arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 343 records · Page 19Linked to original sources

arXiv2024

Learning the expressibility of quantum circuit ansatz using transformer

With the exponentially faster computation for certain problems, quantum computing has garnered significant attention in recent years. Variational quantum algorithms are crucial methods to implement quantum computing, and an appropriate task-specific quantum circuit ansatz can effectively enhance the quantum advantage of VQAs. However, the vast search space makes it challenging to find the optimal task-specific ansatz. Expressibility, quantifying the diversity of quantum circuit ansatz states to explore the Hilbert space effectively, can be used to evaluate whether one ansatz is superior to another. In this work, we propose using a transformer model to predict the expressibility of quantum circuit ansatze. We construct a dataset containing random PQCs generated by the gatewise pipeline, with varying numbers of qubits and gates. The expressibility of the circuits is calculated using three measures: KL divergence, relative KL divergence, and maximum mean discrepancy. A transformer model is trained on the dataset to capture the intricate relationships between circuit characteristics and expressibility. Four evaluation metrics are employed to assess the performance of the transformer. Numerical results demonstrate that the trained model achieves high performance and robustness across various expressibility measures. This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.

quant-ph↗

arXiv2024

What's in a Name? Beyond Class Indices for Image Recognition

Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models only learn to map an image to a predefined class index, without revealing the actual semantic meaning of the object in the image. In contrast, vision-language models like CLIP are able to assign semantic class names to unseen objects in a 'zero-shot' manner, though they are once again provided a pre-defined set of candidate names at test-time. In this paper, we reconsider the recognition problem and task a vision-language model with assigning class names to images given only a large (essentially unconstrained) vocabulary of categories as prior information. We leverage non-parametric methods to establish meaningful relationships between images, allowing the model to automatically narrow down the pool of candidate names. Our proposed approach entails iteratively clustering the data and employing a voting mechanism to determine the most suitable class names. Additionally, we investigate the potential of incorporating additional textual features to enhance clustering performance. To achieve this, we employ the CLIP vision and text encoders to retrieve relevant texts from an external database, which can provide supplementary semantic information to inform the clustering process. Furthermore, we tackle this problem both in unsupervised and partially supervised settings, as well as with a coarse-grained and fine-grained search space as the unconstrained dictionary. Remarkably, our method leads to a roughly 50% improvement over the baseline on ImageNet in the unsupervised setting.

cs.CV↗

arXiv2024

Improved SQP and SLSQP Algorithms for Feasible Path-based Process Optimisation

Feasible path algorithms have been widely used for process optimisation due to its good convergence. The sequential quadratic programming (SQP) algorithm is usually used to drive the feasible path algorithms towards optimality. However, existing SQP algorithms may suffer from inconsistent quadratic programming (QP) subproblems and numerical noise, especially for ill-conditioned process optimisation problems, leading to a suboptimal or infeasible solution. In this work, we propose an improved SQP algorithm (I-SQP) and an improved sequential least squares programming algorithm (I-SLSQP) that solves a least squares (LSQ) subproblem at each major iteration. A hybrid method through the combination of two existing relaxations is proposed to solve the inconsistent subproblems for better convergence and higher efficiency. We find that a certain part of the dual LSQ algorithm suffers from serious cancellation errors, resulting in an inaccurate search direction or no viable search direction generated. Therefore, the QP solver is used to solve LSQ subproblems in such a situation. The computational results indicates that I-SLSQP is more robust than fmincon in MATLAB, IPOPT, Py-SLSQP and I-SQP. It is also shown that I-SLSQP and Py-SLSQP is superior to I-SQP for ill-conditioned process optimisation problems, whilst I-SQP is more computationally efficient than I-SLSQP and Py-SLSQP for well-conditioned problems.

math.OC↗

arXiv2024

Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or reinforcement learning approaches. These methods usually rely on physical models or handcrafted rewards to accurately describe the specific system, rather than on a generalized understanding like animals do. Here we propose a hierarchical framework to construct primitive-, environmental- and strategic-level knowledge that are all pre-trainable, reusable and enrichable for legged robots. The primitive module summarizes knowledge from animal motion data, where, inspired by large pre-trained models in language and image understanding, we introduce deep generative models to produce motor control signals stimulating legged robots to act like real animals. Then, we shape various traversing capabilities at a higher level to align with the environment by reusing the primitive module. Finally, a strategic module is trained focusing on complex downstream tasks by reusing the knowledge from previous levels. We apply the trained hierarchical controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles and play in a designed challenging multi-agent chase tag game, where lifelike agility and strategy emerge in the robots.

cs.RO↗

arXiv2024

CRUISE on Quantum Computing for Feature Selection in Recommender Systems

Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms. This feature selection problem is a Quadratic Unconstrained Binary Optimization(QUBO) problem. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm compared to using pure Mutual Information. Extensive experiments have demonstrated that the use of Counterfactual Analysis holds great promise for addressing such problems.

cs.IR↗

arXiv2024

A Near-Optimal Category Information Sampling in RFID Systems

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Then, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments validate the practicality of OPT-C.

cs.NI↗

arXiv2024

Pulmonary Embolism Mortality Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data

Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using Computed Tomography Pulmonary Angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE mortality. Materials and Methods: 918 patients (median age 64 years, range 13-99 years, 52% female) with 3,978 CTPAs were identified via retrospective review across three institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes. Four models were developed as follows: (1) using CTPA imaging features only; (2) using clinical variables only; (3) multimodal, integrating both CTPA and clinical variables; and (4) multimodal fused with calculated PESI score. Performance and contribution from each modality were evaluated using concordance index (c-index) and Net Reclassification Improvement, respectively. Performance was compared to PESI predictions using the Wilcoxon signed-rank test. Kaplan-Meier analysis was performed to stratify patients into high- and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction. Results: For both data sets, the PESI-fused and multimodal models achieved higher c-indices than PESI alone. Following stratification of patients into high- and low-risk groups by multimodal and PESI-fused models, mortality outcomes differed significantly (both p<0.001). A strong correlation was found between high-risk grouping and RV dysfunction. Conclusions: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.

cs.CV↗

arXiv2024

An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition

Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. However, these methods extract semantic features simply. They ignore that proper prompt design for rich and fine-grained action cues can provide robust representation space clustering. In order to alleviate the problem of insufficient information available for skeleton sequences, we design an information compensation learning framework from an information-theoretic perspective to improve zero-shot action recognition accuracy with a multi-granularity semantic interaction mechanism. Inspired by ensemble learning, we propose a multi-level alignment (MLA) approach to compensate information for action classes. MLA aligns multi-granularity embeddings with visual embedding through a multi-head scoring mechanism to distinguish semantically similar action names and visually similar actions. Furthermore, we introduce a new loss function sampling method to obtain a tight and robust representation. Finally, these multi-granularity semantic embeddings are synthesized to form a proper decision surface for classification. Significant action recognition performance is achieved when evaluated on the challenging NTU RGB+D, NTU RGB+D 120, and PKU-MMD benchmarks and validate that multi-granularity semantic features facilitate the differentiation of action clusters with similar visual features.

cs.CV↗

arXiv2024

MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model

Deciphering the human visual experience through brain activities captured by fMRI represents a compelling and cutting-edge challenge in the field of neuroscience research. Compared to merely predicting the viewed image itself, decoding brain activity into meaningful captions provides a higher-level interpretation and summarization of visual information, which naturally enhances the application flexibility in real-world situations. In this work, we introduce MindSemantix, a novel multi-modal framework that enables LLMs to comprehend visually-evoked semantic content in brain activity. Our MindSemantix explores a more ideal brain captioning paradigm by weaving LLMs into brain activity analysis, crafting a seamless, end-to-end Brain-Language Model. To effectively capture semantic information from brain responses, we propose Brain-Text Transformer, utilizing a Brain Q-Former as its core architecture. It integrates a pre-trained brain encoder with a frozen LLM to achieve multi-modal alignment of brain-vision-language and establish a robust brain-language correspondence. To enhance the generalizability of neural representations, we pre-train our brain encoder on a large-scale, cross-subject fMRI dataset using self-supervised learning techniques. MindSemantix provides more feasibility to downstream brain decoding tasks such as stimulus reconstruction. Conditioned by MindSemantix captioning, our framework facilitates this process by integrating with advanced generative models like Stable Diffusion and excels in understanding brain visual perception. MindSemantix generates high-quality captions that are deeply rooted in the visual and semantic information derived from brain activity. This approach has demonstrated substantial quantitative improvements over prior art. Our code will be released.

cs.CV↗

arXiv2024

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross LLMs alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologist. Multi-center experiments validate both MRANet's overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies. The code is available at https://github.com/zzs95/MRANet.

eess.IV↗

arXiv2024

A conjecture of Zhi-Wei Sun on matrices concerning multiplicative subgroups of finite fields

Motivated by the recent work of Zhi-Wei Sun on determinants involving the Legendre symbol, in this paper, we study some matrices concerning subgroups of finite fields. For example, let $q\equiv 3\pmod 4$ be an odd prime power and let $ϕ$ be the unique quadratic multiplicative character of the finite field $\mathbb{F}_q$. If set $\{s_1,\cdots,s_{(q-1)/2}\}=\{x^2:\ x\in\mathbb{F}_q\setminus\{0\}\}$, then we prove that $$\det\left[t+ϕ(s_i+s_j)+ϕ(s_i-s_j)\right]_{1\le i,j\le (q-1)/2}=\left(\frac{q-1}{2}t-1\right)q^{\frac{q-3}{4}}.$$ This confirms a conjecture of Zhi-Wei Sun.

math.NT↗

arXiv2024

High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

stat.ME↗

arXiv2024

Region-specific Risk Quantification for Interpretable Prognosis of COVID-19

The COVID-19 pandemic has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates. This paper introduces an interpretable deep survival prediction model designed specifically for improved understanding and trust in COVID-19 prognosis using chest X-ray (CXR) images. By integrating a large-scale pretrained image encoder, Risk-specific Grad-CAM, and anatomical region detection techniques, our approach produces regional interpretable outcomes that effectively capture essential disease features while focusing on rare but critical abnormal regions. Our model's predictive results provide enhanced clarity and transparency through risk area localization, enabling clinicians to make informed decisions regarding COVID-19 diagnosis with better understanding of prognostic insights. We evaluate the proposed method on a multi-center survival dataset and demonstrate its effectiveness via quantitative and qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and time-dependent AUCs (0.799 and 0.691). These results suggest that our explainable deep survival prediction model surpasses traditional survival analysis methods in risk prediction, improving interpretability for clinical decision making and enhancing AI system trustworthiness.

cs.CV↗

arXiv2024

Canonized then Minimized RMSD for Three-Dimensional Structures

Existing molecular canonization algorithms typically operate on one-dimensional (1D) string representations or two-dimensional (2D) connectivity graphs of a molecule and are not able to differentiate equivalent atoms based on three-dimensional (3D) structures. The stereochemical tags on each atom are in fact determined according to established Cahn-Ingold-Prelog (CIP) rules for comparing grades, which can help to further differentiate atoms with similar environment. Therefore, a stereochemical-rule-based canonization algorithm that is capable of assigning canonical indices using 3D structural information is of great value. On top of the Schneider-Sayle-Landrum (SSL) partition-based canonization algorithm, we propose an enhanced canonization algorithm to expand its applicability. The initial index assignment rules are redesigned, so that the obtained canonical indices are compatible with the most of the common CIP Sequence Rules, which greatly eases the stereochemical assignment. Furthermore, a branching tiebreaking step is added to secure an accurate evaluation of the structural difference through the minimized root-mean-square deviation (RMSD) between structures, with an option to include hydrogen atoms or not. Our algorithm is implemented with Python and can efficiently obtain minimized RMSD taking into account of the symmetry of molecular systems , contributing to the fields of drug design, molecular docking, and data analysis of molecular dynamics simulation.

physics.chem-ph↗

arXiv2024

On cyclotomic matrices involving Gauss sums over finite fields

Inspired by the works of L. Carlitz and Z.-W. Sun on cyclotomic matrices, in this paper, we investigate certain cyclotomic matrices involving Gauss sums over finite fields, which can be viewed as finite field analogues of certain matrices related to the Gamma function. For example, let $q=p^n$ be an odd prime power with $p$ prime and $n\in\mathbb{Z}^+$. Let $ζ_p=e^{2π{\bf i}/p}$ and let $χ$ be a generator of the group of all mutiplicative characters of the finite field $\mathbb{F}_q$. For the Gauss sum $$G_q(χ^{r})=\sum_{x\in\mathbb{F}_q}χ^{r}(x)ζ_p^{{\rm Tr}_{\mathbb{F}_q/\mathbb{F}_p}(x)},$$ we prove that $$\det \left[G_q(χ^{2i+2j})\right]_{0\le i,j\le (q-3)/2}=(-1)^{α_p}\left(\frac{q-1}{2}\right)^{\frac{q-1}{2}}2^{\frac{p^{n-1}-1}{2}},$$ where $$α_p= \begin{cases} 1 & \mbox{if}\ n\equiv 1\pmod 2, (p^2+7)/8 & \mbox{if}\ n\equiv 0\pmod 2. \end{cases}$$

math.NT↗

arXiv2024

Four Changing-Look Active Galactic Nuclei Found From Optical Variations

We report the finding of four changing-look (CL) active galactic nuclei (AGN). We selected these sources due to their potential as interesting targets when considering their relatively-large optical flux variations and related mid-infrared flux variations. To identify their CL feature, we use archival spectra from the Sloan Digital Sky Survey (SDSS) taken at least 8 years ago as well as spectra taken recently from the Transient Name Server (TNS) and with the 2.4-m LiJiang telescope (LJT). We study the sources' spectral changes by fitting and determining the H$_α$ and H$_β$ components and verify their CL behavior. When comparing the TNS and/or LJT spectra to the SDSS ones, all four sources showed the appearance of a broad or a stronger broad H$_α$ component and a relatively weak broad H$_β$ component. As two of the four sources are established to have a brighter-and-bluer feature in the photometric data, during the time periods in which the TNS and LJT spectra were taken, this feature likely accompanied the turn-on of the broad components. Thus, we suggest that this brighter-and-bluer feature can be used as a criterion for efficiently finding CL sources among previously spectroscopically classified type 2 AGN, such as from among the sources provided by the SDSS.

astro-ph.GA↗

arXiv2024

Entangling Excitons with Microcavity Photons

We provide a systemic theory to entangle excitons with microcavity photons. This is realized by adopting an exciton-optomechanics system and introducing a nonlinear dispersive interaction with a mechanical oscillator. We show that when either the exciton and cavity modes in the weak-coupling regime, or the two exciton-polariton modes in the strong-coupling regime, are respectively resonant with the optomechanical Stokes and anti-Stokes sidebands, entanglement between excitons and cavity photons, or between two exciton polaritons, can be established. The entanglement is in the steady state and can potentially be achievable at room temperature. In both cases, genuine tripartite entanglement is shown to be present.

quant-ph↗

arXiv2024

Electronic correlations and partial gap in the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$

The discovery of superconductivity with a critical temperature of about 80~K in La$_{3}$Ni$_{2}$O$_{7}$ single crystals under pressure has received enormous attention. La$_{3}$Ni$_{2}$O$_{7}$ is not superconducting under ambient pressure but exhibits a transition at $T^{\ast} \simeq 115$~K. Understanding the electronic correlations and charge dynamics is an important step towards the origin of superconductivity and other instabilities. Here, our optical study shows that La$_{3}$Ni$_{2}$O$_{7}$ features strong electronic correlations which significantly reduce the electron's kinetic energy and place this system in the proximity of the Mott phase. The low-frequency optical conductivity reveals two Drude components arising from multiple bands at the Fermi level. The transition at $T^{\ast}$ removes the Drude component exhibiting non-Fermi liquid behavior, whereas the one with Fermi-liquid behavior is barely affected. These observations in combination with theoretical results suggest that the Fermi surface dominated by the Ni-$d_{3z^{2}-r^{2}}$ orbital is removed due to the transition at $T^{\ast}$. Our experimental results provide pivotal information for understanding the transition at $T^{\ast}$ and superconductivity in La$_{3}$Ni$_{2}$O$_{7}$.

cond-mat.supr-con↗