Random Initialization Research Articles

C2Fi-NeRF: Coarse to fine inversion NeRF for 6D pose estimation

Estimating the 6DoF pose of objects in complex scenarios is one of the core challenges in environmental perception for unmanned systems. Recently, mesh-free pose estimation methods based on “inverse” NeRF have achieved state-of-the-art (SOTA) accuracy under ideal data conditions compared to traditional methods. However, the overall performance of this strategy is suboptimal due to some overlooked details, such as NeRF’s sampling of pixel backpropagation, which can lead to local minima in high-resolution images. Random initialization of poses increases the difficulty of network convergence and estimation bias. Pose estimation neglects geometric consistency constraints, resulting in low robustness to occluded environments. To address these issues, this paper proposes a “coarse-to-fine” NeRF pose prediction framework (C2Fi-NeRF). During the training phase, an affinity-based full-pixel backpropagation strategy is proposed, abandoning the sparse sampling of traditional NeRF training. The complete gradient map is divided into affinity blocks, which are rendered and backpropagated in sequence. This not only achieves efficient full-pixel training for high-resolution images but also significantly improves the quality and consistency of rendered images, reducing noise and artifacts. The prediction phase is divided into two parts: the coarse phase optimizes reprojection errors through feature point matching to introduce precise initialization data, accelerating NeRF convergence and reducing bias potential. The fine estimation phase integrates multi-view geometry and color consistency constraints in the inverse NeRF iteration, enhancing pixel rendering robustness in complex occluded scenarios while also refining pose prediction. Experiments demonstrate that compared to existing NeRF-based and mainstream deep learning methods, C2Fi-NeRF is competitive in accuracy and efficiency on relevant datasets (NeRF-Synthetic, LLFF, Replica, YCB) and is more suitable for practical robotic applications.

An Empirical Exploration of the Effectiveness of Word Embedding Techniques for Emotion Recognition

An Empirical Exploration of the Effectiveness of Word Embedding Techniques for Emotion Recognition

Prediction of SO2 concentration at desulfurization outlet of thermal power units based on reliefF-SC and ISAO-ARELM

Abstract The flue gas desulfurization (FGD) system of thermal power units operates under complex conditions and exhibits significant nonlinearity. Establishing an accurate prediction model for outlet SO2 concentration is crucial for optimizing the control of the FGD system. The study constructs an autoregressive limit learning machine (ARELM) prediction model for SO2 concentration at the desulfurization outlet of thermal power units, leveraging the improved feature selection algorithm ReliefF-SC and the improved snow ablation optimizer (ISAO). Initially, the time delays of the input variables are compensated and the ReliefF-SC algorithm, which incorporates the Spearman correlation coefficient and cosine similarity, is designed to obtain the optimal feature set for predicting SO2 concentration at the outlet. To enhance the ELM's capacity to process time-series data, the autoregressive (AR) concept is integrated into the ELM framework. Furthermore, to mitigate the impact of random initialization of ARELM network parameters on model stability, the ISAO algorithm is proposed by introducing the sine-cosine position update strategy and adaptive adjustment of subpopulation size. Finally, experimental validation is conducted using actual plant operation data. The results indicate that the established SO2 concentration prediction model for desulfurization outlets of thermal power units is highly accurate and offers valuable theoretical guidance and technical support for optimizing the control of the FGD systems.

On the strong stability of ergodic iterations

Abstract We revisit processes generated by iterated random functions driven by a stationary and ergodic sequence. Such a process is called strongly stable if a random initialization exists for which the process is stationary and ergodic, and for any other initialization the difference of the two processes converges to zero almost surely. Under some mild conditions on the corresponding recursive map, without any condition on the driving sequence we show the strong stability of iterations. Several applications are surveyed such as generalized autoregression and queuing. Furthermore, new results are deduced for Langevin-type iterations with dependent noise and for multitype branching processes.

Multi-Step Ahead Water Level Forecasting Using Deep Neural Networks

Stream gauge height (water level) is a significant indicator for forecasting future floods. Flooding occurs when the water level exceeds the flood stage. Predicting imminent floods can save lives, protect infrastructure, and improve road traffic management and transportation. Deep neural networks have been increasingly used in this domain due to their predictive capabilities in capturing complex features and interdependencies. This study employs four distinct models—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), transformer, and LSTNet—with MLP serving as the baseline model to forecast water levels. The models are trained using data from 20 distinct river gages across the state of Missouri to ensure consistent performance. Random search optimization is employed for hyperparameter tuning. The prediction intervals are set at 4, 6, 8, and 10 (each interval equivalent to 30 min) to ensure that performance results are robust and not due to random weight initialization or suboptimal hyperparameters and are consistent throughout different prediction intervals. The findings of this study indicate that the LSTNet model leads to a better performance than the other models, with a median RMSE of 0.00724, 0.00959, 0.01204, and 0.01230 for the 4, 6, 8, and 10 intervals, respectively. As climate change leads to localized storms driven by atmospheric shifts, water level fluctuations are becoming increasingly extreme, further exacerbating data drift in real-world datasets. The LSTNet model demonstrates superior performance in terms of RMSE, MAE, and the correlation coefficient across all prediction intervals when forecasting water levels under data drift conditions.

Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model.

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

Multi-Search Strategy-based Improved Water Flow Optimizer Algorithm for Cluster Analysis

Clustering is a popular technique that has proven its capability in diverse fields like data analytics, business intelligence, social mining, image recognition, document clustering and bioinformatics. This technique determines the valuable information from a pre-defined set of data and groups similar information into the same cluster. In literature, many algorithms have been presented based on several clustering approaches. It is observed that partitional clustering algorithms are widely popular due to there simplicity and easy implementation such as k-means, k-medoids, and k-harmonic means. However, traditional algorithms suffer from several limitations like being trapped in local optima and depending on the initial solution quality. Heuristic algorithms are also proposed to alleviate the problems of traditional clustering algorithms. But, sometimes, these algorithms also get stuck in local minima and exhibit a lack of balance between search mechanisms. Hence, this work presents an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. In the proposed IWFO algorithm, the initial solution is generated based on the logistic chaotic map instead of random initialization, and in turn, an optimal quality solution is generated. The search mechanism of the WFO algorithm is enhanced based on an improved search mechanism which is the combination of non-linear functions and the previous best solution. Further, the local optima issue is alleviated using a multi-start search mechanism. The efficacy of the proposed IWFO algorithm is evaluated using twelve benchmark clustering datasets and results are compared with seventeen clustering algorithms. The simulation results are assessed using intra-cluster distance (intra), standard deviation (SD), rank, accuracy rate (AR) and detection rate (DR) parameters. Further, a statistical test is also conducted to validate the efficacy of the proposed IWFO algorithm. The results showed that proposed IWFO algorithms obtain superior quality results on most of the datasets.

Dynamics of finite width Kernel and prediction fluctuations in mean field neural networks*

Abstract We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Starting from a dynamical mean field theory description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the O ( 1 / width ) fluctuations of the dynamical mean field theory order parameters over random initializations of the network weights. Our results, while perturbative in width, unlike prior analyses, are non-perturbative in the strength of feature learning. We find that once the mean field/µP parameterization is adopted, the leading finite size effect on the dynamics is to introduce initialization variance in the predictions and feature kernels of the networks. In the lazy limit of network training, all kernels are random but static in time and the prediction variance has a universal form. However, in the rich, feature learning regime, the fluctuations of the kernels and predictions are dynamically coupled with a variance that can be computed self-consistently. In two layer networks, we show how feature learning can dynamically reduce the variance of the final tangent kernel and final network predictions. We also show how initialization variance can slow down online learning in wide but finite networks. In deeper networks, kernel variance can dramatically accumulate through subsequent layers at large feature learning strengths, but feature learning continues to improve the signal-to-noise ratio of the feature kernels. In discrete time, we demonstrate that large learning rate phenomena such as edge of stability effects can be well captured by infinite width dynamics and that initialization variance can decrease dynamically. For convolutional neural networks trained on CIFAR-10, we empirically find significant corrections to both the bias and variance of network dynamics due to finite width.

Sustainable optimization of balancing valve settings in urban heating systems with an enhanced Jaya algorithm

With the continuous growth of global energy demand, energy conservation in heating has become a research hotspot for urban sustainable development. However, the research on energy conservation by adjusting the balance valve in the secondary network of heating system is relatively few. To address this gap, this paper solves the balancing valve opening adjustment (BVOA) problem with the goal of minimizing heat consumption and the energy usage of circulation pumps. A mixed-integer linear programming model is designed for this problem, and an effective EJaya algorithm is proposed. Initially, a random initialization method with three scrambling strategies is employed to generate high-quality solutions. Moreover, a segmented operator is proposed to enhance the algorithm’s search efficiency. Subsequently, Q-learning is utilized to select from three neighborhood operators, enhancing the algorithm’s local search capabilities. Additionally, an adaptive search balance strategy is designed to balance the methods of updating solutions, aiding in finding better solutions. Finally, extensive experiments have validated the effectiveness and efficiency of the proposed algorithm in solving the BVOA problem, contributing to the promotion of urban energy sustainability and social welfare.

Three-dimensional cooperative guidance with impact angle constraints via value-policy decomposed multi-agent reinforcement learning

For a three-dimensional impact angle-constrained time-coordination guidance problem, a value-policy decomposed multi-agent twin delayed deep deterministic (VPD-MATD3) policy gradient algorithm is proposed in this paper, and a temporal-spatial cooperative guidance law is designed consequently. The derived cooperative engagement kinematics is formulated as a value-policy decomposed partially observable Markov decision process. Value decomposition and policy decomposition are correspondingly proposed to address the two bottlenecks of reward coupling and policy coupling in 3D guidance, thus improving the training process. The training environment with multiple uncertainties, such as observation noise, maneuverability perturbation, autopilot time lag, switching communication topology, and random initialization, is subsequently constructed. Time-energy type reward functions are designed, incorporating energy consumption as a direct optimization indicator via a penalty term. A long short-term memory network layer is inserted inside the policy networks to suppress the negative effect of observation noise. The policy training curves verify the significant effect of the proposed algorithm in improving training convergence. The policy testing results illustrate the robustness and generalization of the VPD-MATD3 cooperative guidance law and its ability to cope with randomly switched communication topologies. Moreover, the comparative testing further reveals its time-energy suboptimal property.

LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype’s approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm’s concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis.

Effect of different weight initialization strategies on transfer learning for plant disease detection

AbstractThe weight initialization technique for transfer learning refers to the practice of using pretrained models that can be modified to solve new problems, instead of starting the training process from scratch. In this study, six different transfer learning weight initialization strategies were proposed for plant disease detection: scratch (i.e., random initialization), pretrained model on cross‐domain (ImageNet), model trained on related domain (ISIC 2019), model trained on related domain (ISIC 2019) with cross‐domain (ImageNet) weights, model trained on same domain (PlantVillage), and model trained on same domain (PlantVillage) with cross‐domain weights (ImageNet). Weights from each strategy were transferred to a target dataset (Plant Pathology 2021). These strategies were implemented using eight deep learning architectures. It was observed that transferring from any strategy led to an average acceleration of convergence ranging from 33.88% to 73.16% in mean loss and an improvement of 8.72%–42.12% in mean F1‐score compared to the scratch strategy. Moreover, although smaller and less comprehensive than ImageNet, transferring information from the same domain or related domain proved to be competitive compared to transferring from ImageNet. This indicates that ImageNet, which is widely favoured in the literature, may not necessarily represent the most optimal transfer source for the given context. In addition, to identify which strategies have significant differences, a post hoc analysis using Tukey's HSD test was conducted. Finally, the classifications made by the proposed models were visualized using Grad‐CAM to provide a qualitative understanding of how different weight initialization strategies affect the focus areas of the models.

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

AbstractCan the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Coal allocation optimization based on a hybrid residual prediction model with an improved genetic algorithm

The objective of the coal blending optimization problem is to find an optimal coal blending in the feasible domain such that the blended coal meets the quality requirements at the end of the coking process and the cost of coal blending is minimized. This paper proposes a hybrid residual prediction model and an improved genetic algorithm to solve this problem and predict coke quality. For this purpose, a hybrid residual prediction model is used to predict coke quality. The model first uses a random forest feature extraction method to reduce the dimensionality of the data, and then trains several prediction models such as eXtreme Gradient Boosting (XGBoost), Adaboost and Light Gradient-Boosting Machine (lightGBM) for different coke indicators an improved genetic algorithm based on the adaptive weighted genetic algorithm (awGA) and another improved genetic algorithm based on a priori knowledge and adaptive random initialization method were designed and implemented to solve the optimization problem under strict constraints (P-awGA). The experimental results show that using the hybrid residual prediction model and the improved genetic algorithm can accurately predict the coke quality and use less time to obtain a lower-cost coal blending solution.

A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model's stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods.

A genetic algorithm method for improving suboptimal sensor arrangements in coverage and connectivity problems

This paper is concerned with the solution of a Coverage and Connectivity Problem (CCP). The proposed method performs the placement of sensors forming a connected Wireless Sensor Network (WSN) to fully cover a Field of Interest (FoI). Full coverage is attained despite the presence of opaque obstacles that impair both sensing and communication. To this end, we leverage set operations involving polygons to tessellate the free space. Moreover, we propose the concept of CR-visibility to assess the total area coverage of a polygon by a sensor. The deployment of the minimal number of sensors to completely cover the FoI is cast into the form of an Integer Linear Program (ILP). Two different formulations of connectivity constraints are appended to the ILP. The first one is necessary and sufficient for connectivity, whereas the second alternative is only sufficient. While the number of inequalities in the former grows combinatorially with the number of sensors, the growth is linear in the latter, rendering it more computationally appealing for real-sized FoI. Lastly, we formulate an unconstrained version of the CCP, which is solved by a Genetic Algorithm (GA) with integer variables. We present a small simulation scenario for initial illustration of the proposed method, and a larger, real-life scenario based on the map of an actual urban setting. The results obtained with the real-life scenario show that: (i) high-quality solutions can be obtained in short computation times by imposing the sufficient constraints for connectivity; (ii) the large number of inequalities associated to the necessary and sufficient constraints render the numerical solution impractical; (iii) by using random initialization, the GA solution of the unconstrained problem requires more sensors than the ILP solution with the sufficient connectivity constraint; (iv) including that ILP solution in the initial population of the GA enables it to find a sensor placement that requires fewer sensors.

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In this paper we consider the scalability of multi-angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. Moreover, MA-QAOA is less sensitive to system size, therefore we predict that this factor will be even larger for big graphs. However, MA-QAOA was found to be not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies.

Weakly-supervised learning-based pathology detection and localization in 3D chest CT scans.

Recent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity. In light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self-supervised approach, for multiple chest-related abnormality identification including low lung density area ("LLDA"), consolidation ("CONS"), nodules ("NOD") and interstitial pattern ("IP"). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3Dlocalization. We introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest-related abnormalities: "LLDA", "CONS", "NOD", "IP" and "NORMAL". This method is based on a 3D patch-based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine-tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine-tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi-label patient-level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine-tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine-tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitativeevaluation. Validated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi-abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], -value < 0.001) and 0.963 (95% CI: [0.952, 0.976], -value < 0.001) in the multi-label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non-pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against falsepositives. The proposed method integrates self-supervised learning algorithms for pretraining, utilizes a patch-based approach for 3D pathology localization and develops an aggregation method for multi-label prediction at patient-level. It shows promise in efficiently detecting and localizing multiple anomalies within a singlescan.