration density, and low cost preferably adopting complementary metal-oxide-semiconductor (CMOS) processes. Heterogeneous integration of silicon photonics and thin-film lithium niobate (TFLN) combines the advantages of both platforms, and enables co-integration of high-performance modulators, photodetectors, and passive photonic components, offering an ideal route to meet these requirements. However, process incompatibilities have constrained the direct integration of TFLN with only passive silicon photonics. Here, we demonstrate the first heterogeneous back-end-of-line integration of TFLN with a full-functional and active silicon photonics platform via trench-based die-to-wafer bonding. This technology introduces TFLN after completing the full CMOS compatible processes for silicon photonics. Si/SiN passive components including low-loss fiber interfaces, 56-GHz Ge photodetectors, 100-GHz TFLN modulators, and multilayer metallization are integrated on a single silicon chip with efficient inter-layer and inter-material optical coupling. The integrated on-chip optical links exhibit greater than 60 GHz electrical-to-electrical bandwidth and support 128-GBaud OOK and 100-GBaud PAM4 transmission below forward error-correction thresholds, establishing a scalable platform for energy-efficient, high-capacity photonic systems.

physics.optics↗

arXiv2025

The Solar Close Observations and Proximity Experiments (SCOPE) mission

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of the current sheet between coronal mass ejections and their associated solar flares, and energetic particles produced by either reconnection or fast-mode shocks driven by coronal mass ejections. This will help to resolve the nature of reconnections in current sheets, and energetic particle acceleration regions. To investigate coronal heating, the mission will observe nano-flares on scales smaller than 70 km in the solar corona and regions smaller than 40 km in the photosphere, where magnetohydrodynamic waves originate. To study solar wind acceleration mechanisms, the mission will also track the process of ion charge-state freezing in the solar wind. A key achievement will be the observation of the coronal magnetic field at unprecedented proximity to the solar photosphere. The polar regions will also be observed at close range, and the inner edge of the solar system dust disk may be identified for the first time. This work presents the detailed background, science, and mission concept of SCOPE and discusses how we aim to address the questions mentioned above.

astro-ph.SR↗

arXiv2025

{\delta}-EMG: A Monotonic Graph Index for Approximate Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high-dimensional spaces is a foundational component of many modern retrieval and recommendation systems. Currently, almost all algorithms follow an $\epsilon$-Recall-Bounded principle when comparing performance: they require the ANN search results to achieve a recall of more than $1-\epsilon$ and then compare query-per-second (QPS) performance. However, this approach only accounts for the recall of true positive results and does not provide guarantees on the deviation of incorrect results. To address this limitation, we focus on an Error-Bounded ANN method, which ensures that the returned results are a $(1/\delta)$-approximation of the true values. Our approach adopts a graph-based framework. To enable Error-Bounded ANN search, we propose a $\delta$-EMG (Error-bounded Monotonic Graph), which, for the first time, provides a provable approximation for arbitrary queries. By enforcing a $\delta$-monotonic geometric constraint during graph construction, $\delta$-EMG ensures that any greedy search converges to a $(1/\delta)$-approximate neighbor without backtracking. Building on this foundation, we design an error-bounded top-$k$ ANN search algorithm that adaptively controls approximation accuracy during query time. To make the framework practical at scale, we introduce $\delta$-EMQG (Error-bounded Monotonic Quantized Graph), a localized and degree-balanced variant with near-linear construction complexity. We further integrate vector quantization to accelerate distance computation while preserving theoretical guarantees. Extensive experiments on the ANN-Benchmarks dataset demonstrate the effectiveness of our approach. Under a recall requirement of 0.99, our algorithm achieves 19,000 QPS on the SIFT1M dataset, outperforming other methods by more than 40\%.

cs.IR↗

arXiv2025

Constraining Neutron Capture Cross Sections for $^{88}\mathrm{Y}$ with Gamma-ray Strength Function in $(p,p^\prime\gamma)$ Surrogate Reaction

We demonstrate to extract $^{88}\mathrm{Y}(n,\gamma)$ cross sections using the $(p,p'\gamma)$ surrogate reaction with proper treatment of the spin-parity distribution of the compound nucleus $^{89}\mathrm{Y}$. Experimental data of both $\gamma$-decay probability and $\gamma$-ray strength function are used to constrain the nuclear model parameters within a computational framework combining the Bayesian optimization and Markov chain Monte Carlo method, which helps to significantly reduce the $(n,\gamma)$ data uncertainty. The $^{88}\mathrm{Y}(n,\gamma)$ cross sections are then extracted with a narrow uncertainty of 7.6\%-23.1\% within neutron energy range of 0.01 to 3.0 MeV for the first time, where no experimental data are available. Moreover, our method is verified with the $^{88}\mathrm{Sr}(p,\gamma)$ reaction, of which the measured data are available for comparison. This work opens interesting perspectives on the matter of extracting ($n,\gamma$) reaction cross sections on unstable nuclei as surrogate reaction experiments are becoming widely available.

nucl-th↗

arXiv2025

CrossStateECG: Multi-Scale Deep Convolutional Network with Attention for Rest-Exercise ECG Biometrics

Current research in Electrocardiogram (ECG) biometrics mainly emphasizes resting-state conditions, leaving the performance decline in rest-exercise scenarios largely unresolved. This paper introduces CrossStateECG, a robust ECG-based authentication model explicitly tailored for cross-state (rest-exercise) conditions. The proposed model creatively combines multi-scale deep convolutional feature extraction with attention mechanisms to ensure strong identification across different physiological states. Experimental results on the exercise-ECGID dataset validate the effectiveness of CrossStateECG, achieving an identification accuracy of 92.50% in the Rest-to-Exercise scenario (training on resting ECG and testing on post-exercise ECG) and 94.72% in the Exercise-to-Rest scenario (training on post-exercise ECG and testing on resting ECG). Furthermore, CrossStateECG demonstrates exceptional performance across both state combinations, reaching an accuracy of 99.94% in Rest-to-Rest scenarios and 97.85% in Mixed-to-Mixed scenarios. Additional validations on the ECG-ID and MIT-BIH datasets further confirmed the generalization abilities of CrossStateECG, underscoring its potential as a practical solution for post-exercise ECG-based authentication in dynamic real-world settings.

cs.LG↗

arXiv2025

Considerations on the Design of Transceivers for Ambient Internet of Things

The Ambient IoT (A-IoT) will introduce trillions of connections and enable low-cost battery-less devices. The A-IoT nodes can achieve low cost ($\sim\$ 0.1$ like RFID tag), sub-1mW average power consumption, $\leq 10$ kbps data rates, maintenance-free working for decades, cm-scale size, and support applications like supply chain and smart agriculture. The transceiver challenges in A-IoT focus on sub-mW receivers and crystal-less clock generation. The paper proposes an approximate low-IF receiver and carrier-auxiliary IF feedback LO synthesizer architecture for Type-B/C A-IoT devices, which tracks the RF carrier frequency and eliminates external crystals. The proposed receiver and LO generator are implemented using 55nm CMOS technology. After locking the LO calibration loop, the receiver sensitivity is better than -88 dBm. The proposed receiver architecture will promote zero-power devices for ubiquitous IoT connectivity, bridging digital and physical worlds.

eess.SY↗

arXiv2025

The pseudo-analytical density solution to parameterized Fokker-Planck equations via deep learning

Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density functions are only attainable in specific cases. To speed up the solving process of parameterized FPEs with several system parameters, we introduce a deep learning-based method to obtain the pseudo-analytical density (PAD). Unlike previous numerical methodologies that necessitate solving the FPE separately for each set of system parameters, the PAD simultaneously addresses all the FPEs within a predefined continuous range of system parameters during a single training phase. The approach utilizes a Gaussian mixture distribution (GMD) to represent the stationary probability density, the solution to the FPE. By leveraging a deep residual network, each system parameter configuration is mapped to the parameters of the GMD, ensuring that the weights, means, and variances of the Gaussian components adaptively align with the corresponding true density functions. A grid-free algorithm is further developed to effectively train the residual network, resulting in a feasible PAD obeying necessary normalization and boundary conditions. Extensive numerical studies validate the accuracy and efficiency of our method, promising significant acceleration in the response analysis of multi-parameter, multi-dimensional stochastic nonlinear systems.

physics.comp-ph↗

arXiv2025

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models

Post-training pruning, as one of the key techniques for compressing large language models, plays a vital role in lightweight model deployment and model sparsity. However, current mainstream pruning methods dependent on the Hessian matrix face significant limitations in both pruning speed and practical effectiveness due to the computationally intensive nature of second-order derivative calculations. This paper presents SwiftPrune, a novel Hessian-free weight pruning method that achieves hardware-efficient model compression through two key innovations: 1) SwiftPrune eliminates the need for computationally intensive Hessian matrix calculations by introducing a contribution-based weight metric, which evaluates the importance of weights without relying on second-order derivatives. 2) we employ the Exponentially Weighted Moving Average (EWMA) technique to bypass weight sorting, enabling the selection of weights that contribute most to LLM accuracy and further reducing time complexity. Our approach is extended to support structured sparsity pruning, facilitating efficient execution on modern hardware accelerators. We validate the SwiftPrune on three LLMs (namely LLaMA2, LLaMA3, and Pythia), demonstrating that it significantly enhances compression performance. The experimental findings reveal that SwiftPrune completes the pruning process within seconds, achieving an average speedup of 12.29x (up to 56.02x) over existing SOTA approaches.

cs.LG↗

arXiv2024

Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: "what anomaly occurred?", "why did it happen?", and "how severe is this abnormal event?". In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically, each instance of the proposed benchmark involves three sets of human annotations to indicate the "what", "why" and "how" of an anomaly, including 1) anomaly type, start and end times, and event descriptions, 2) natural language explanations for the cause of an anomaly, and 3) free text reflecting the effect of the abnormality. In addition, we also introduce MMEval, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anomalies. Finally, we propose a novel prompt-based method that can serve as a baseline approach for the challenging CUVA. We conduct extensive experiments to show the superiority of our evaluation metric and the prompt-based approach. Our code and dataset are available at https://github.com/fesvhtr/CUVA.

cs.CV↗

arXiv2024

On duality of four dimensional $\mathcal{N}=1$ gauge theory

We show that Seiberg-like duality of $\mathcal{N}=1$ gauge theory coupled with tensor chiral fields and fundamental chiral fields works if the meson spectrum built from the tensor fields takes particular form: a) It should be truncated; b) The $R$ charges of tensor fields $\{R_a\}$ and the truncated mesons $\{R_j\}$ take very special values. The meson spectrum so that the duality works is encoded elegantly in the factorization of the polynomial $y^n-1=\Phi_{+}\Phi_{-}$. Our consideration covers many known $\mathcal{N}=1$ dualities and generates a large class of new examples.

hep-th↗

arXiv2023

Three dimensional quotient singularity and 4d $\mathcal{N}=1$ AdS/CFT correspondence

We systematically study the AdS/CFT correspondence induced by D3 branes probing three dimensional Gorenstein quotient singularity $\mathbb{C}^3/G$. The field theory is given by the McKay quiver, which has a vanishing NSVZ beta function assuming that all the chiral fields have the $U(1)_R$ charge $\frac{2}{3}$. Various physical quantities such as quiver Hilbert series, superconformal index, central charges, etc are computed, which match exactly with those computed using the singularity. We also study the relevant deformation of those theories and find the dual geometry, therefore generate many new interesting AdS/CFT pairs. The quiver gauge theory defined using finite subgroups of $SO(3)$ group has some interesting features, for example, its Seiberg duality behavior is quite interesting.

hep-th↗

arXiv2022

Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification

Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time. Thus, making good use of limited labeled samples in a small dataset to build a high-performance model is the key to medical image classification problem. In this paper, we propose a deeply supervised Layer Selective Attention Network (LSANet), which comprehensively uses label information in feature-level and prediction-level supervision. For feature-level supervision, in order to better fuse the low-level features and high-level features, we propose a novel visual attention module, Layer Selective Attention (LSA), to focus on the feature selection of different layers. LSA introduces a weight allocation scheme which can dynamically adjust the weighting factor of each auxiliary branch during the whole training process to further enhance deeply supervised learning and ensure its generalization. For prediction-level supervision, we adopt the knowledge synergy strategy to promote hierarchical information interactions among all supervision branches via pairwise knowledge matching. Using the public dataset, MedMNIST, which is a large-scale benchmark for biomedical image classification covering diverse medical specialties, we evaluate LSANet on multiple mainstream CNN architectures and various visual attention modules. The experimental results show the substantial improvements of our proposed method over its corresponding counterparts, demonstrating that LSANet can provide a promising solution for label-efficient learning in the field of medical image classification.

cs.CV↗

arXiv2022

Increasing rate of weighted product of partial quotients in continued fractions

Let $[a_1(x),a_2(x),\cdots,a_n(x),\cdots]$ be the continued fraction expansion of $x\in[0,1)$. In this paper, we study the increasing rate of the weighted product $a^{t_0}_n(x)a^{t_1}_{n+1}(x)\cdots a^{t_m}_{n+m}(x)$ ,where $t_i\in \mathbb{R}_+\ (0\leq i \leq m)$ are weights. More precisely, let $\varphi:\mathbb{N}\to\mathbb{R}_+$ be a function with $\varphi(n)/n\to \infty$ as $n\to \infty$. For any $(t_0,\cdots,t_m)\in \mathbb{R}^{m+1}_+$ with $t_i\geq 0$ and at least one $t_i\neq0 \ (0\leq i\leq m)$, the Hausdorff dimension of the set $$\underline{E}(\{t_i\}_{i=0}^m,\varphi)=\left\{x\in[0,1):\liminf\limits_{n\to \infty}\dfrac{\log \left(a^{t_0}_n(x)a^{t_1}_{n+1}(x)\cdots a^{t_m}_{n+m}(x)\right)}{\varphi(n)}=1\right\}$$ is obtained. Under the condition that $(t_0,\cdots,t_m)\in \mathbb{R}^{m+1}_+$ with $0<t_0\leq t_1\leq \cdots \leq t_m$, we also obtain the Hausdorff dimension of the set \begin{equation*} \overline{E}(\{t_i\}_{i=0}^m,\varphi)=\left\{x\in[0,1):\limsup\limits_{n\to \infty}\dfrac{\log \left(a^{t_0}_n(x)a^{t_1}_{n+1}(x)\cdots a^{t_m}_{n+m}(x)\right)}{\varphi(n)}=1\right\}.\end{equation*}

math.NT↗