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Yanjun Li

Publications and source records attributed to Yanjun Li.

At least 37 records · Page 2Linked to original sources
arXiv2025

Further results on permutation pentanomials over ${\mathbb F}_{q^3}$ in characteristic two

Let $q=2^m.$ In a recent paper \cite{Zhang3}, Zhang and Zheng investigated several classes of permutation pentanomials of the form $ε_0x^{d_0}+L(ε_{1}x^{d_1}+ε_{2}x^{d_2})$ over ${\mathbb F}_{q^3}~(d_0=1,2,4)$ from some certain linearized polynomial $L(x)$ by using multivariate method and some techniques to determine the number of the solutions of some equations. They proposed an open problem that there are still some permutation pentanomials of that form have not been proven. In this paper, inspired by the idea of \cite{LiKK}, we further characterize the permutation property of the pentanomials with the above form over ${\mathbb F}_{q^3}~(d_0=1,2,4)$. The techniques in this paper would be useful to investigate more new classes of permutation pentanomials.

math.NT↗
arXiv2024

SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation

Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Large amounts of full-body CT images provide the opportunity to pre-train powerful models, e.g., STU-Net pre-trained in a supervised fashion, to segment numerous anatomical structures. However, it remains unclear in which conditions these pre-trained models can be transferred to various downstream medical segmentation tasks, particularly segmenting the other modalities and diverse targets. To address this problem, a large-scale benchmark for comprehensive evaluation is crucial for finding these conditions. Thus, we collected 87 public datasets varying in modality, target, and sample size to evaluate the transfer ability of full-body CT pre-trained models. We then employed a representative model, STU-Net with multiple model scales, to conduct transfer learning across modalities and targets. Our experimental results show that (1) there may be a bottleneck effect concerning the dataset size in fine-tuning, with more improvement on both small- and large-scale datasets than medium-size ones. (2) Models pre-trained on full-body CT demonstrate effective modality transfer, adapting well to other modalities such as MRI. (3) Pre-training on the full-body CT not only supports strong performance in structure detection but also shows efficacy in lesion detection, showcasing adaptability across target tasks. We hope that this large-scale open evaluation of transfer learning can direct future research in volumetric medical image segmentation.

eess.IV↗
arXiv2024

Cohort profile: the Northwest China Real-world and Population-based Cohort

The Northwest China Real-World and Population-based cohort is an ongoing prospective cohort with more than 25 million population, covering almost all residents across approximately 1.66 million square kilometers in northwest China; The cohort integrates data from various sources, including health profiles, examination records, electronic health records, mortality records, statistical yearbooks, and environmental datasets, covering comprehensive health-related factors such as demographics, lifestyle factors, family medical history, living conditions, enrollment in national public health services, physical examinations, blood assay tests, diagnostic assessments, disease outcomes, and cause-specific mortality. This real-world dataset can evaluate clinical treatment effectiveness and prognosis, assess impact of health policy, and investigate the health effects of multiple risk factors . From January 2019 to December 2023, the cohort has included 13,634,481 participants, accumulating 47,050,707 person-years of follow-up, with 13,598,407 medical diagnosis records and 881,114 recorded deaths. Cohort data are available upon request. De-identified and anonymized data are stored on local servers and accessed through a data-sharing platform, enabling users to utilize the data without direct access to the raw information. A description of the proposed research can be sent to Yining Yang & Qian Di.

cs.CY↗
arXiv2024

GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI

Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 53.96%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI.

eess.IV↗
arXiv2024

A Quantum Automatic Tool for Finding Impossible Differentials

Due to the superiority of quantum computing, traditional cryptography is facing severe threat. This makes the security evaluation of cryptographic systems in quantum attack models significant and urgent. For symmetric ciphers, the security analysis heavily relies on cyptanalytic tools. Thus exploring the use of quantum algorithms to traditional cyptanalytic tools has drawn a lot of attention. In this study, we utilize quantum algorithms to improve impossible differential attack, and design two quantum automatic tools for searching impossible differentials. The proposed quantum algorithms exploit the idea of miss-in-the-middle and the properties of truncated differentials. We rigorously prove their validity and calculate the quantum resources required to implement them. Compared to existing classical automatic cryptanalysis, the quantum tools proposed have the advantage of accurately characterizing S-boxes while only requiring polynomial complexity, and can take into consideration the impact of the key schedules in single-key model.

quant-ph↗
arXiv2024

Inoculating solid-state homogeneous precipitation by impurity atoms through a spinodal decomposition like pathway

Solid-state homogeneous precipitation of nano-sized precipitates is one of the most effective processes to strengthen metal alloys, where the final density and size distribution of precipitates are largely controlled by the precipitation kinetics. Here, we report a strategy to inoculate the homogeneous precipitation of coherent precipitates to enhance the precipitation strengthening. Using the technologically important dilute Al-Zr alloys as an example, we demonstrate that an addition of a trace level of economical and readily available, non-L1$_{2}$ phase forming impurity atoms, X (X= Sn, Sb, Bi or Cd) and Si, can significantly enhance the diffusivity of Zr atoms and overturn the precipitation of L1$_{2}$-structured Al$_{3}$Zr nanoparticles from the classical homogeneous nucleation and growth pathway into a nonclassical nucleation pathway: Al$_{3}$Zr forms through the spontaneous formation of nano-scale local concentration fluctuations of Zr atoms on Zr-X(-Si)-vacancy clusters followed by a continuous increase of the concentration and chemical short-range ordering (CSRO). Such an impurity atoms induced heterogeneous nucleation based on a "spinodal decomposition like" mechanism dramatically accelerates the precipitation kinetics, leading to an order of magnitude higher number density of precipitates and a record high hardening efficiency of solute Zr atoms. By formulating the generalized selection principles for inoculating impurity elements, this inoculation strategy should be extendable to a broader range of materials to further explore the precipitation strengthening potentials.

cond-mat.mtrl-sci↗
arXiv2024

Minimal Ternary Linear Codes from Vectorial Functions

The study on minimal linear codes has received great attention due to their significant applications in secret sharing schemes and secure two-party computation. Until now, numerous minimal linear codes have been discovered. However, to the best of our knowledge, no infinite family of minimal ternary linear codes was found from vectorial functions. In this paper, we present a necessary and sufficient condition for a large class of ternary linear codes from vectorial functions such that those codes are minimal. Based on that, we construct several minimal ternary linear codes with three-weight from vectorial regular plateaued functions, and determine their weight distributions. Moreover, we also give a necessary and sufficient condition for a large family of ternary linear codes from vectorial functions such that the codes are minimal and violate the AB condition simultaneously. According to this characterization, we find several minimal ternary linear codes violating the AB condition. Notably, our results show that our method can be applied to solve a problem on minimal linear codes proposed by Li et al.

cs.IT↗
arXiv2024

Morphological Profiling for Drug Discovery in the Era of Deep Learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

q-bio.QM↗
arXiv2023

BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation

Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets, which are time-consuming, computationally expensive, and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction, and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.

cs.LG↗
arXiv2023

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

Initial hours of hospital admission impact clinical trajectory, but early clinical decisions often suffer due to data paucity. With clustering analysis for vital signs within six hours of admission, patient phenotypes with distinct pathophysiological signatures and outcomes may support early clinical decisions. We created a single-center, longitudinal EHR dataset for 75,762 adults admitted to a tertiary care center for 6+ hours. We proposed a deep temporal interpolation and clustering network to extract latent representations from sparse, irregularly sampled vital sign data and derived distinct patient phenotypes in a training cohort (n=41,502). Model and hyper-parameters were chosen based on a validation cohort (n=17,415). Test cohort (n=16,845) was used to analyze reproducibility and correlation with biomarkers. The training, validation, and testing cohorts had similar distributions of age (54-55 yrs), sex (55% female), race, comorbidities, and illness severity. Four clusters were identified. Phenotype A (18%) had most comorbid disease with higher rate of prolonged respiratory insufficiency, acute kidney injury, sepsis, and three-year mortality. Phenotypes B (33%) and C (31%) had diffuse patterns of mild organ dysfunction. Phenotype B had favorable short-term outcomes but second-highest three-year mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) had early/persistent hypotension, high rate of early surgery, and substantial biomarker rate of inflammation but second-lowest three-year mortality. After comparing phenotypes' SOFA scores, clustering results did not simply repeat other acuity assessments. In a heterogeneous cohort, four phenotypes with distinct categories of disease and outcomes were identified by a deep temporal interpolation and clustering network. This tool may impact triage decisions and clinical decision-support under time constraints.

cs.LG↗
arXiv2023

Tableaux for the Logic of Strategically Knowing How

The logic of goal-directed knowing-how extends the standard epistemic logic with an operator of knowing-how. The knowing-how operator is interpreted as that there exists a strategy such that the agent knows that the strategy can make sure that p. This paper presents a tableau procedure for the multi-agent version of the logic of strategically knowing-how and shows the soundness and completeness of this tableau procedure. This paper also shows that the satisfiability problem of the logic can be decided in PSPACE.

cs.LO↗
arXiv2023

A Bioinspired Synthetic Nervous System Controller for Pick-and-Place Manipulation

The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the design of functional subnetworks (FSNs) to realize dynamical systems. However, the FSN approach has previously relied on the analytical solution of the governing equations, which is difficult for designing more complex NN controllers. Incorporating plasticity into SNSs and using learning algorithms to tune the parameters offers a promising solution for systematic design in this situation. In this paper, we theoretically analyze the computational advantages of SNSs compared with other classical artificial neural networks. We then use learning algorithms to develop compact subnetworks for implementing addition, subtraction, division, and multiplication. We also combine the learning-based methodology with a bioinspired architecture to design an interpretable SNS for the pick-and-place control of a simulated gantry system. Finally, we show that the SNS controller is successfully transferred to a real-world robotic platform without further tuning of the parameters, verifying the effectiveness of our approach.

cs.RO↗
arXiv2022

A locking-free discontinuous Galerkin method for linear elastic Steklov eigenvalue problem

In this paper, a discontinuous Galerkin finite element method of Nitsche's version for the Steklov eigenvalue problem in linear elasticity is presented. The a priori error estimates are analyzed under a low regularity condition, and the robustness with respect to nearly incompressible materials (locking-free) is proven. Furthermore, some numerical experiments are reported to show the effectiveness and robustness of the proposed method.

math.NA↗
arXiv2022

Minimal Binary Linear Codes from Vectorial Boolean Functions

Recently, much progress has been made to construct minimal linear codes due to their preference in secret sharing schemes and secure two-party computation. In this paper, we put forward a new method to construct minimal linear codes by using vectorial Boolean functions. Firstly, we give a necessary and sufficient condition for a generic class of linear codes from vectorial Boolean functions to be minimal. Based on that, we derive some new three-weight minimal linear codes and determine their weight distributions. Secondly, we obtain a necessary and sufficient condition for another generic class of linear codes from vectorial Boolean functions to be minimal and to be violated the AB condition. As a result, we get three infinite families of minimal linear codes violating the AB condition. To the best of our knowledge, this is the first time that minimal liner codes are constructed from vectorial Boolean functions. Compared with other known ones, in general the minimal liner codes obtained in this paper have higher dimensions.

cs.IT↗
arXiv2022

Analysis and visualization of spatial transcriptomic data

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

q-bio.QM↗
arXiv2021

Knowing How to Plan

Various planning-based know-how logics have been studied in the recent literature. In this paper, we use such a logic to do know-how-based planning via model checking. In particular, we can handle the higher-order epistemic planning involving know-how formulas as the goal, e.g., find a plan to make sure p such that the adversary does not know how to make p false in the future. We give a PTIME algorithm for the model checking problem over finite epistemic transition systems and axiomatize the logic under the assumption of perfect recall.

cs.AI↗
arXiv2021

Leaderless Consensus of a Hierarchical Cyber-Physical System

This paper models a class of hierarchical cyber-physical systems and studies its associated consensus problem. The model has a pyramid structure, which reflects many realistic natural or human systems. By analyzing the spectrum of the coupling matrix, it is shown that all nodes in the physical layer can reach a consensus based on the proposed distributed protocols without interlayer delays. Then, the result is extended to the case with interlayer delays. A necessary and sufficient condition for consensus-seeking is derived from the frequency domain perspective, which describes a permissible range of the delay. Finally, the application of the proposed model in the power-sharing problem is simulated to demonstrate the effectiveness and significance of the analytic results.

eess.SY↗