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Peng Shi

Publications and source records attributed to Peng Shi.

At least 73 records · Page 4Linked to original sources
arXiv2024

Demonstrating Invalidity of Solution Methods Involved in Solving Compatibility Equations

In the theory of elasticity, the constraint of compatibility conditions on displacement field is not equivalent to the property of displacement field. The difference may broaden the possibility of solutions to elasticity problems, which means that the solution methods involved in solving the compatibility equations like stress-based solution method and stress function method may be invalid. The study argues the validity of the solution methods involved in solving the compatibility equations. It is concluded that the absence of constraints on local rigid body rotation causes the non-uniqueness of displacement fields for a rectangular beam purely bent and a cantilever loaded at the end.

physics.class-ph↗
arXiv2024

Optical skyrmions and other topological quasiparticles of light

Skyrmions are topologically stable quasiparticles that have been predicted and demonstrated in quantum fields, solid-state physics, and magnetic materials, but only recently observed in electromagnetic fields, triggering fast expanding research across different spectral ranges and applications. Here we review the recent advances in optical skyrmions within a unified framework. Starting from fundamental theories, including classification of skyrmionic states, we describe generation and topological control of different kinds of optical skyrmions in structured and time-dependent optical fields. We further highlight generalized classes of optical topological quasiparticles beyond skyrmions and outline the emerging applications, future trends, and open challenges. A complex vectorial field structure of optical quasiparticles with versatile topological characteristics emerges as an important feature in modern spin-optics, imaging and metrology, optical forces, structured light and topological and quantum technologies.

physics.optics↗
arXiv2023

Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning

Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized sequence-to-sequence format opens up the opportunity to make the maximum utilization of large language model knowledge toward structured prediction. Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format. This significantly bounds its usefulness in data-limited settings where finetuning large models cannot properly generalize to the target format. To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples, during the fine-tuning process. By leveraging the dynamism of sparsity, our approach mitigates the impact of well-learned samples and prioritizes underperforming instances for improvement in generalization. Across five tasks of sequence labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low resource settings offering upto 40% performance improvements over full fine-tuning depending on target evaluation settings. Also, compared to in-context learning and other parameter-efficient fine-tuning approaches, FISH-DIP performs comparably or better, notably in extreme low-resource settings.

cs.CL↗
arXiv2023

Experimental investigations of quasi-coherent micro-instabilities in Ohmic plasmas

The ITG and TEM instabilities with quasi-coherent spectra have been identified experimentally, by the newly developed far-forward collective scattering measurements in J-TEXT tokamak Ohmical plasmas. The ITG mode has characteristic frequencies in the range of 30-100kHz and wavenumber of k_θρ_s<0.3. After the plasma density exceeds at critical value, the ITG mode shows a bifurcation behavior, featured by frequency decrease and amplitude enhancement. Meanwhile, the ion energy loss enhancement and confinement degradation are also observed. It gives the direct experimental evidence for ion thermal transport caused by ITG instability.

physics.plasm-ph↗
arXiv2023

Video-Text Retrieval by Supervised Sparse Multi-Grained Learning

While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse space shared between the video and the text for video-text retrieval. The shared sparse space is initialized with a finite number of sparse concepts, each of which refers to a number of words. With the text data at hand, we learn and update the shared sparse space in a supervised manner using the proposed similarity and alignment losses. Moreover, to enable multi-grained alignment, we incorporate frame representations for better modeling the video modality and calculating fine-grained and coarse-grained similarities. Benefiting from the learned shared sparse space and multi-grained similarities, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of S3MA over existing methods. Our code is available at https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.

cs.CV↗
arXiv2023

OpenAgents: An Open Platform for Language Agents in the Wild

Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs). Current language agent frameworks aim to facilitate the construction of proof-of-concept language agents while neglecting the non-expert user access to agents and paying little attention to application-level designs. We present OpenAgents, an open platform for using and hosting language agents in the wild of everyday life. OpenAgents includes three agents: (1) Data Agent for data analysis with Python/SQL and data tools; (2) Plugins Agent with 200+ daily API tools; (3) Web Agent for autonomous web browsing. OpenAgents enables general users to interact with agent functionalities through a web user interface optimized for swift responses and common failures while offering developers and researchers a seamless deployment experience on local setups, providing a foundation for crafting innovative language agents and facilitating real-world evaluations. We elucidate the challenges and opportunities, aspiring to set a foundation for future research and development of real-world language agents.

cs.CL↗
arXiv2023

Enhanced Pricing and Management of Bundled Insurance Risks with Dependence-aware Prediction using Pair Copula Construction

We propose a dependence-aware predictive modeling framework for multivariate risks stemmed from an insurance contract with bundling features - an important type of policy increasingly offered by major insurance companies. The bundling feature naturally leads to longitudinal measurements of multiple insurance risks, and correct pricing and management of such risks is of fundamental interest to financial stability of the macroeconomy. We build a novel predictive model that fully captures the dependence among the multivariate repeated risk measurements. Specifically, the longitudinal measurement of each individual risk is first modeled using pair copula construction with a D-vine structure, and the multiple D-vines are then integrated by a flexible copula. The proposed model provides a unified modeling framework for multivariate longitudinal data that can accommodate different scales of measurements, including continuous, discrete, and mixed observations, and thus can be potentially useful for various economic studies. A computationally efficient sequential method is proposed for model estimation and inference, and its performance is investigated both theoretically and via simulation studies. In the application, we examine multivariate bundled risks in multi-peril property insurance using proprietary data from a commercial property insurance provider. The proposed model is found to provide improved decision making for several key insurance operations. For underwriting, we show that the experience rate priced by the proposed model leads to a 9% lift in the insurer's net revenue. For reinsurance, we show that the insurer underestimates the risk of the retained insurance portfolio by 10% when ignoring the dependence among bundled insurance risks.

stat.ME↗
arXiv2023

The hidden spin-momentum locking and topological defects in unpolarized light fields

Electromagnetic waves characterized by intensity, phase, and polarization degrees of freedom are widely applied in data storage, encryption, and communications. However, these properties can be substantially affected by phase disorders and disturbances, whereas high-dimensional degrees of freedom including momentum and angular momentum of electromagnetic waves can offer new insights into their features and phenomena, for example topological characteristics and structures that are robust to these disturbances. Here, we discover and demonstrate theoretically and experimentally spin-momentum locking and topological defects in unpolarized light. The coherent spin is locked to the kinetic momentum except for a small coupling spin term, due to the simultaneous presence of transverse magnetic and electric components in unpolarized light. To cancel the coupling term, we employ a metal film acting as a polarizer to form some skyrmion-like spin textures at the metal/air interface. Using an in-house scanning optical microscopic system to image the out-of-plane spin density of the focused unpolarized vortex light, we obtained experimental results that coincide well with our theoretical predictions. The theory and technique promote the applications of topological defects in optical data storage, encryption, and decryption, and communications.

physics.optics↗
arXiv2023

Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the equivalence between the KG and the text in the dataset. Using cyclic evaluation we find that manually created WebNLG is much better than automatically created TeKGen and T-REx. Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation. We also construct two synthetic datasets using large language models (LLMs), and observe that these are conducive to models that perform significantly well on cyclic generation of text, but less so on cyclic generation of KGs, probably because of a lack of a consistent underlying ontology.

cs.CL↗
arXiv2023

Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak

This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and the high-field-side high-density (HFSHD) front. These phenomena were extensively investigated by using Langmuir probe and Polarimeter-interferometer diagnostics.

physics.plasm-ph↗
arXiv2023

Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models

In this paper, we study the problem of generating obstinate (over-stability) adversarial examples by word substitution in NLP, where input text is meaningfully changed but the model's prediction does not, even though it should. Previous word substitution approaches have predominantly focused on manually designed antonym-based strategies for generating obstinate adversarial examples, which hinders its application as these strategies can only find a subset of obstinate adversarial examples and require human efforts. To address this issue, in this paper, we introduce a novel word substitution method named GradObstinate, a gradient-based approach that automatically generates obstinate adversarial examples without any constraints on the search space or the need for manual design principles. To empirically evaluate the efficacy of GradObstinate, we conduct comprehensive experiments on five representative models (Electra, ALBERT, Roberta, DistillBERT, and CLIP) finetuned on four NLP benchmarks (SST-2, MRPC, SNLI, and SQuAD) and a language-grounding benchmark (MSCOCO). Extensive experiments show that our proposed GradObstinate generates more powerful obstinate adversarial examples, exhibiting a higher attack success rate compared to antonym-based methods. Furthermore, to show the transferability of obstinate word substitutions found by GradObstinate, we replace the words in four representative NLP benchmarks with their obstinate substitutions. Notably, obstinate substitutions exhibit a high success rate when transferred to other models in black-box settings, including even GPT-3 and ChatGPT. Examples of obstinate adversarial examples found by GradObstinate are available at https://huggingface.co/spaces/anonauthors/SecretLanguage.

cs.CL↗
arXiv2023

A novel integrated method of detection-grasping for specific object based on the box coordinate matching

To better care for the elderly and disabled, it is essential for service robots to have an effective fusion method of object detection and grasp estimation. However, limited research has been observed on the combination of object detection and grasp estimation. To overcome this technical difficulty, a novel integrated method of detection-grasping for specific object based on the box coordinate matching is proposed in this paper. Firstly, the SOLOv2 instance segmentation model is improved by adding channel attention module (CAM) and spatial attention module (SAM). Then, the atrous spatial pyramid pooling (ASPP) and CAM are added to the generative residual convolutional neural network (GR-CNN) model to optimize grasp estimation. Furthermore, a detection-grasping integrated algorithm based on box coordinate matching (DG-BCM) is proposed to obtain the fusion model of object detection and grasp estimation. For verification, experiments on object detection and grasp estimation are conducted separately to verify the superiority of improved models. Additionally, grasping tasks for several specific objects are implemented on a simulation platform, demonstrating the feasibility and effectiveness of DG-BCM algorithm proposed in this paper.

cs.RO↗
arXiv2023

Topological state transitions in electromagnetic topological defects

The recent emergence of electromagnetic topological defects has attracted wide interest in fields from topological photonics to deep-subwavelength light-mater interactions. Previously, much of the research has focused on constructing specific topological defects but the fundamental theory describing the physical mechanisms underlying their formation and transitions is lacking. Here, we present a spin-orbit coupling based theory describing such mechanisms for various configurations of spin topological defects in confined electromagnetic fields. The results reveal that their formation originates from the conservation of total angular momentum and that their transitions are determined by anisotropic spin-orbit couplings. By engineering the spin-orbit couplings, we observe the formation and transitions of Neel-type, twisted-type, and Bloch-type spin topological defects in confined electromagnetic fields. A stable Block-type spin topological defect is reported for the first time. Our theory can also describe the transitions of field topological defects. The findings enrich the portfolio of electromagnetic topological defects, deepen our understanding of conserved laws, spin-orbit couplings and transitions of topological defects in confined electromagnetic systems, and predict applications in high-density optical data transmissions and chiral quantum optics.

physics.optics↗
arXiv2023

Binding Language Models in Symbolic Languages

Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e.g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations. Specifically, we employ GPT-3 Codex as the LM. In the parsing stage, with only a few in-context exemplars, Codex is able to identify the part of the task input that cannot be answerable by the original programming language, correctly generate API calls to prompt Codex to solve the unanswerable part, and identify where to place the API calls while being compatible with the original grammar. In the execution stage, Codex can perform versatile functionalities (e.g., commonsense QA, information extraction) given proper prompts in the API calls. Binder achieves state-of-the-art results on WikiTableQuestions and TabFact datasets, with explicit output programs that benefit human debugging. Note that previous best systems are all finetuned on tens of thousands of task-specific samples, while Binder only uses dozens of annotations as in-context exemplars without any training. Our code is available at https://github.com/HKUNLP/Binder .

cs.CL↗
arXiv2023

Non-fragile Finite-time Stabilization for Discrete Mean-field Stochastic Systems

In this paper, the problem of non-fragile finite-time stabilization for linear discrete mean-field stochastic systems is studied. The uncertain characteristics in control parameters are assumed to be random satisfying the Bernoulli distribution. A new approach called the ``state transition matrix method" is introduced and some necessary and sufficient conditions are derived to solve the underlying stabilization problem. The Lyapunov theorem based on the state transition matrix also makes a contribution to the discrete finite-time control theory. One practical example is provided to validate the effectiveness of the newly proposed control strategy.

math.OC↗
arXiv2023

General Solution to 2D Steady Navier-Stokes Equation for Incompressible Flow without vorticity diffusion

The study solves the general solution to 2D steady Navier-Stokes equation for incompressible flow without vorticity diffusion, which is more general than Stokes flow. In order to obtain the general solution, two potential functions are introduced to express the velocity: a vector potential describing the rotational incompressible flow and a scalar potential describing the irrotational incompressible flow. The results show that the vorticity equation expressed with potential functions is a biharmonic function, which means that the potential functions describing the flow field are polynomials of no more than fourth degree. For a steady unidirectional shear flow, the velocity and pressure fields can be described with the vector potential expressed by a polynomial of third degree. For non unidirectional two-dimensional steady shear flow, there may be four independent parameters in the two potential functions.

physics.flu-dyn↗
arXiv2022

Spin/momentum properties of the paraxial optical beams

Spin angular momentum, an elementary dynamical property of classical electromagnetic fields, plays an important role in spin-orbit and light-matter interactions, especially in near-field optics. The research on optical spins has led to the discovery of phenomena such as optical spin-momentum locking and photonic topological quasiparticles, as well as applications in high-precision detection and nanometrology. Here, we investigate spin-momentum relations in paraxial optical systems and show that the optical spin angular momentum contains transverse and longitudinal spin components simultaneously. The transverse spin originates from inhomogeneities of field and governed by the vorticity of the kinetic momentum density, whereas the longitudinal spin parallel to the local canonical momentum is proportional to the polarization ellipticity of light. Moreover, the skyrmionlike spin textures arise from the optical transverse spin can be observed in paraxial beams, and their topologies are maintained free from the influence of the Gouy phase during propagation. Interestingly, the optical singularities, including both phase and polarization singularities, can also affect the spin-momentum properties significantly. Our findings describe the intrinsic spin-momentum properties in paraxial optical systems and apply in the analysis of the properties of spin-momentum in optical focusing, imaging, and scattering systems.

physics.optics↗
arXiv2022

Spin decomposition and topological properties in a generic electromagnetic field

Electromagnetic spins, including longitudinal and transverse ones, have been playing important roles in light-matter interactions. Here, we formulate a unified equation to uncover the physical origins and topological properties of longitudinal and transverse spins in a generic electromagnetic field. The equation reveals universally that the transverse spin is locked with the kinetic momentum and originated from the transverse inhomogeneities of field, whereas the helix-dependent longitudinal spin orients parallel to the local wavevector. Remarkably, a hidden extraordinary helix-dependent transverse spin possessing helix-dependent spin-momentum locking is discovered and the number of locking states consistent with the nontrivial topological spin Chern number. Furthermore, this spin which determines the inverted helical components is related to the Berry curvature closely. The findings, which are demonstrated experimentally by measuring the three-dimensional spin components in the focusing configuration, will deepen the understanding the underlying physics of spins and open an avenue for chiral quantum optical applications.

physics.optics↗