arXiv subjects

Shang Wang

Publications and source records attributed to Shang Wang.

At least 19 recordsLinked to original sources

arXiv2026

Attosecond Reconstruction of Strain Tensors via Electronic Fingerprints

We demonstrate an attosecond transient absorption spectroscopy (ATAS) scheme for reconstructing strain tensors in two-dimensional materials. Using strained graphene as a prototype system, we show that the fishbone structures in ATAS serve as distinctive spectral fingerprints of strain, where strain-induced shifts and splittings of van Hove singularities encode the magnitude and orientation of the strain tensor, respectively. By combining density-matrix simulations with analytical modeling, we establish a direct mapping between transient absorption spectra and strain tensors, enabling accurate retrieval of lattice deformation from ultrafast electronic responses. Our work introduces an attosecond spectroscopic paradigm for ultrafast strain metrology, where electronic fingerprints replace conventional structural probes for sensing lattice deformation in quantum materials.

cond-mat.mtrl-sci↗

arXiv2026

RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs

Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisition-time and imaging-platform differences between UAV and reference imagery induce substantial cross-domain appearance and viewpoint shifts, challenging robust six-degree-of-freedom (6-DoF) pose estimation. We address these shifts by sampling UAV-viewpoint reference views from Google 3D Tiles across locations, altitudes, and orientations. A two-stage cross-domain fine-tuning recipe adapts SALAD using pose-near positives and geographically distant hard negatives, while local geometric consistency re-ranks the Top-K candidates. We further propose Retrieval-In-Matching (RIM), which freezes the adapted DINOv2-B retriever and distils a local-descriptor decoder that reuses its token field alongside a shallow VGG19 detail stream. One query-side DINOv2-B forward thus serves both SALAD retrieval and local description, eliminating a second foundation-model backbone while preserving retrieval descriptors by construction. We evaluate RIM zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data. RIM outperforms ten recent retrieval baseline families. At 25/50 m under the full 3D distance metric, it improves Recall@1 over SALAD by 8.55/13.77 percentage points on EPFL and 4.45/8.94 points on Park. At Top-K=5, the complete measured localization query, including retrieval, candidate matching, and robust geometric verification, takes 67.9 ms end-to-end: 1.8 times faster than the strongest separate sparse-matching baseline and over 40 times faster than RoMa, while achieving comparable re-ranking accuracy. These results establish an efficient and deployable pipeline for UAV global visual localization in GNSS-challenged environments.

cs.CV↗

arXiv2026

Tunneling spectra of $\mathrm{TaO}_x$ junctions for van der Waals superconductors

Tunneling spectroscopy and its evolution are crucial for elucidating the intricate electronic structure and emergent phenomena in quantum materials.Nevertheless, high-quality measurements -- specifically those tracking evolution across temperature and external fields -- remain a formidable challenge. We have fabricated a high-quality $\mathrm{TaO}_x$-based planar tunneling junction by using magnetron sputtering for van der Waals (vdW) superconductors. Using the vdW superconductor $\mathrm{Bi}_2\mathrm{Sr}_2\mathrm{CaCu}_2\mathrm{O}_{8+\delta}$ (Bi2212) as a benchmark, this platform yields high-quality tunneling spectra, reproducing the electronic signatures obtained from scanning tunneling spectra acquired from atomically clean surfaces under ultra-high vacuum conditions. This architecture enables high-precision spectroscopy across extensive temperature and magnetic field ranges, offering a universal strategy for probing the electronic structures of diverse two-dimensional systems and facilitating future explorations of material properties.

cond-mat.supr-con↗

arXiv2026

A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models

3D-IC netlist partitioning is commonly optimized using proxy objectives, while final PPA is treated as a costly evaluation rather than an optimization signal. This proxy-driven paradigm makes it difficult to reliably translate additional PPA evaluations into better PPA outcomes. To bridge this gap, we present DOPP (D-Optimal PPA-driven partitioning selection), an approach that bridges the gap between proxies and true PPA metrics. Across eight 3D-IC designs, our framework improves PPA over Open3DBench (average relative improvements of 9.99% congestion, 7.87% routed wirelength, 7.75% WNS, 21.85% TNS, and 1.18% power). Compared with exhaustive evaluation over the full candidate set, DOPP achieves comparable best-found PPA while evaluating only a small fraction of candidates, substantially reducing evaluation cost. By parallelizing evaluations, our method delivers these gains while maintaining wall-clock runtime comparable to traditional baselines.

cs.LG↗

arXiv2026

Planetary climate interactions of the Qinghai-Tibetan Plateau

The Qinghai-Tibetan Plateau (QTP), Earth's "Third Pole", profoundly shapes the Asian monsoon and regional climate and exerts far-reaching influence on the global climate system. Yet its role in organizing planetary-scale climate interactions remains poorly quantified. Here we develop a climate network framework to explicitly resolve the planetary teleconnection architecture associated with the QTP across historical observations and future climate projections, with physical consistency assessed using Lagrangian trajectory diagnostics and targeted numerical experiments. We uncover a persistent and directional interaction structure linking the QTP with multiple major climate tipping elements. In particular, we identify a robust tripolar interaction mode coupling the QTP with both the Arctic and Antarctica through coherent atmospheric-oceanic pathways. Our findings establish the QTP as a critical planetary climate integrator, revealing a significant blind spot in current climate models and risk frameworks regarding cascading tipping dynamics in a warming world.

physics.ao-ph↗

arXiv2026

Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents

Large language model (LLM)-based agents have recently gained considerable attention due to the powerful reasoning capabilities of LLMs. Existing research predominantly focuses on enhancing the task performance of these agents in diverse scenarios. However, as LLM-based agents become increasingly integrated into real-world applications, significant concerns emerge regarding their accumulation of sensitive or outdated knowledge. Addressing these concerns requires the development of mechanisms that allow agents to selectively forget previously learned knowledge, giving rise to a new term LLM-based agent unlearning. This paper initiates research on unlearning in LLM-based agents. Specifically, we propose a novel and comprehensive framework that categorizes unlearning scenarios into three contexts: state unlearning (forgetting specific states or items), trajectory unlearning (forgetting sequences of actions) and environment unlearning (forgetting entire environments or categories of tasks). Within this framework, we introduce a natural language-based unlearning method that trains a conversion model to transform high-level unlearning requests into actionable unlearning prompts, guiding agents through a controlled forgetting process. Moreover, to evaluate the robustness of the proposed framework, we introduce an unlearning inference adversary capable of crafting prompts, querying agents, and observing their behaviors in an attempt to infer the forgotten knowledge. Experimental results show that our approach effectively enables agents to forget targeted knowledge while preserving performance on untargeted tasks, and prevents the adversary from inferring the forgotten knowledge.

cs.MA↗

arXiv2026

Turning Black Box into White Box: Dataset Distillation Leaks

Dataset distillation compresses a large real dataset into a small synthetic one, enabling models trained on the synthetic data to achieve performance comparable to those trained on the real data. Although synthetic datasets are assumed to be privacy-preserving, we show that existing distillation methods can cause severe privacy leakage because synthetic datasets implicitly encode the weight trajectories of the distilled model, they become over-informative and exploitable by adversaries. To expose this risk, we introduce the Information Revelation Attack (IRA) against state-of-the-art distillation techniques. Experiments show that IRA accurately predicts both the distillation algorithm and model architecture, and can successfully infer membership and recover sensitive samples from the real dataset.

cs.CR↗

arXiv2026

Identifying Mechanism of Energy-Resolved Attoclock

We study above-threshold ionization (ATI) of atoms in strong elliptical laser fields numerically and analytically. Recent benchmark experiments for H showed that the attoclock offset angle related to each ATI ring increases remarkably with energy and this characteristic phenomenon can be attributed to the laser-induced nonadiabatic initial velocity and position of the electron at the tunnel exit [PRL127, 273201 (2021)]. However, the specific mechanism of how the nonadiabatic effects influence this angle remains unclear. Here, by using a strong-field model that analytically and quantitatively decouples complex nonadiabatic effects and Coulomb effects, the detailed mechanism can be clearly identified. We show that due to nonadiabatic effects, the angles associated with lower (higher) energy rings are dominated by the main (minor) axis of the laser ellipse, jumping from $0^o$ to $90^o$. These field-related rigid effects are softened by Coulomb-induced exit velocity closely related to system symmetry, resulting in a significant but smooth increase in angle with energy.

physics.atom-ph↗

arXiv2025

A Coulomb-included model for high-order harmonic generation from atoms

In strong laser-atom interactions, the Coulomb potential can affect the trajectories of rescattering electron in high-order harmonic generation (HHG). Here, by constructing a semi-analytical Coulomb-included model and comparing it with numerical experiments that allow for direct observation of electron trajectories, we identify the role of Coulomb potential in different processes of HHG. We show that the symmetry of the system determined by Coulomb potential plays an important role in the ionization process of HHG, inducing the tunneling-out time of electrons to shift towards earlier times. This symmetry-related effect reflects the quantum properties of atomic systems, in sharp contrast to the classical Coulomb-induced acceleration in the recombination process. In particular, compared with other strong-filed models, the scaling law of the amplitude of HHG electron trajectories predicted by this model agrees with the numerical experiments, indicating that the model developed here can be used to quantitatively describe HHG. This model can also be used to study strong-field ionization significantly influenced by rescattering.

physics.atom-ph↗

arXiv2025

Elastic Architecture Search for Efficient Language Models

As large pre-trained language models become increasingly critical to natural language understanding (NLU) tasks, their substantial computational and memory requirements have raised significant economic and environmental concerns. Addressing these challenges, this paper introduces the Elastic Language Model (ELM), a novel neural architecture search (NAS) method optimized for compact language models. ELM extends existing NAS approaches by introducing a flexible search space with efficient transformer blocks and dynamic modules for dimension and head number adjustment. These innovations enhance the efficiency and flexibility of the search process, which facilitates more thorough and effective exploration of model architectures. We also introduce novel knowledge distillation losses that preserve the unique characteristics of each block, in order to improve the discrimination between architectural choices during the search process. Experiments on masked language modeling and causal language modeling tasks demonstrate that models discovered by ELM significantly outperform existing methods.

cs.CL↗

arXiv2025

Hyperbolic Structured Classification for Robust Single Positive Multi-label Learning

Single Positive Multi-Label Learning (SPMLL) addresses the challenging scenario where each training sample is annotated with only one positive label despite potentially belonging to multiple categories, making it difficult to capture complex label relationships and hierarchical structures. While existing methods implicitly model label relationships through distance-based similarity, lacking explicit geometric definitions for different relationship types. To address these limitations, we propose the first hyperbolic classification framework for SPMLL that represents each label as a hyperbolic ball rather than a point or vector, enabling rich inter-label relationship modeling through geometric ball interactions. Our ball-based approach naturally captures multiple relationship types simultaneously: inclusion for hierarchical structures, overlap for co-occurrence patterns, and separation for semantic independence. Further, we introduce two key component innovations: a temperature-adaptive hyperbolic ball classifier and a physics-inspired double-well regularization that guides balls toward meaningful configurations. To validate our approach, extensive experiments on four benchmark datasets (MS-COCO, PASCAL VOC, NUS-WIDE, CUB-200-2011) demonstrate competitive performance with superior interpretability compared to existing methods. Furthermore, statistical analysis reveals strong correlation between learned embeddings and real-world co-occurrence patterns, establishing hyperbolic geometry as a more robust paradigm for structured classification under incomplete supervision.

cs.CV↗

arXiv2025

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs

Low-Rank Adaptation (LoRA) has become the leading Parameter-Efficient Fine-Tuning (PEFT) method for Large Language Models (LLMs), as it significantly reduces GPU memory usage while maintaining competitive fine-tuned model quality on downstream tasks. Despite these benefits, we identify two key inefficiencies in existing LoRA fine-tuning systems. First, they incur substantial runtime overhead due to redundant memory accesses on large activation tensors. Second, they miss the opportunity to concurrently fine-tune multiple independent LoRA adapters that share the same base model on the same set of GPUs. This leads to missed performance gains such as reduced pipeline bubbles, better communication overlap, and improved GPU load balance. To address these issues, we introduce LoRAFusion, an efficient LoRA fine-tuning system for LLMs. At the kernel level, we propose a graph-splitting method that fuses memory-bound operations. This design eliminates unnecessary memory accesses and preserves the performance of compute-bound GEMMs without incurring the cost of recomputation or synchronization. At the scheduling level, LoRAFusion introduces an adaptive batching algorithm for multi-job fine-tuning. It first splits LoRA adapters into groups to intentionally stagger batch execution across jobs, and then solves a bin-packing problem within each group to generate balanced, dependency-aware microbatches. LoRAFusion achieves up to $1.96\times$ ($1.47\times$ on average) end-to-end speedup compared to Megatron-LM, and up to $1.46\times$ ($1.29\times$ on average) improvement over mLoRA, the state-of-the-art multi-LoRA fine-tuning system. Our fused kernel achieves up to $1.39\times$ ($1.27\times$ on average) kernel performance improvement and can directly serve as a plug-and-play replacement in existing LoRA systems. We open-source LoRAFusion at https://github.com/CentML/lorafusion.

cs.LG↗

arXiv2025

Partial Eigenvalue Assignment for Nonlinear Systems

In this paper, we study control design methods for assigning a subset of nonlinear right or left eigenvalues to a specified set of scalar-valued functions via nonlinear Sylvester equations. This framework can be viewed as a generalization of partial linear eigenvalue assignment (also referred to as partial pole placement) for linear systems. First, we propose a method for partial nonlinear right eigenvalue assignment via state feedback using a nonlinear Sylvester equation and a condition for preserving an open-loop nonlinear right eigenvalue. This method can be applied to partial stabilization of nonlinear systems. Then, as the dual problem, we present a method for partial nonlinear left eigenvalue assignment via the dual nonlinear Sylvester equation and a condition for preserving an open-loop nonlinear left eigenvalue, which can be applied to partial observer design for nonlinear system.

math.OC↗

arXiv2025

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning

Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image, the model must first classify the main visual events (verb classification), then identify the participating entities and their semantic roles (semantic role labeling), and finally localize these entities in the image (semantic role localization). Existing methods treat verb classification as a single-label problem, but we show through a comprehensive analysis that this formulation fails to address the inherent ambiguity in visual event recognition, as multiple verb categories may reasonably describe the same image. This paper makes three key contributions: First, we reveal through empirical analysis that verb classification is inherently a multi-label problem due to the ubiquitous semantic overlap between verb categories. Second, given the impracticality of fully annotating large-scale datasets with multiple labels, we propose to reformulate verb classification as a single positive multi-label learning (SPMLL) problem - a novel perspective in SR research. Third, we design a comprehensive multi-label evaluation benchmark for SR that is carefully designed to fairly evaluate model performance in a multi-label setting. To address the challenges of SPMLL, we futher develop the Graph Enhanced Verb Multilayer Perceptron (GE-VerbMLP), which combines graph neural networks to capture label correlations and adversarial training to optimize decision boundaries. Extensive experiments on real-world datasets show that our approach achieves more than 3\% MAP improvement while remaining competitive on traditional top-1 and top-5 accuracy metrics.

cs.CV↗

arXiv2025

Isolate Trigger: Detecting and Eliminating Adaptive Backdoor Attacks

Deep learning models are widely deployed in various applications but remain vulnerable to stealthy adversarial threats, particularly backdoor attacks. Backdoor models trained on poisoned datasets behave normally with clean inputs but cause mispredictions when a specific trigger is present. Most existing backdoor defenses assume that adversaries only inject one backdoor with small and conspicuous triggers. However, adaptive backdoor that entangle multiple trigger patterns with benign features can effectively bypass existing defenses. To defend against these attacks, we propose Isolate Trigger (IsTr), an accurate and efficient framework for backdoor detection and mitigation. IsTr aims to eliminate the influence of benign features and reverse hidden triggers. IsTr is motivated by the observation that a model's feature extractor focuses more on benign features while its classifier focuses more on trigger patterns. Based on this difference, IsTr designs Steps and Differential-Middle-Slice to resolve the detecting challenge of isolating triggers from benign features. Moreover, IsTr employs unlearning-based repair to remove both attacker-injected and natural backdoors while maintaining model benign accuracy. We extensively evaluate IsTr against six representative backdoor attacks and compare with seven state-of-the-art baseline methods across three real-world applications: digit recognition, face recognition, and traffic sign recognition. In most cases, IsTr reduces detection overhead by an order of magnitude while achieving over 95\% detection accuracy and maintaining the post-repair attack success rate below 3\%, outperforming baseline defenses. IsTr remains robust against various adaptive attacks, even when trigger patterns are heavily entangled with benign features.

cs.CR↗

arXiv2025

A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture

This paper proposes a neural architecture search space using ResNet as a framework, with search objectives including parameters for convolution, pooling, fully connected layers, and connectivity of the residual network. In addition to recognition accuracy, this paper uses the loss value on the validation set as a secondary objective for optimization. The experimental results demonstrate that the search space of this paper together with the optimisation approach can find competitive network architectures on the MNIST, Fashion-MNIST and CIFAR100 datasets.

cs.NE↗

arXiv2025

A Transformer-based Neural Architecture Search Method

This paper presents a neural architecture search method based on Transformer architecture, searching cross multihead attention computation ways for different number of encoder and decoder combinations. In order to search for neural network structures with better translation results, we considered perplexity as an auxiliary evaluation metric for the algorithm in addition to BLEU scores and iteratively improved each individual neural network within the population by a multi-objective genetic algorithm. Experimental results show that the neural network structures searched by the algorithm outperform all the baseline models, and that the introduction of the auxiliary evaluation metric can find better models than considering only the BLEU score as an evaluation metric.

cs.CL↗

arXiv2025

Isolated elliptically-polarized attosecond pulse generation in gapped graphene driven by linearly polarized laser fields

We theoretically investigate high-order harmonic generation (HHG) and its ellipticity in gapped graphene, driven by a femtosecond short-pulse laser at various orientation angles, employing the two-band density-matrix equations within the tight-binding approximation. The orientation-dependent harmonic spectra exhibit pronounced enhancement of specific harmonics, which we attribute to the caustic effect. Using the recombination trajectory model, we reveal that the orientation dependence of these enhanced harmonics originates from the distinct band structures encountered by electrons ionized from the two inequivalent $\textrm{K}$ points. Moreover, we focus on the ellipticity of the enhanced harmonics at specific angles and demonstrate that it primarily depends on the phase difference between the parallel and perpendicular components, which can be accurately predicted by our recombination trajectory model. Based on these insights, we propose a two-color (fundamental plus second-harmonic) field scheme to generate isolated elliptically polarized attosecond pulses (IEAPs) in gapped graphene. Our findings may provide a promising pathway toward the generation of IEAPs in gapped graphene or transition metal dichalcogenides.

physics.atom-ph↗