arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 361 records · Page 20Linked to original sources

arXiv2024

Theoretical investigation on the optical absorption spectra in cyclo[n]carbons (n=10, 14, 18)

The optical absorption spectra of cyclo[n]carbons (n=10, 14, 18) are investigated in the framework of time-dependent density functional theory. The collective plasmon excitations well develop as the increases of the ring size and the symmetry group of cyclo[n]carbons. An increase in intensity for the main peaks with the growing number of atoms in cyclo[n]carbons is observed. With the increase of the radius of the monocyclic ring, as more electrons participate in the dipole oscillation the main excitation peaks are red-shifted to the lower energy. The highly symmetrical structures of cyclo[n]carbons (D_{nh}) possess degenerate levels, leading to simpler spectra with fewer peaks. The Fourier transform of the induced electron density of the cyclo[n]carbons (n=10, 14, 18) is investigated at the excitation frequencies.

physics.atm-clus↗

arXiv2024

Policy Bifurcation in Safe Reinforcement Learning

Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, where policies map states to actions in a smooth, uninterrupted manner; however, our research finds that in some scenarios, the feasible policy should be discontinuous or multi-valued, interpolating between discontinuous local optima can inevitably lead to constraint violations. We are the first to identify the generating mechanism of such a phenomenon, and employ topological analysis to rigorously prove the existence of policy bifurcation in safe RL, which corresponds to the contractibility of the reachable tuple. Our theorem reveals that in scenarios where the obstacle-free state space is non-simply connected, a feasible policy is required to be bifurcated, meaning its output action needs to change abruptly in response to the varying state. To train such a bifurcated policy, we propose a safe RL algorithm called multimodal policy optimization (MUPO), which utilizes a Gaussian mixture distribution as the policy output. The bifurcated behavior can be achieved by selecting the Gaussian component with the highest mixing coefficient. Besides, MUPO also integrates spectral normalization and forward KL divergence to enhance the policy's capability of exploring different modes. Experiments with vehicle control tasks show that our algorithm successfully learns the bifurcated policy and ensures satisfying safety, while a continuous policy suffers from inevitable constraint violations.

cs.LG↗

arXiv2024

Measurement of Out-of-Plane first-order Displacement Derivatives in Orthogonal shear directions Using Dichroic Mirrors

This paper proposed a novel and temporal phase-shift digital shearography system for simultaneous measurement of first order displacement derivative in orthogonal shear directions. Dual lasers with wavelengths of 532nm and 637nm, three splitter prism structure, two dichroic mirrors with different response wavelength, and the color CMOS are used in the system. Two dichroic mirrors can be used as shear mirrors to realize shear in orthogonal directions at the same time. The system realizes the measurement of the first-order displacement derivative information in the orthogonal direction of the round metal aluminum plate of diameter 250mm. The experimental results show that the overall displacement integral PV error in the x and y directions is 2.7%-14.8%, which verified the reliability of the system.

physics.optics↗

arXiv2024

Cavity magnomechanics: from classical to quantum

Hybrid quantum systems based on magnons in magnetic materials have made significant progress in the past decade. They are built based on the couplings of magnons with microwave photons, optical photons, vibration phonons, and superconducting qubits. In particular, the interactions among magnons, microwave cavity photons, and vibration phonons form the system of cavity magnomechanics (CMM), which lies in the interdisciplinary field of cavity QED, magnonics, quantum optics, and quantum information. Here, we review the experimental and theoretical progress of this emerging field. We first introduce the underlying theories of the magnomechanical coupling, and then some representative classical phenomena that have been experimentally observed, including magnomechanically induced transparency, magnomechanical dynamical backaction, magnon-phonon cross-Kerr nonlinearity, etc. We also discuss a number of theoretical proposals, which show the potential of the CMM system for preparing different kinds of quantum states of magnons, phonons, and photons, and hybrid systems combining magnomechanics and optomechanics and relevant quantum protocols based on them. Finally, we summarize this review and provide an outlook for the future research directions in this field.

quant-ph↗

arXiv2024

TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research

In this paper, we introduce a new dataset in the medical field of Traumatic Brain Injury (TBI), called TBI-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of TBI. This dataset, built upon the foundation of standard text and image data, incorporates specific annotations within the EMRs, extracting key content from the text information, and categorizes the annotation content of imaging data into five types: brain midline, hematoma, left cerebral ventricle, right cerebral ventricle and fracture. TBI-IT aims to be a foundational dataset for feature learning in image segmentation tasks and named entity recognition.

eess.IV↗

arXiv2024

EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model

Early weakly supervised video grounding (WSVG) methods often struggle with incomplete boundary detection due to the absence of temporal boundary annotations. To bridge the gap between video-level and boundary-level annotation, explicit-supervision methods, i.e., generating pseudo-temporal boundaries for training, have achieved great success. However, data augmentations in these methods might disrupt critical temporal information, yielding poor pseudo boundaries. In this paper, we propose a new perspective that maintains the integrity of the original temporal content while introducing more valuable information for expanding the incomplete boundaries. To this end, we propose EtC (Expand then Clarify), first use the additional information to expand the initial incomplete pseudo boundaries, and subsequently refine these expanded ones to achieve precise boundaries. Motivated by video continuity, i.e., visual similarity across adjacent frames, we use powerful multimodal large language models (MLLMs) to annotate each frame within initial pseudo boundaries, yielding more comprehensive descriptions for expanded boundaries. To further clarify the noise of expanded boundaries, we combine mutual learning with a tailored proposal-level contrastive objective to use a learnable approach to harmonize a balance between incomplete yet clean (initial) and comprehensive yet noisy (expanded) boundaries for more precise ones. Experiments demonstrate the superiority of our method on two challenging WSVG datasets.

cs.CV↗

arXiv2024

Microwave-to-optics conversion using magnetostatic modes and a tunable optical cavity

Quantum computing, quantum communication and quantum networks rely on hybrid quantum systems operating in different frequency ranges. For instance, the superconducting qubits work in the gigahertz range, while the optical photons used in communication are in the range of hundreds of terahertz. Due to the large frequency mismatch, achieving the direct coupling and information exchange between different information carriers is generally difficult. Accordingly, a quantum interface is demanded, which serves as a bridge to establish information linkage between different quantum systems operating at distinct frequencies. Recently, the magnon mode in ferromagnetic spin systems has received significant attention. While the inherent weak optomagnonic coupling strength restricts the microwave-to-optical photon conversion efficiency using magnons, the versatility of the magnon modes, together with their readily achievable strong coupling with other quantum systems, endow them with many distinct advantages. Here, we realize the magnon-based microwave-light interface by adopting an optical cavity with adjustable free spectrum range and different kinds of magnetostatic modes in two microwave cavity configurations. By optimizing the parameters, an internal conversion efficiency of $1.28 \times 10^{-7}$ is achieved. We analyze the impact of various parameters on the microwave-to-optics conversion. The study provides useful guidance and insights to further enhancing the microwave-to-optics conversion efficiency using magnons.

quant-ph↗

arXiv2024

LocalGCL: Local-aware Contrastive Learning for Graphs

Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-consuming and costly process of annotating graph labels manually prompts the growth of self-supervised learning (SSL) techniques. As a dominant approach of SSL, Contrastive learning (CL) learns discriminative representations by differentiating between positive and negative samples. However, when applied to graph data, it overemphasizes global patterns while neglecting local structures. To tackle the above issue, we propose \underline{Local}-aware \underline{G}raph \underline{C}ontrastive \underline{L}earning (\textbf{\methnametrim}), a self-supervised learning framework that supplementarily captures local graph information with masking-based modeling compared with vanilla contrastive learning. Extensive experiments validate the superiority of \methname against state-of-the-art methods, demonstrating its promise as a comprehensive graph representation learner.

cs.LG↗

arXiv2024

Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model

Reinforcement learning (RL) shows great potential in sequential decision-making. At present, mainstream RL algorithms are data-driven, which usually yield better asymptotic performance but much slower convergence compared with model-driven methods. This paper proposes mixed policy gradient (MPG) algorithm, which fuses the empirical data and the transition model in policy gradient (PG) to accelerate convergence without performance degradation. Formally, MPG is constructed as a weighted average of the data-driven and model-driven PGs, where the former is the derivative of the learned Q-value function, and the latter is that of the model-predictive return. To guide the weight design, we analyze and compare the upper bound of each PG error. Relying on that, a rule-based method is employed to heuristically adjust the weights. In particular, to get a better PG, the weight of the data-driven PG is designed to grow along the learning process while the other to decrease. Simulation results show that the MPG method achieves the best asymptotic performance and convergence speed compared with other baseline algorithms.

cs.LG↗

arXiv2024

Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4

This study aims to explore the best practices for utilizing GenAI as a programming tool, through a comparative analysis between GPT-4 and GLM-4. By evaluating prompting strategies at different levels of complexity, we identify that simplest and straightforward prompting strategy yields best code generation results. Additionally, adding a CoT-like preliminary confirmation step would further increase the success rate. Our results reveal that while GPT-4 marginally outperforms GLM-4, the difference is minimal for average users. In our simplified evaluation model, we see a remarkable 30 to 100-fold increase in code generation efficiency over traditional coding norms. Our GenAI Coding Workshop highlights the effectiveness and accessibility of the prompting methodology developed in this study. We observe that GenAI-assisted coding would trigger a paradigm shift in programming landscape, which necessitates developers to take on new roles revolving around supervising and guiding GenAI, and to focus more on setting high-level objectives and engaging more towards innovation.

cs.SE↗

arXiv2024

User Experience Design Professionals' Perceptions of Generative Artificial Intelligence

Among creative professionals, Generative Artificial Intelligence (GenAI) has sparked excitement over its capabilities and fear over unanticipated consequences. How does GenAI impact User Experience Design (UXD) practice, and are fears warranted? We interviewed 20 UX Designers, with diverse experience and across companies (startups to large enterprises). We probed them to characterize their practices, and sample their attitudes, concerns, and expectations. We found that experienced designers are confident in their originality, creativity, and empathic skills, and find GenAI's role as assistive. They emphasized the unique human factors of "enjoyment" and "agency", where humans remain the arbiters of "AI alignment". However, skill degradation, job replacement, and creativity exhaustion can adversely impact junior designers. We discuss implications for human-GenAI collaboration, specifically copyright and ownership, human creativity and agency, and AI literacy and access. Through the lens of responsible and participatory AI, we contribute a deeper understanding of GenAI fears and opportunities for UXD.

cs.CY↗

arXiv2024

Joint Design of ISAC Waveform under PAPR Constraints

In this paper, we formulate the precoding problem of integrated sensing and communication (ISAC) waveform as a non-convex quadratically constrainted quadratic program (QCQP), in which the weighted sum of communication multi-user interference (MUI) and the gap between dual-use waveform and ideal radar waveform is minimized with peak-to-average power ratio (PAPR) constraints. We propose an efficient algorithm based on alternating direction method of multipliers (ADMM), which is able to decouple multiple variables and provide a closed-form solution for each subproblem. In addition, to improve the sensing performance in both spatial and temporal domains, we propose a new criteria to design the ideal radar waveform, in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest. The limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform. Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service (QoS) and sensing performance.

eess.SP↗

arXiv2024

Multi-color Wavefront Sensor using Talbot effect for High-order Harmonic Generation

We present a novel method for multi-color wavefront measurement of high-order harmonic generation beams using the Talbot effect, validated both theoretically and experimentally for the first time. Each harmonic maintains a unique wavefront and produces an independent set of self-images along the optical axis.We achieved the wavefronts reconstruction of three harmonics in a single measurement scan, expanding the spectrally-resolved capability of the conventional Talbot effect wavefront sensor. This breakthrough introduces a novel tool for studying the multi-color wavefront in high-order harmonic generation, unlocking the potential to investigate spatiotemporal ultrafast nonlinear dynamics in attosecond pulse formation on a shot-by-shot basis.

physics.optics↗

arXiv2024

HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for Hypertensive Intracerebral Hemorrhage Research

In this paper, we introduce a new dataset in the medical field of hypertensive intracerebral hemorrhage (HICH), called HICH-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of HICH. This dataset, built upon the foundation of standard text and image data, incorporates specific annotations within the EMRs, extracting key content from the text information, and categorizes the annotation content of imaging data into four types: brain midline, hematoma, left and right cerebral ventricle. HICH-IT aims to be a foundational dataset for feature learning in image segmentation tasks and named entity recognition. To further understand the dataset, we have trained deep learning algorithms to observe the performance. The pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP. The dataset has been uploaded to https://github.com/CYBUS123456/HICH-IT-Datasets. Index Terms-HICH, Deep learning, Intraparenchymal hemorrhage, named entity recognition, novel dataset

cs.CV↗

arXiv2024

A Cross-Language Investigation into Jailbreak Attacks in Large Language Models

Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security challenges, including the risk of 'jailbreak' attacks that manipulate LLMs to produce prohibited content. A particularly underexplored area is the Multilingual Jailbreak attack, where malicious questions are translated into various languages to evade safety filters. Currently, there is a lack of comprehensive empirical studies addressing this specific threat. To address this research gap, we conducted an extensive empirical study on Multilingual Jailbreak attacks. We developed a novel semantic-preserving algorithm to create a multilingual jailbreak dataset and conducted an exhaustive evaluation on both widely-used open-source and commercial LLMs, including GPT-4 and LLaMa. Additionally, we performed interpretability analysis to uncover patterns in Multilingual Jailbreak attacks and implemented a fine-tuning mitigation method. Our findings reveal that our mitigation strategy significantly enhances model defense, reducing the attack success rate by 96.2%. This study provides valuable insights into understanding and mitigating Multilingual Jailbreak attacks.

cs.CR↗

arXiv2024

Single-sized phase-change metasurfaces for dynamic information multiplexing and encryption

Optical metasurfaces empower us to manipulate the electromagnetic space and control light propagation at the nanoscale, offering a powerful tool to achieve modulation of light for information processing and storage. In this work, we propose a phase-change metasurface to realize dynamic multiplexing and encryption of near-field information. Based on the orientation degeneracy and polarization control governed by Malus's law, we elaborately design the orientation distribution of Sb$_2$S$_3$ meta-atoms with the same dimension to simultaneously satisfy the amplitude modulation requirements of different channels. Using the corresponding polarization control as decoding keys, three different nanoprinting images can be displayed, and these multiplexed images can be switched on and off by leveraging the reversible tunability of the Sb$_2$S$_3$ meta-atoms between the amorphous and crystalline states. With the unparalleled advantages of ultra-compactness, simple design strategy, high information density and security, the proposed metasurfaces afford promising prospects for high-end applications in ultracompact and intelligent dynamic display, high-dense optical data storage, optical information encryption, etc.

physics.optics↗

arXiv2024

The Dantzig Selector: Sparse Signals Recovery via l_p-q Minimization

In the paper, we proposed the Dantzig selector based on the $l_{p-q}$ ($0<p\leq1, 1<q\leq2$) minimization for the signal recovery. First, we establish the convex combination representation of sparse vectors under the $l_{p-q}$ minimization problem. Next, we give the signal recovery guarantees that based on two classes of restricted isometry property frames. Last, some graphical illustrations are presented for the sufficient conditions of the signal recovery.

math.OC↗

arXiv2024

Electronic and magnetic excitations in La$_3$Ni$_2$O$_7$

The striking discovery of high-temperature superconductivity (HTSC) of 80 K in a bilayer nickelate La$_3$Ni$_2$O$_7$ under a moderately high pressure of about 14 GPa ignited a new wave of studying HTSC in nickelates. The properties of the parental phase at ambient pressure may contain key information on basic interactions therein and bosons that may mediate pairing giving birth to superconductivity. Moreover, the bilayer structure of La$_3$Ni$_2$O$_7$ may suggest a distinct minimal model in comparison to cuprate superconductors. Here using X-ray absorption spectroscopy and resonant inelastic X-ray scattering, we studied La$_3$Ni$_2$O$_7$ at ambient pressure, and found that Ni 3$d_{x^2-y^2}$, Ni 3$d_{z^2}$, and ligand oxygen 2$p$ orbitals dominate the low-energy physics with a small charge-transfer energy. Remarkably, well-defined optical-like magnetic excitations were found to soften into a quasi-static spin-density-wave ordering, evidencing the strong electronic correlations and rich magnetic properties. Based on a Heisenberg spin model, we found that the inter-layer effective magnetic superexchange interaction is much larger than the intra-layer ones, and proposed two viable magnetic structures. Our results set the foundation for further exploration of La$_3$Ni$_2$O$_7$ superconductor.

cond-mat.supr-con↗