Iterative Optimization Scheme Research Articles

Mixture-Based Screening of Focused Combinatorial Libraries by NMR: Application to the Antiapoptotic Protein hMcl-1.

We report on an innovative ligand discovery strategy based on protein NMR-based screening of a combinatorial library of ∼125,000 compounds that was arranged in 96 distinct mixtures. Using sensitive solution protein NMR spectroscopy and chemical perturbation-based screening followed by an iterative synthesis, deconvolutions, and optimization strategy, we demonstrate that the approach could be useful in the identification of initial binding molecules for difficult drug targets, such as those involved in protein-protein interactions. As an application, we will report novel agents targeting the Bcl-2 family protein hMcl-1. The approach is of general applicability and could be deployed as an effective screening strategy for de novo identification of ligands, particularly when tackling targets involved in protein-protein interactions.

Multi-view clustering via label-embedded regularized NMF with dual-graph constraints

Nonnegative matrix factorization (NMF) methods have achieved remarkable performances in multi-view clustering due to their effectiveness and efficiency. To better obtain a low-dimensional common representation, the limited labels and the geometric structure of the multi-view data should be fully utilized in clustering. In this work, we introduce a novel multi-view learning approach, dubbed label-embedded regularized NMF with dual-graph constraints (LeNMF-DC), for clustering. Our proposed LeNMF-DC approach mainly utilizes matrix factorization to obtain a low-dimensional common representation of the multi-view data, in which the prior knowledge hidden in data can be fully explored. Specifically, we construct three graph regularization terms to preserve the manifold structure in the data, feature and label space, respectively. Moreover, we take advantage of the labels of the labeled samples without additional parameters. In addition, we develop an alternate iterative optimization scheme to solve the model of LeNMF-DC and then show its convergence rate. Compared with traditional multi-view clustering approaches, the labels of unlabeled samples in our proposed LeNMF-DC approach are assigned by the label constraint matrix rather than the clustering algorithm, and thus it avoids performance loss during the clustering. Experimental results on four benchmark datasets manifest that our LeNMF-DC approach can achieve superior performances than several state-of-the-art approaches in multi-view clustering.

Tensor tree decomposition as a rank‐reduction method for pre‐stack interpolation

AbstractTensors have been proposed to represent pre‐stack seismic data, particularly for seismic data denoising and reconstruction. They naturally permit describing multi‐dimensional seismic signals that depend on time (or frequency) and source and receiver coordinates. A tensor representation aims to preserve the information embedded in the multi‐linear array in a reduced space. Such a representation is part of many algorithms for seismic data reconstruction via tensor completion methodologies. We investigate and apply one particular tensor tree representation to seismic data reconstruction. The Tensor Tree decomposition methodology permits decomposing a high‐order tensor into third‐order tensors. The technique relies on the truncated singular value decomposition, which branches the tensors into low‐dimensional tensors. As a benefit, the tensor tree allows us to reorganize the tensor into a matrix that demands the least singular values to have an optimal low‐rank approximation. We have developed an algorithm that uses the tensor tree for data reconstruction in an iterative optimization scheme and directly compared it to the parallel matrix factorization. At first, we demonstrated the proposed methodology results with five‐dimensional synthetic shot data and then moved forward with five‐dimensional field data, where we analysed it both pre and post‐stack. The tensor tree performs well in reconstructing both synthetic and field data with high fidelity, at the same level as the well‐established parallel matrix factorization.

Joint Latent Space and Label Inference Estimation with Adaptive Fused Data and Label Graphs

Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this article, we present a new framework for graph-based semi-supervised learning. We present a powerful method for simultaneous label inference and linear transform estimation. The targeted linear transformation is used to obtain a discriminant subspace. To improve semi-supervised learning, our framework focuses on exploiting the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the label increases the supervision information indirectly through an introduced informative matrix called the label graph, thus avoiding the use of hard confidence-based decisions as used in self-supervised methods. In addition, the estimation of labels and projected data is made more robust by using smoothing concepts based on hybrid graphs. For each type of smoothing, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data and the similarity of the labels. The proposed method leads to an improved discriminative linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. They also show superior performance compared to semi-supervised methods that use integration and label inference simultaneously.

Ancillary Services in Targeted Advertising: From Prediction to Prescription

Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the net present value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy—that is, scalable and efficient—to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%–3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift.History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0491 .

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

An iterative optimization scheme to accommodate inequality constraints in air quality geostatistical estimation of multivariate PM

The kriging-based estimation of the different types of atmospheric particulate matter (PM) pollutions defined in the air quality regulation raises some operational problems because the (co)kriging equations are obtained by minimizing a linear combination of the estimation variances subject to unbiasedness constraints. As a consequence, the estimation process can result in total PM10 concentrations that are less than the PM2.5 concentrations which would be physically impossible. In a previous publication, it was shown that a convenient external drift modeling can reduce the number of spatial locations where the inequality constraint is not satisfied, without completely solving the problem. In this work, the formulation of the cokriging system is modified, inspired by previous works focusing on positive kriging. The introduction of additional constraints on the cokriging weights are presented, leading to a unique and optimal solution to the problem of cokriging under inequality constraints between two variables. Some computational and algorithmic details are introduced. An evaluation of the penalized cokriging is provided by using the European PM monitoring sites dataset: some maps and performance scores are given to assess the relevance of our iterative optimization scheme.

CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.

A Performance Analysis of Soil Dielectric Models over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM <15%) and organic soils (SOM ≥15%) would be the preferred option.

Learning Swarm Interaction Dynamics From Density Evolution

In this article, we consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker–Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of partial differential equations. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.

Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

Tensor Principal Component Analysis in High Dimensional CP Models

The CP decomposition for high dimensional non-orthogonal spiked tensors is an important problem with broad applications across many disciplines. However, previous works with theoretical guarantee typically assume restrictive incoherence conditions on the basis vectors for the CP components. In this paper, we propose new computationally efficient composite PCA and concurrent orthogonalization algorithms for tensor CP decomposition with theoretical guarantees under mild incoherence conditions. The composite PCA applies the principal component or singular value decompositions twice, first to a matrix unfolding of the tensor data to obtain singular vectors and then to the matrix folding of the singular vectors obtained in the first step. It can be used as an initialization for any iterative optimization schemes for the tensor CP decomposition. The concurrent orthogonalization algorithm iteratively estimates the basis vector in each mode of the tensor by simultaneously applying projections to the orthogonal complements of the spaces generated by other CP components in other modes. It is designed to improve the alternating least squares estimator and other forms of the high order orthogonal iteration for tensors with low or moderately high CP ranks, and it is guaranteed to have second or higher order convergence when the error of any given initial estimator is bounded by a small constant. Our theoretical investigation provides estimation accuracy and convergence rates for the two proposed algorithms. Both proposed algorithms are applicable to deterministic tensor, its noisy version, and the order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2K$ </tex-math></inline-formula> covariance tensor of order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> tensor data in a factor model with uncorrelated factors. Simulation experiments demonstrate significant practical superiority of our approach over existing methods.

A comparison of methods to estimate leaf area index using either crop-specific or generic proximal hyperspectral datasets

The leaf area index (LAI) is an important parameter indicating the crop growth status. Many vegetation indices and models have been developed to estimate LAI of different crops. However, the utility of and differences between crop-specific and generic algorithms for LAI estimation covering several crops, dates, and sites need to be compared. The main objectives of this study were to: (1) evaluate the suitability and robustness of different vegetation indices for LAI estimation of different crops; (2) compare the performance of crop-specific and generic algorithms; (3) evaluate the performance of statistical regression, machine learning regression, and the PROSAIL-D with different inversion strategies. Results showed that: (1) the simple ratio index (SR) performed best for cotton and winter wheat, the MERIS terrestrial chlorophyll index (MTCI) performed best for maize; (2) the models trained over specific crop types performed better than those trained over all crop types together; (3) for crop-specific models, artificial neural networks (ANN) performed better than support vector machine regression (SVR), partial least square regression (PLSR), and statistical regression methods, while the PROSAIL-D yielded similar or slightly better performance as compared with ANN method. Furthermore, the look-up table (LUT) strategy performed better than iterative optimization strategy (Shuffled Complex Evolution method developed at the University of Arizona, SCE-UA). Results indicated that there was not an optimal model that could be used to precisely estimate LAI of different crops since the relationship between the specific hyperspectral reflectance and the LAI of different crops were different. In addition, the PROSAIL-D with crop specific input parameters is a potential method for LAI estimation of different crops with hyperspectral reflectance.

The Balanced Window-Based Load Event Optimal Matching for NILM

Load event matching is the key for event-based non-intrusive load monitoring (NILM). It aims to find the load event sequence corresponding to the appliance’s operation cycle from all detected load events. Firstly, a novel combinatorial optimization model for load event matching is established. To reduce the dimension of the estimation matrix in the proposed optimization model, the concept of balanced window (BW) is introduced to indicate a period of load operation within which events can be fully matched. Then an iterative load event optimal matching strategy based on nested BWs is proposed, where most inner BWs are processed firstly. For the event matching in one specific BW, a two-stage optimization algorithm is designed. In the first stage, a set of candidate load event sequences is generated by using the depth first search algorithm with pruning, which reduces the solution space of the combinatorial optimization problem. In the second stage, the optimal load event sequences that result in the optimal fit of the aggregated load power is selected. Comparative experiments on the publicly-accessible REDD datasets have shown that the proposed method achieves better performance against the existing unsupervised NILM methods, and is promising for practical implementation.

Optimized Dynamic Motion Performance for a 5-DoF Electromagnetic Manipulation

High precision manipulation and optimization of actuation current are major concerns in the design of magnetic manipulation systems. In this paper, a flexible magnetic field mapping model has been employed to determine the field distribution of a compact electromagnetic manipulation system in 3D space, and validated by a high-resolution magnetic calibration system and Finite Element Analysis, which enables an improved accuracy and performance of magnetic manipulation. In addition, an iterative current optimization scheme is used to optimize the actuation current through Iterated Tikhonov Regularization. Finally, a feedback control algorithm including a Proportional-Integral-Derivative controller and Extended State Observer is conducted to solve general external disturbance during the autonomous manipulation of magnetic objects. By incorporating magnetic field mapping, feedback control, and current optimization model, the accuracy of the system has been demonstrated by navigating a small-size permanent magnet through predefined trajectories (Z-shaped, spiral curve) with the average position error of 0.27 mm (body length of 1 mm) and maximum average velocity of 3.59 mm/s. Meanwhile, the results showed that the required maximum actuation current was reduced from 4.00 A to 1.81 A. Our work provides a reliable and systematic approach for high-precision magnetic manipulation and optimization of actuation currents.

GPyro: uncertainty-aware temperature predictions for additive manufacturing

In additive manufacturing, process-induced temperature profiles are directly linked to part properties, and their prediction is crucial for achieving high-quality products. Temperature predictions require an accurate process model, which is usually either a physics-based or a data-driven simulator. Although many high-performance models have been developed, they all suffer from disadvantages such as long execution times, the need for large datasets, and error accumulation in long prediction horizons. These caveats undermine the utility of such modeling approaches and pose problems in their integration within iterative optimization and closed-loop control schemes. In this work, we introduce GPyro, a generative model family specifically designed to address these issues and enable fast probabilistic temperature predictions. GPyro combines physics-informed and parametric regressors with a set of smooth attention mechanisms and learns the evolution of the dynamics inherent to a system by employing Gaussian processes. The model predictions are equipped with confidence intervals quantifying the uncertainty at each timestep. We applied GPyro to Wire-arc additive manufacturing and learned an accurate model from a single experiment on a real welding cell, almost in real-time. Our model can be easily integrated within existing loop-shaping and optimization frameworks.

OW-TAL: Learning Unknown Human Activities for Open-World Temporal Action Localization

Current temporal action localization methods work well on a closed-world assumption, in which all action categories to be localized are known as a priori. However, this assumption doesn’t apply to open-world scenarios, as novel categories that never appeared in the training stage will be encountered without explicit supervision. Distinct from the closed-world setting, localizing actions under the open-world setup poses two significant challenges: 1) identifying unknown actions from diverse knowns and localizing their temporal boundaries. 2) defying forgetting of previous actions when incrementally updating knowledge of identified unknown actions. To address the aforementioned challenges, we develop a two-branch framework with Unknown and Known action modeling Networks, a.k.a. UK-Net, for the problem of Open-World Temporal Action Localization (OW-TAL). The potential patterns underlying unknown and known actions, as well as their dynamic transformation, are modeled in a unified pipeline. Specifically, a self-attention based position-sensitive module is designed to produce actionness scores for unknown actions in a class-agnostic way. Besides, an iterative optimization strategy is developed to enable knowledge derived from known categories to be shared with the unknowns. In addition, a self-paced learning strategy is proposed to instructionally guide class-incremental learning while defying catastrophic forgetting. Benefiting from the above components, our UK-Net yields superior performance on three challenging datasets, i.e., THUMOS14, ActivityNet1.2, and MUSES. Experimental results also demonstrate the competitive performance of our method when compared with traditional closed-world counterparts.

Adaptive safety-aware semi-supervised clustering

Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine learning field. S3C aims to reduce the performance degradation probability of wrong prior knowledge. The existing S3C methods tries to artificially estimate the risk or safety degrees through a predefined formula. The performance of S3C heavily depends on the quality of the formula. To alleviate the influence of the artificially defined formula, it is meaningful to develop an novel S3C version which can adaptively estimate the safety degrees. To achieve the goal, we propose an adaptive safety-aware semi-supervised fuzzy c-means algorithm (AS3FCM) which is used to deal with mislabeled instances. In order to adaptively estimate the safety degrees, AS3FCM employs a local consistency strategy to build a regularization term. Meanwhile, two regularization terms are constructed to constrain the outputs of the labeled instances. The constructed regularization terms are then embedded into the objective function of FCM. Finally, an alternating iterative optimization strategy is utilized to solve the optimization problem. In each iteration, the safety degrees can be adaptively computed and updated. To evaluate the performance of our algorithm, some experiments are conducted on toy and several benchmark datasets. As the mislabeled percentages change, AS3FCM can obtain the clustering accuracy from 81.23% to 82.87% on Heart and 86.84% to 88.12% on WDBC while the other S3C methods yield the accuracy from 80.09% to 82.41% on Heart and 86.34% to 87.94% on WDBC. It verifies that AS3FCM can reasonably estimate the safety degrees and achieve a safe exploration of the mislabeled instances.

Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions.

A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices.

Strict Twice Iterative Optimization Strategy to Synthesize Ultrabright Fluorescent Carbon Dots for UV and pH Dual-Encryption Fluorescent Ink.

In this work, ultrabright fluorescent carbon dots (U-CDs) were synthesized by using a strict twice iterative optimization strategy. Their relative photoluminescence (PL) quantum yield is close to 100%, exceeding most of the reported fluorescent CDs and greatly boosting the practical applications of fluorescent CDs in many fields. Then serving as fluorescent anti-counterfeiting ink was taken as an example to briefly introduce the application of the U-CDs. The PL emission of the U-CDs is quenched at the range of pH < 4 or pH > 11 and restored at the range of pH = 5–10. This pH-sensitive PL feature allows the U-CDs to be used as fluorescent ink for pH and UV dual information encryption. The written or printed information is invisible under daylight but visible under UV light. After acid treatment or alkali treatment, the information is invisible even under a UV lamp but visible after neutralization treatment. This work provides a standardized scheme for optimizing the synthesis conditions of fluorescent CDs and paves the way for large-scale production of high-performance fluorescent CDs.