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1. Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective

Author

Li, Jinhao, Xu, Jiaming, Huang, Shan, Chen, Yonghua, Li, Wen, Liu, Jun, Lian, Yaoxiu, Pan, Jiayi, Ding, Li, Zhou, Hao, Wang, Yu, and Dai, Guohao

Subjects
Computer Science - Hardware Architecture, Computer Science - Machine Learning
Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across various fields, from natural language understanding to text generation. Compared to non-generative LLMs like BERT and DeBERTa, generative LLMs like GPT series and Llama series are currently the main focus due to their superior algorithmic performance. The advancements in generative LLMs are closely intertwined with the development of hardware capabilities. Various hardware platforms exhibit distinct hardware characteristics, which can help improve LLM inference performance. Therefore, this paper comprehensively surveys efficient generative LLM inference on different hardware platforms. First, we provide an overview of the algorithm architecture of mainstream generative LLMs and delve into the inference process. Then, we summarize different optimization methods for different platforms such as CPU, GPU, FPGA, ASIC, and PIM/NDP, and provide inference results for generative LLMs. Furthermore, we perform a qualitative and quantitative comparison of inference performance with batch sizes 1 and 8 on different hardware platforms by considering hardware power consumption, absolute inference speed (tokens/s), and energy efficiency (tokens/J). We compare the performance of the same optimization methods across different hardware platforms, the performance across different hardware platforms, and the performance of different methods on the same hardware platform. This provides a systematic and comprehensive summary of existing inference acceleration work by integrating software optimization methods and hardware platforms, which can point to the future trends and potential developments of generative LLMs and hardware technology for edge-side scenarios., Comment: 43 pages, 15 figures

Published
2024

2. MARCA: Mamba Accelerator with ReConfigurable Architecture

Author

Li, Jinhao, Huang, Shan, Xu, Jiaming, Liu, Jun, Ding, Li, Xu, Ningyi, and Dai, Guohao

Subjects
Computer Science - Hardware Architecture, Computer Science - Artificial Intelligence
Abstract

We propose a Mamba accelerator with reconfigurable architecture, MARCA.We propose three novel approaches in this paper. (1) Reduction alternative PE array architecture for both linear and element-wise operations. For linear operations, the reduction tree connected to PE arrays is enabled and executes the reduction operation. For element-wise operations, the reduction tree is disabled and the output bypasses. (2) Reusable nonlinear function unit based on the reconfigurable PE. We decompose the exponential function into element-wise operations and a shift operation by a fast biased exponential algorithm, and the activation function (SiLU) into a range detection and element-wise operations by a piecewise approximation algorithm. Thus, the reconfigurable PEs are reused to execute nonlinear functions with negligible accuracy loss.(3) Intra-operation and inter-operation buffer management strategy. We propose intra-operation buffer management strategy to maximize input data sharing for linear operations within operations, and inter-operation strategy for element-wise operations between operations. We conduct extensive experiments on Mamba model families with different sizes.MARCA achieves up to 463.22$\times$/11.66$\times$ speedup and up to 9761.42$\times$/242.52$\times$ energy efficiency compared to Intel Xeon 8358P CPU and NVIDIA Tesla A100 GPU implementations, respectively., Comment: 9 pages, 10 figures, accepted by ICCAD 2024. arXiv admin note: text overlap with arXiv:2001.02514 by other authors

Published
2024

3. Application of Eight Machine Learning Algorithms in the Establishment of Infertility and Pregnancy Diagnostic Models: A Comprehensive Analysis of Amino Acid and Carnitine Metabolism.

Author

Zhang, Rui, Zhou, Lei, Hao, Xiaoyan, Yang, Liu, Ding, Li, Xing, Ruiqing, Hu, Juanjuan, Wang, Fengjuan, Zhai, Xiaonan, Guo, Yuanbing, Cai, Zheng, Gao, Jiawei, Yang, Jun, and Liu, Jiayun

Subjects
amino acids, carnitines, infertility, metabolomics, pregnancy
Abstract

To explore the effects of altered amino acids (AAs) and the carnitine metabolism in non-pregnant women with infertility (NPWI), pregnant women without infertility (PWI) and infertility-treated pregnant women (ITPW) compared with non-pregnant women (NPW, control), and develop more efficient models for the diagnosis of infertility and pregnancy, 496 samples were evaluated for levels of 21 AAs and 55 carnitines using targeted high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Three methods were used to screen the biomarkers for modeling, with eight algorithms used to build and validate the model. The ROC, sensitivity, specificity, and accuracy of the infertility diagnosis training model were higher than 0.956, 82.89, 66.64, and 82.57%, respectively, whereas those of the validated model were higher than 0.896, 77.67, 69.72, and 83.38%, respectively. The ROC, sensitivity, specificity, and accuracy of the pregnancy diagnosis training model were >0.994, 96.23, 97.79, and 97.69%, respectively, whereas those of the validated model were >0.572, 96.39, 93.03, and 94.71%, respectively. Our findings indicate that pregnancy may alter the AA and carnitine metabolism in women with infertility to match the internal environment of PWI. The developed model demonstrated good performance and high sensitivity for facilitating infertility and pregnancy diagnosis.

Published
2024

4. Pareto-Optimal Learning from Preferences with Hidden Context

Author

Boldi, Ryan, Ding, Li, Spector, Lee, and Niekum, Scott

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Ensuring AI models align with human values is essential for their safety and functionality. Reinforcement learning from human feedback (RLHF) uses human preferences to achieve this alignment. However, preferences sourced from diverse populations can result in point estimates of human values that may be sub-optimal or unfair to specific groups. We propose Pareto Optimal Preference Learning (POPL), which frames discrepant group preferences as objectives with potential trade-offs, aiming for policies that are Pareto-optimal on the preference dataset. POPL utilizes Lexicase selection, an iterative process to select diverse and Pareto-optimal solutions. Our empirical evaluations demonstrate that POPL surpasses baseline methods in learning sets of reward functions, effectively catering to distinct groups without access to group numbers or membership labels. Furthermore, we illustrate that POPL can serve as a foundation for techniques optimizing specific notions of group fairness, ensuring inclusive and equitable AI model alignment.

Published
2024

6. Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations.

Author

Meric-Bernstam, Funda, Lloyd, Michael, Koc, Soner, Evrard, Yvonne, McShane, Lisa, Lewis, Michael, Evans, Kurt, Li, Dali, Rubinstein, Lawrence, Welm, Alana, Dean, Dennis, Srivastava, Anuj, Grover, Jeffrey, Ha, Min, Chen, Huiqin, Huang, Xuelin, Varadarajan, Kaushik, Wang, Jing, Roth, Jack, Welm, Bryan, Govinden, Ramaswamy, Ding, Li, Kaochar, Salma, Mitsiades, Nicholas, Carvajal-Carmona, Luis, Herylyn, Meenhard, Davies, Michael, Shapiro, Geoffrey, Fields, Ryan, Trevino, Jose, Harrell, Joshua, Doroshow, James, Chuang, Jeffrey, and Moscow, Jeffrey

Subjects
Humans, Animals, Xenograft Model Antitumor Assays, Neoplasms, National Cancer Institute (U.S.), United States, Mice, Antineoplastic Agents, Consensus
Abstract

Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Published
2024

7. Tumour evolution and microenvironment interactions in 2D and 3D space

Author

Mo, Chia-Kuei, Liu, Jingxian, Chen, Siqi, Storrs, Erik, Targino da Costa, Andre Luiz N., Houston, Andrew, Wendl, Michael C., Jayasinghe, Reyka G., Iglesia, Michael D., Ma, Cong, Herndon, John M., Southard-Smith, Austin N., Liu, Xinhao, Mudd, Jacqueline, Karpova, Alla, Shinkle, Andrew, Goedegebuure, S. Peter, Abdelzaher, Abdurrahman Taha Mousa Ali, Bo, Peng, Fulghum, Lauren, Livingston, Samantha, Balaban, Metin, Hill, Angela, Ippolito, Joseph E., Thorsson, Vesteinn, Held, Jason M., Hagemann, Ian S., Kim, Eric H., Bayguinov, Peter O., Kim, Albert H., Mullen, Mary M., Shoghi, Kooresh I., Ju, Tao, Reimers, Melissa A., Weimholt, Cody, Kang, Liang-I, Puram, Sidharth V., Veis, Deborah J., Pachynski, Russell, Fuh, Katherine C., Chheda, Milan G., Gillanders, William E., Fields, Ryan C., Raphael, Benjamin J., Chen, Feng, and Ding, Li

Published
2024
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8. DALex: Lexicase-like Selection via Diverse Aggregation

Author

Ni, Andrew, Ding, Li, and Spector, Lee

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

Lexicase selection has been shown to provide advantages over other selection algorithms in several areas of evolutionary computation and machine learning. In its standard form, lexicase selection filters a population or other collection based on randomly ordered training cases that are considered one at a time. This iterated filtering process can be time-consuming, particularly in settings with large numbers of training cases. In this paper, we propose a new method that is nearly equivalent to lexicase selection in terms of the individuals that it selects, but which does so significantly more quickly. The new method, called DALex (for Diversely Aggregated Lexicase), selects the best individual with respect to a weighted sum of training case errors, where the weights are randomly sampled. This allows us to formulate the core computation required for selection as matrix multiplication instead of recursive loops of comparisons, which in turn allows us to take advantage of optimized and parallel algorithms designed for matrix multiplication for speedup. Furthermore, we show that we can interpolate between the behavior of lexicase selection and its "relaxed" variants, such as epsilon or batch lexicase selection, by adjusting a single hyperparameter, named "particularity pressure," which represents the importance granted to each individual training case. Results on program synthesis, deep learning, symbolic regression, and learning classifier systems demonstrate that DALex achieves significant speedups over lexicase selection and its relaxed variants while maintaining almost identical problem-solving performance. Under a fixed computational budget, these savings free up resources that can be directed towards increasing population size or the number of generations, enabling the potential for solving more difficult problems., Comment: 15 pages, 4 figures. Accepted at EuroGP'24

Published
2024

9. Optimizing Neural Networks with Gradient Lexicase Selection

Author

Ding, Li and Spector, Lee

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead to both stagnation at local optima and poor generalization. Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy. In this paper, we investigate how lexicase selection, in its general form, can be integrated into the context of deep learning to enhance generalization. We propose Gradient Lexicase Selection, an optimization framework that combines gradient descent and lexicase selection in an evolutionary fashion. Our experimental results demonstrate that the proposed method improves the generalization performance of various widely-used deep neural network architectures across three image classification benchmarks. Additionally, qualitative analysis suggests that our method assists networks in learning more diverse representations. Our source code is available on GitHub: https://github.com/ld-ing/gradient-lexicase., Comment: ICLR 2022

Published
2023

10. Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance

Author

Boldi, Ryan, Ding, Li, and Spector, Lee

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Navigating deceptive domains has often been a challenge in machine learning due to search algorithms getting stuck at sub-optimal local optima. Many algorithms have been proposed to navigate these domains by explicitly maintaining diversity or equivalently promoting exploration, such as Novelty Search or other so-called Quality Diversity algorithms. In this paper, we present an approach with promise to solve deceptive domains without explicit diversity maintenance by optimizing a potentially large set of defined objectives. These objectives can be extracted directly from the environment by sub-aggregating the raw performance of individuals in a variety of ways. We use lexicase selection to optimize for these objectives as it has been shown to implicitly maintain population diversity. We compare this technique with a varying number of objectives to a commonly used quality diversity algorithm, MAP-Elites, on a set of discrete optimization as well as reinforcement learning domains with varying degrees of deception. We find that decomposing objectives into many objectives and optimizing them outperforms MAP-Elites on the deceptive domains that we explore. Furthermore, we find that this technique results in competitive performance on the diversity-focused metrics of QD-Score and Coverage, without explicitly optimizing for these things. Our ablation study shows that this technique is robust to different subaggregation techniques. However, when it comes to non-deceptive, or ``illumination" domains, quality diversity techniques generally outperform our objective-based framework with respect to exploration (but not exploitation), hinting at potential directions for future work., Comment: Published at the Workshop on Agent Learning in Open-Endedness (ALOE) at NeurIPS 2023

Published
2023

11. Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation

Author

Ding, Li, Zoghi, Masrour, Tennenholtz, Guy, and Karimzadehgan, Maryam

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model parameter updates, assess them using pertinent evaluation methods, and then adapt the model based on the optimal updates and previous progress history. EV3 offers substantial flexibility without imposing stringent constraints like differentiability on the key objectives relevant to the tasks of interest, allowing for exploratory updates with intentionally-biased gradients and through a diversity of losses and optimizers. Additionally, the assessment phase provides reliable safety controls to ensure robust generalization, and can dynamically prioritize tasks in scenarios with multiple objectives. With inspiration drawn from evolutionary algorithms, meta-learning, and neural architecture search, we investigate an application of EV3 to knowledge distillation. Our experimental results illustrate EV3's capability to safely explore the modeling landscape, while hinting at its potential applicability across numerous domains due to its inherent flexibility and adaptability. Finally, we provide a JAX implementation of EV3, along with source code for experiments, available at: https://github.com/google-research/google-research/tree/master/ev3., Comment: NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World (RealML)

Published
2023

12. Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization

Author

Ding, Li, Zhang, Jenny, Clune, Jeff, Spector, Lee, and Lehman, Joel

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when RLHF is commonly used to optimize for average human preferences, especially in generative tasks that demand diverse model responses. Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics. This paper introduces Quality Diversity through Human Feedback (QDHF), a novel approach that progressively infers diversity metrics from human judgments of similarity among solutions, thereby enhancing the applicability and effectiveness of QD algorithms in complex and open-ended domains. Empirical studies show that QDHF significantly outperforms state-of-the-art methods in automatic diversity discovery and matches the efficacy of QD with manually crafted diversity metrics on standard benchmarks in robotics and reinforcement learning. Notably, in open-ended generative tasks, QDHF substantially enhances the diversity of text-to-image generation from a diffusion model and is more favorably received in user studies. We conclude by analyzing QDHF's scalability, robustness, and quality of derived diversity metrics, emphasizing its strength in open-ended optimization tasks. Code and tutorials are available at https://liding.info/qdhf., Comment: ICML 2024

Published
2023

13. Effect of genistein supplementation on microenvironment regulation of breast tumors in obese mice

Author

Shengzi Jin, Yingce Zheng, Ding Li, Xingyao Liu, Tingting Zhu, Shuang Wang, Zhonghua Liu, and Yun Liu

Subjects
Obesity, Breast cancer, Genistein, Tumor microenvironment, Cancer associated adipocyte, PPAR-γ, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Obesity is an important risk factor for breast cancer in women before and after menopause. Adipocytes, key mediators in the tumor microenvironment, play a pivotal role in the relationship between obesity with cancer. However, the potential of dietary components in modulating this relationship remains underexplored. Genistein, a soy-derived isoflavone, has shown promise in reducing breast cancer risk, attenuating obesity-associated inflammation, and improving insulin resistance. However, there are no reports examining whether genistein has the ability to reduce the effects of obesity on breast tumor development. In this study, we constructed a mammary tumor model in ovariectomized obese mice and examined the effects of genistein on body condition and tumor growth. Moreover, the effects of genistein on the tumor microenvironment were examined via experimental observation of peritumoral adipocytes and macrophages. In addition, we further investigated the effect of genistein on adipocyte and breast cancer cell crosstalk via coculture experiments. Our findings indicate that dietary genistein significantly alleviates obesity, systemic inflammation, and metabolic disorders induced by a high-fat diet in ovariectomized mice. Notably, it also inhibits tumor growth in vivo. The impact of genistein extends to the tumor microenvironment, where it reduces the production of cancer-associated adipocytes (CAAs) and the recruitment of M2d-subtype macrophages. In vitro, genistein mitigates the transition of adipocytes into CAAs and inhibits the expression of inflammatory factors by activating PPAR-γ pathway and degrading nuclear NF-κB. Furthermore, it impedes the acquisition of invasive properties and epithelial‒mesenchymal transition in breast cancer cells under CAA-induced inflammation, disrupting the Wnt3a/β-catenin pathway. Intriguingly, the PPAR-γ inhibitor T0070907 counteracted the effects of genistein in the coculture system, underscoring the specificity of its action. Our study revealed that genistein can mitigate the adverse effects of obesity on breast cancer by modulating the tumor microenvironment. These findings provide new insights into how genistein intake and a soy-based diet can reduce breast cancer risk.

Published
2024
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14. Impact of repeated heat-pressing on the microstructure and flexural strength of lithium disilicate glass-ceramics

Author

Jiale Li, Ding Li, Qi Wang, Fu Wang, and Zhenzhen Zhang

Subjects
IPS e.max Press, Flexural strength, Repeated heat-pressing, Microstructure, Dentistry, RK1-715
Abstract

Abstract Background The leftover material from the heat-pressing of IPS e.max Press ceramic is often discarded, despite some laboratories exploring its potential for reuse. However, there is a lack of data on the performance of IPS e.max Press ceramic when combined with the button portions. This study investigated the impact of repeated heat-pressing on the crystal structure and flexural strength of lithium disilicate glass-ceramic (LDGC). Methods Specimens (N = 30, n = 10 per group) were categorized based on the number of heat-pressing cycles: G0 (control group, no heat-pressing), G1 (one cycle of heat-pressing), and G2 (two cycles of heat-pressing). The crystal structure of LDGC bars was characterized using X-ray diffraction (XRD). Flexural strength was tested, and microstructures were analyzed via scanning electron microscopy (SEM) and the ImageJ processing program. Data were analyzed using one-way analysis of variance (ANOVA), and multiple pairwise comparisons of means were performed with Tukey’s post-hoc test. Results G2 exhibited significantly lower flexural strength and crystallinity, as well as larger crystal size, compared to G1 and G0 (p

Published
2024
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15. CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild

Author

Ding, Li, Terwilliger, Jack, Parab, Aishni, Wang, Meng, Fridman, Lex, Mehler, Bruce, and Reimer, Bryan

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computer Vision and Pattern Recognition
Abstract

Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans' visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis., Comment: ACM Transactions on Computer-Human Interaction

Published
2023
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16. Particularity

Author

Spector, Lee, Ding, Li, and Boldi, Ryan

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.2, I.2.6
Abstract

We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this "particularity" approach from the use of lexicase selection in genetic programming to "particularist" approaches to other forms of machine learning and to the design of adaptive systems more generally., Comment: Genetic Programming Theory and Practice XX

Published
2023

17. A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights

Author

Yu, Jing, Gui, Xiuqi, Zou, Yunhao, Liu, Qian, Yang, Zhicheng, An, Jusheng, Guo, Xuan, Wang, Kaihua, Guo, Jiaming, Huang, Manni, Zhou, Shuhan, Zuo, Jing, Chen, Yimin, Deng, Lu, Yuan, Guangwen, Li, Ning, Song, Yan, Jia, Jia, Zeng, Jia, Zhao, Yuxi, Liu, Xianming, Du, Xiaoxian, Liu, Yansheng, Wang, Pei, Zhang, Bing, Ding, Li, Robles, Ana I., Rodriguez, Henry, Zhou, Hu, Shao, Zhen, Wu, Lingying, and Gao, Daming

Published
2024
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25. Combined KRAS-MAPK pathway inhibitors and HER2-directed drug conjugate is efficacious in pancreatic cancer

Author

Bulle, Ashenafi, Liu, Peng, Seehra, Kuljeet, Bansod, Sapana, Chen, Yali, Zahra, Kiran, Somani, Vikas, Khawar, Iftikhar Ali, Chen, Hung-Po, Dodhiawala, Paarth B., Li, Lin, Geng, Yutong, Mo, Chia-Kuei, Mahsl, Jay, Ding, Li, Govindan, Ramaswamy, Davies, Sherri, Mudd, Jacqueline, Hawkins, William G., Fields, Ryan C., DeNardo, David G., Knoerzer, Deborah, Held, Jason M., Grierson, Patrick M., Wang-Gillam, Andrea, Ruzinova, Marianna B., and Lim, Kian-Huat

Published
2024
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29. Probabilistic Lexicase Selection

Author

Ding, Li, Pantridge, Edward, and Spector, Lee

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency., Comment: GECCO 2023

Published
2023
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30. Efficient Halftoning via Deep Reinforcement Learning

Author

Jiang, Haitian, Xiong, Dongliang, Jiang, Xiaowen, Ding, Li, Chen, Liang, and Huang, Kai

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Halftoning aims to reproduce a continuous-tone image with pixels whose intensities are constrained to two discrete levels. This technique has been deployed on every printer, and the majority of them adopt fast methods (e.g., ordered dithering, error diffusion) that fail to render structural details, which determine halftone's quality. Other prior methods of pursuing visual pleasure by searching for the optimal halftone solution, on the contrary, suffer from their high computational cost. In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach. Specifically, we formulate halftoning as a reinforcement learning problem, in which each binary pixel's value is regarded as an action chosen by a virtual agent with a shared fully convolutional neural network (CNN) policy. In the offline phase, an effective gradient estimator is utilized to train the agents in producing high-quality halftones in one action step. Then, halftones can be generated online by one fast CNN inference. Besides, we propose a novel anisotropy suppressing loss function, which brings the desirable blue-noise property. Finally, we find that optimizing SSIM could result in holes in flat areas, which can be avoided by weighting the metric with the contone's contrast map. Experiments show that our framework can effectively train a light-weight CNN, which is 15x faster than previous structure-aware methods, to generate blue-noise halftones with satisfactory visual quality. We also present a prototype of deep multitoning to demonstrate the extensibility of our method.

Published
2023
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31. Operational Research: Methods and Applications

Author

Petropoulos, Fotios, Laporte, Gilbert, Aktas, Emel, Alumur, Sibel A., Archetti, Claudia, Ayhan, Hayriye, Battarra, Maria, Bennell, Julia A., Bourjolly, Jean-Marie, Boylan, John E., Breton, Michèle, Canca, David, Charlin, Laurent, Chen, Bo, Cicek, Cihan Tugrul, Cox Jr, Louis Anthony, Currie, Christine S. M., Demeulemeester, Erik, Ding, Li, Disney, Stephen M., Ehrgott, Matthias, Eppler, Martin J., Erdoğan, Güneş, Fortz, Bernard, Franco, L. Alberto, Frische, Jens, Greco, Salvatore, Gregory, Amanda J., Hämäläinen, Raimo P., Herroelen, Willy, Hewitt, Mike, Holmström, Jan, Hooker, John N., Işık, Tuğçe, Johnes, Jill, Kara, Bahar Y., Karsu, Özlem, Kent, Katherine, Köhler, Charlotte, Kunc, Martin, Kuo, Yong-Hong, Lienert, Judit, Letchford, Adam N., Leung, Janny, Li, Dong, Li, Haitao, Ljubić, Ivana, Lodi, Andrea, Lozano, Sebastián, Lurkin, Virginie, Martello, Silvano, McHale, Ian G., Midgley, Gerald, Morecroft, John D. W., Mutha, Akshay, Oğuz, Ceyda, Petrovic, Sanja, Pferschy, Ulrich, Psaraftis, Harilaos N., Rose, Sam, Saarinen, Lauri, Salhi, Said, Song, Jing-Sheng, Sotiros, Dimitrios, Stecke, Kathryn E., Strauss, Arne K., Tarhan, İstenç, Thielen, Clemens, Toth, Paolo, Berghe, Greet Vanden, Vasilakis, Christos, Vaze, Vikrant, Vigo, Daniele, Virtanen, Kai, Wang, Xun, Weron, Rafał, White, Leroy, Van Woensel, Tom, Yearworth, Mike, Yıldırım, E. Alper, Zaccour, Georges, and Zhao, Xuying

Subjects
Mathematics - Optimization and Control
Abstract

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.

Published
2023
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32. Signaling pathways activated and regulated by stem cell-derived exosome therapy

Author

Ding Li, Danni Li, Zhao Wang, Jiaojiao Li, Khawar Ali Shahzad, Yanhong Wang, and Fei Tan

Subjects
Exosome, Stem cell, Pathway, Signaling cascade, Surgery, Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5, Biochemistry, QD415-436
Abstract

Abstract Stem cell-derived exosomes exert comparable therapeutic effects to those of their parental stem cells without causing immunogenic, tumorigenic, and ethical disadvantages. Their therapeutic advantages are manifested in the management of a broad spectrum of diseases, and their dosing versatility are exemplified by systemic administration and local delivery. Furthermore, the activation and regulation of various signaling cascades have provided foundation for the claimed curative effects of exosomal therapy. Unlike other relevant reviews focusing on the upstream aspects (e.g., yield, isolation, modification), and downstream aspects (e.g. phenotypic changes, tissue response, cellular behavior) of stem cell-derived exosome therapy, this unique review endeavors to focus on various affected signaling pathways. After meticulous dissection of relevant literature from the past five years, we present this comprehensive, up-to-date, disease-specific, and pathway-oriented review. Exosomes sourced from various types of stem cells can regulate major signaling pathways (e.g., the PTEN/PI3K/Akt/mTOR, NF-κB, TGF-β, HIF-1α, Wnt, MAPK, JAK-STAT, Hippo, and Notch signaling cascades) and minor pathways during the treatment of numerous diseases encountered in orthopedic surgery, neurosurgery, cardiothoracic surgery, plastic surgery, general surgery, and other specialties. We provide a novel perspective in future exosome research through bridging the gap between signaling pathways and surgical indications when designing further preclinical studies and clinical trials.

Published
2024
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33. Identification and analysis of epithelial‐mesenchymal transition‐related key long non‐coding RNAs in hypospadias

Author

Hongjie Gao, Chen Ding, Mengmeng Chang, Zhiyi Lu, Ding Li, Dan Bi, and Fengyin Sun

Subjects
data mining, diseases, Biology (General), QH301-705.5
Abstract

Abstract EMT dysfunction is a dominant mechanisms of hypospadias. Thus, identification of EMT‐related lncRNAs based on transcriptome sequencing data of hypospadias might provide novel molecular markers and therapeutic targets for hypospadias. First, the microarray data related to hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the differentially expressed lncRNAs and messenger RNAs (mRNAs) related to EMT were screened to construct lncRNA‐mRNA co‐expression interaction pairs. In addition, the microRNA (miRNA) prediction analysis was performed through bioinformatics methods to construct a ceRNA network. Moreover, function prediction and function enrichment and pathway analyses were also performed. Finally, the core EMT‐related lncRNAs were verified based on mRNA expression changes and cell functions. A total of 6 EMT‐related lncRNAs were identified and 123 mRNA‐lncRNA co‐expression interaction pairs were screened in this study. Additionally, a ceRNA regulatory network comprising 17 mRNAs, 4 lncRNAs, and 28 miRNAs was constructed based on the prediction of hypospadias‐related miRNAs. The validation results of the dataset GSE121712 revealed that only BEX1 was positively correlated with the expression of the lncRNA GNAS‐AS1 (r = 0.874, P < 0.01), both of which had high expression. The cell experiment results demonstrated that interfering with the expression of GNAS‐AS1 significantly promoted the proliferation, migration, and EMT of cells. Importantly, it was confirmed that GNAS‐AS1 can serve as a ceRNA and play an important role in the EMT of hypospadias. Hence, it may be considered as a potential target in the treatment of this disease.

Published
2024
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34. Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region

Author

Wei Hu, Kai Qin, Fan Lu, Ding Li, and Jason B. Cohen

Subjects
Methane, TROPOMI, Coal mine, Mass balance equation, Mining engineering. Metallurgy, TN1-997
Abstract

Abstract A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.

Published
2024
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35. Flight situational assessment based on complex network and MFIM-TOPSIS

Author

PENG Yating, WEN Xiangxi, WU Minggong, ZHU Deshan, and DING Li

Subjects
flight conflict network, complex network, flight situational assessment, superior-disadvantage solution distance method, multi-factor interaction matrix, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

In order to objectively and accurately assess the airborne flight posture in the trajectory-based operation (TBO) mode, a flight posture assessment method based on complex network and MFIM-TOPSIS is proposed. Firstly, the precise protection zone of different aircraft types is established by analyzing the risk of lateral collision between aircraft, and the flight conflict network is constructed by using the three-dimensional velocity barrier method, which is close to the TBO air operation environment, based on which six network topology indexes, such as total node degree, average point strength and network density, are selected to build the flight attitude assessment index system. To weaken the influence of the assessment subjectivity, an interaction matrix that can reflect subjective factors-objective factors is proposed. The maximum information coefficient is used to improve the coding of interaction matrix. Finally the flight attitude level status is assessed by using the superiority-disadvantage solution distance method. The simulation analysis and the example analysis of Takasaki Airport show that the evaluation model can accurately assess the air traffic situation and provide auxiliary information for controllers' decision making and command.

Published
2024
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36. Compressed sensing 3D T2WI radiomics model: improving diagnostic performance in muscle invasion of bladder cancer

Author

Shuo Li, Zhichang Fan, Junting Guo, Ding Li, Zeke Chen, Xiaoyue Zhang, Yongfang Wang, Yan Li, Guoqiang Yang, and Xiaochun Wang

Subjects
Magnetic resonance imaging, Compressed sensing, Bladder cancer, Muscle invasion, Radiomics, Medical technology, R855-855.5
Abstract

Abstract Background Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. Methods This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. Results In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63–0.96) and 0.77(95%CI:0.60–0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55–0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. Conclusion The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.

Published
2024
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37. Embedded monitoring system and teaching of artificial intelligence online drug component recognition

Author

Ding Li, Wu Zhengrong, Zhang Junmin, Zhao Quanyi, Chen Xiaoling, Jia Zhong, and He Dian

Subjects
drug composition identification, embedded monitoring system, drug teaching, artificial intelligence, Biology (General), QH301-705.5
Abstract

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

Published
2024
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38. DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

Author

Chen, Chao, Liu, Xinhao, Li, Yiming, Ding, Li, and Feng, Chen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our method., Comment: accepted by CVPR 2023

Published
2022

39. Unveiling the microbial diversity across the northern Ninety East Ridge in the Indian Ocean

Author

Ding Li, Liping Wang, Fan Jiang, Xiang Zeng, Qinzeng Xu, Xuelei Zhang, Qiang Zheng, and Zongze Shao

Subjects
Ninety-East Ridge, microbial diversity, 16S rRNA gene, ecological differentiation, bioindicator, Microbiology, QR1-502
Abstract

Prokaryotes play a crucial role in marine ecosystem health and drive biogeochemical processes. The northern Ninety East Ridge (NER) of the Indian Ocean, a pivotal yet understudied area for these cycles, has been the focus of our study. We employed high-throughput 16S rRNA gene sequencing to analyze 35 water samples from five stations along the ridge, categorized into three depth- and dissolved oxygen-level-based groups. Our approach uncovered a clear stratification of microbial communities, with key bioindicators such as Prochlorococcus MIT9313, Sva0996 marine group, and Candidatus Actinomarina in the upper layer; Ketobacter, Pseudophaeobacter, Nitrospina, and SAR324 clade in the middle layer; and Methylobacterium-Methylorubrum, Sphingomonas, Sphingobium, and Erythrobacter in the deep layer. Methylobacterium-Methylorubrum emerged as the most abundant bacterial genus, while Nitrosopumilaceae predominated among archaeal communities. The spatial and depth-wise distribution patterns revealed that Ketobacter was unique to the northern NER, whereas Methylobacterium-Methylorubrum, UBA10353, SAR324 clade, SAR406, Sva0996_marine_group, Candidatus Actinomarina were ubiquitous across various marine regions, exhibiting niche differentiation at the OTU level. Environmental factors, especially dissolved oxygen (DO), silicate, nitrate, and salinity, significantly influence community structure. These findings not only reveal the novelty and adaptability of the microbial ecosystem in the northern NER but also contribute to the broader understanding of marine microbial diversity and its response to environmental heterogeneity.

Published
2024
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40. Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI

Author

Ding Li, Yan Liu, and Jun Huang

Subjects
explainable AI, graph learning, software code vulnerability, feature representation, Computer engineering. Computer hardware, TK7885-7895
Abstract

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.

Published
2024
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41. Group Recommendation Model Based on User Common Intention and Social Interaction

Author

QIAN Zhongsheng, ZHANG Ding, LI Duanming, WANG Yahui, YAO Changsen, YU Qingyuan

Subjects
group recommendation, user common intention, social interaction, graph neural network, Electronic computers. Computer science, QA75.5-76.95
Abstract

Existing group recommendation models often have a monotonous approach when solving user representation, and only simple social relationships between users are utilized. This makes user representation inaccurate and most models do not consider the impact of user common intention and social interaction on group preferences. As a result, recommended items are not aligned with user needs. To address these issues, a new group recommendation model based on user common intention and social interaction (GR-UCISI) is proposed. Firstly, a user intention separation model that combines user-item interaction history with social interaction is constructed. Graph neural networks are utilized to collect user-item interaction and social interaction information, and to solve user intention and item representation. Secondly, by utilizing the social network random walk algorithm and the [K-means] clustering algorithm, users can be grouped. User group, user intention and group intention aggregation process are combined to obtain group common intention representation. Finally, group common intention representation and item representation are calculated to obtain the list of recommended items for the group. This method fully considers the impact of user individuality and commonality among group members on group preferences. It also utilizes social relationships to alleviate the problem of data sparsity and improve model performance. The experimental results show that compared with the model with the best recommendation effect of nine models, on the Gowalla dataset, the Precision and NDCG of the GR-UCISI model are increased by 3.01% and 5.26% respectively, on the Yelp-2018 dataset, the Precision and NDCG of the GR-UCISI model are increased by 2.96% and 1.12% respectively.

Published
2024
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42. Identification and analysis of MSC-Exo-derived LncRNAs related to the regulation of EMT in hypospadias

Author

Mengmeng Chang, Hongjie Gao, Yingying Li, Chen Ding, Zhiyi Lu, Ding Li, Fan Huang, Jiawei Chen, and Fengyin Sun

Subjects
Hypospadias, Bioinformatics analysis, EMT, MSC-Exos, HCG18, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Objective This study aims to screen the differentially expressed long non-coding RNAs (DELncRNAs) related to the regulation of epithelial-mesenchymal transition (EMT) in hypospadias in mesenchymal stem cell-derived exosomes (MSC-Exons) and explore the potential mechanism of these lncRNAs for the EMT in hypospadias. Methods In this study, the microarray data related to MSC-Exos and hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the lncRNAs highly expressed in MSC-Exos and the differentially expressed mRNAs and lncRNAs in children with hypospadias were screened, respectively. In addition, the lncRNAs enriched in MSC-Exos and differentially expressed lncRNAs in hypospadias were intersected to obtain the final DElncRNAs. Moreover, the co-expression interaction pairs of differentially expressed lncRNAs and mRNAs were analyzed to construct a Competing Endogenous RNA (ceRNA) network. Finally, the candidate lncRNAs in exosomes were subjected to in vitro cell function verification. Results In this study, a total of 4 lncRNAs were obtained from the microarray data analysis. Further, a ceRNA regulatory network of MSC-Exo-derived lncRNAs related to the regulation of EMT in hypospadias was constructed, including 4 lncRNAs, 2 mRNAs, and 6 miRNAs. The cell function verification results indicated that the exosomes secreted by MSCs may transport HLA complex group 18 (HCG18) into target cells, which promoted the proliferation, migration, and EMT of these cells. Conclusion MSC-Exo-derived lncRNA HCG18 can enter target cells, and it may be involved in the regulation of EMT in hypospadias through the ceRNA network.

Published
2024
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43. Fringing Field Effect of Orbitrap and Its Compensation Method

Author

WANG Jun-tian1, REN Yi2, HUANG Zheng-xu1,3, CHEN Zheng-ge1, DING Li

Subjects
orbitrap / fringing field / compensator electrode / resolution / simulation, Chemistry, QD1-999
Abstract

Since it was introduced, the Orbitrap has been proved to be a powerful ion analyzer whose resolution is affected by the fringing field in the electrode structure. Orbitrap is an ultra-high precision instrument. The gap between two outer half electrodes and the design of ion perforation on one side of the outer electrode will produce fringing field effect, resulting in the distortion of the ideal electrostatic field. The distorted field formed in the internal space of Orbitrap will have a serious impact on the resolution. Thermo Fisher Scientific is the only company that has a patent on Orbitrap mass spectrometers and related technologies, however, it has not provided relevant materials to explain the problem in detail and specific solutions, and few researchers have conducted in-depth research. Therefore, it is necessary to study the influence of the distortion field caused by the ion perforation and the outer electrode gap on the resolution, and propose a solution to the fringing field effect. In this work, ion optical simulation software (SIMION) was used to establish ideal, distorted and corrected models, in order to measure the limiting resolution of the orbital trap by measuring the axial vibration period dispersion corresponding to different initial axial amplitudes and different orbital radius. By comparing the experimental and theoretical results of the ideal model, the reliability of the selection of simulation parameters and the calculation method of ion motion were verified. In the distortion model experiment, the ion axial motion period dispersion caused by the fringing field effect arising from the outer electrode gap and ion perforation and its influence on the resolution was investigated. In the correction model experiment, it was proved that the distortion field can be corrected by adjusting the voltage of the two compensating electrodes, so as to overcome the fringing field effect caused by the ion perforation and the outer electrode gap. In addition, it was also found that the axial flight period error caused by radial dispersion in the corrected model is smaller than that in the ideal model, and the difference between the two is small in the axial dispersion. The comprehensive result of the corrected model is better than that of the ideal model. Therefore, the error caused by scratches on the surface of the electrodes can be corrected by adjusting the voltage of the compensating electrodes. The final simulation results showed that the limiting mass resolution for m/z 100 ion can reach to 2 500 000.

Published
2024
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44. Nephroblastoma-specific dysregulated gene SNHG15 with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

Author

Mengmeng Chang, Ding Li, Li Su, Chen Ding, Zhiyi Lu, Hongjie Gao, and Fengyin Sun

Subjects
ScRNA-Seq, Bulk RNA-Seq, Prognostic, Nephroblastoma, M2 macrophages, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Published
2024
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45. Dietary copper requirement of broilers fed a corn-soybean meal diet during 22–42 d of age

Author

Ling Zhu, Wei Wu, Bingxin Wu, Yun Hu, Liyang Zhang, Weiyun Zhang, Tingting Li, Xiaoyan Cui, Feiyu Gao, Ding Li, Xugang Luo, and Shengchen Wang

Subjects
Copper, Copper content, Gene expression of copper-containing enzyme, Requirement, Broiler, Animal culture, SF1-1100
Abstract

This research was to assess the dietary copper (Cu) requirement of broiler chickens fed a practical corn-soybean meal diet during 22–42 d of age. A total of 288 numbered Arbor Acres male broilers at 22 d of age were randomly allotted 6 treatments with 8 replicate cages (6 broilers per cage) per treatment. Broilers were fed a Cu-unsupplemented corn-soybean meal basal diet (control, containing 7.36 mg Cu/kg) or the basal diet added with 3, 6, 9, 12, or 15 mg Cu/kg from CuSO4·5H2O for 21 d. Quadratic, asymptotic and broken-line models were fitted and the best fitted models were selected to determine dietary Cu requirements. The results revealed that the contents of Cu in serum and liver, mRNA expression levels of Cu- and zinc-containing superoxide dismutase (CuZnSOD) in liver and monoamine oxidase b (MAO B) in heart, as well as protein expression level of CuZnSOD in liver were affected (P

Published
2024
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46. Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits

Author

Ding, Li and Spector, Lee

Subjects
Computer Science - Neural and Evolutionary Computing, Quantum Physics
Abstract

Recent advancements in quantum computing have shown promising computational advantages in many problem areas. As one of those areas with increasing attention, hybrid quantum-classical machine learning systems have demonstrated the capability to solve various data-driven learning tasks. Recent works show that parameterized quantum circuits (PQCs) can be used to solve challenging reinforcement learning (RL) tasks with provable learning advantages. While existing works yield potentials of PQC-based methods, the design choices of PQC architectures and their influences on the learning tasks are generally underexplored. In this work, we introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models, which uses a population-based genetic algorithm to evolve PQC architectures by exploring the search space of quantum operations. Experimental results show that our method can significantly improve the performance of hybrid quantum-classical models in solving benchmark reinforcement problems. We also model the probability distributions of quantum operations in top-performing architectures to identify essential design choices that are critical to the performance.

Published
2022
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47. Lexicase Selection at Scale

Author

Ding, Li, Boldi, Ryan, Helmuth, Thomas, and Spector, Lee

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence
Abstract

Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.

Published
2022
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48. Self-Supervised Visual Place Recognition by Mining Temporal and Feature Neighborhoods

Author

Chen, Chao, Liu, Xinhao, Xu, Xuchu, Li, Yiming, Ding, Li, Wang, Ruoyu, and Feng, Chen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Visual place recognition (VPR) using deep networks has achieved state-of-the-art performance. However, most of them require a training set with ground truth sensor poses to obtain positive and negative samples of each observation's spatial neighborhood for supervised learning. When such information is unavailable, temporal neighborhoods from a sequentially collected data stream could be exploited for self-supervised training, although we find its performance suboptimal. Inspired by noisy label learning, we propose a novel self-supervised framework named \textit{TF-VPR} that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods. Our method follows an iterative training paradigm which alternates between: (1) representation learning with data augmentation, (2) positive set expansion to include the current feature space neighbors, and (3) positive set contraction via geometric verification. We conduct comprehensive experiments on both simulated and real datasets, with either RGB images or point clouds as inputs. The results show that our method outperforms our baselines in recall rate, robustness, and heading diversity, a novel metric we propose for VPR. Our code and datasets can be found at https://ai4ce.github.io/TF-VPR/.

Published
2022

49. Atomically Sharp, Closed Bilayer Phosphorene Edges by Self-Passivation

Author

Lee, Sol, Lee, Yangjin, Ding, Li Ping, Lee, Kihyun, Ding, Feng, and Kim, Kwanpyo

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

Two-dimensional (2D) crystals' edge structures not only influence their overall properties but also dictate their formation due to edge-mediated synthesis and etching processes. Edges must be carefully examined because they often display complex, unexpected features at the atomic scale, such as reconstruction, functionalization, and uncontrolled contamination. Here, we examine atomic-scale edge structures and uncover reconstruction behavior in bilayer phosphorene. We use in situ transmission electron microscopy (TEM) of phosphorene/graphene specimens at elevated temperatures to minimize surface contamination and reduce e-beam damage, allowing us to observe intrinsic edge configurations. Bilayer zigzag (ZZ) edge was found the most stable edge configuration under e-beam irradiation. Through first-principles calculations and TEM image analysis under various tilting and defocus conditions, we find that bilayer ZZ edges undergo edge reconstruction and so acquire closed, self-passivated edge configurations. The extremely low formation energy of the closed bilayer ZZ edge and its high stability against e-beam irradiation are confirmed by first-principles calculations. Moreover, we fabricate bilayer phosphorene nanoribbons with atomically-sharp closed ZZ edges. The identified bilayer ZZ edges will aid in the fundamental understanding of the synthesis, degradation, reconstruction, and applications of phosphorene and related structures., Comment: 22 pages, 5 figures

Published
2022

50. Halftoning with Multi-Agent Deep Reinforcement Learning

Author

Jiang, Haitian, Xiong, Dongliang, Jiang, Xiaowen, Yin, Aiguo, Ding, Li, and Huang, Kai

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Deep neural networks have recently succeeded in digital halftoning using vanilla convolutional layers with high parallelism. However, existing deep methods fail to generate halftones with a satisfying blue-noise property and require complex training schemes. In this paper, we propose a halftoning method based on multi-agent deep reinforcement learning, called HALFTONERS, which learns a shared policy to generate high-quality halftone images. Specifically, we view the decision of each binary pixel value as an action of a virtual agent, whose policy is trained by a low-variance policy gradient. Moreover, the blue-noise property is achieved by a novel anisotropy suppressing loss function. Experiments show that our halftoning method produces high-quality halftones while staying relatively fast., Comment: ICIP 2022

Published
2022
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