Pooling Scheme Research Articles

Effect of Outpatient Pooling Scheme on the Medical Costs of Patients with Chronic Diseases -- Based on Multi-period DID Modeling

The establishment of a sound mechanism for covering outpatient medical bills under the basic medical insurance for urban employees is one of the major livelihood projects to deepen the reform of China's medical security system. This study utilizes the China Health and Retirement Longitudinal Study (CHARLS), combines the interview data from four periods from 2011 to 2018, empirically analyzes the impact of the outpatient pooling scheme on the total outpatient costs and personal burden of patients with chronic diseases by using a multi-period double-difference model and further validates it by using the double-difference propensity score matching method (PSM-DID). The results show that the implementation of the outpatient pooling scheme is conducive to reducing the healthcare burden of chronically ill patients. Although the outpatient co-ordination did not have a significant impact on the reduction of outpatient costs for patients with chronic diseases, it significantly reduced the self-treatment costs of patients with chronic diseases. Further research found that the policy had a greater impact on chronic disease patients with poorer health. Combined with the results of the study, this paper suggests gradually eliminating the differences in basic policy treatment between regions, guaranteeing the level of outpatient treatment for insured patients in different places, and promoting better utilization of the outpatient co-ordination policy effect.

Long-term effects of establishing outpatient pooling funds on financial protection: Empirical evidence from a quasi-natural experiment in China.

A limited benefit package for outpatient care in Chinese universal health coverage led to high out-of-pocket outpatient payments, and even medical impoverishment. The outpatient pooling fund model was introduced in China's Urban Employee Basic Medical Insurance to reduce cost-sharing for outpatient care. This study attempts to examine the dynamic effects of the outpatient pooling scheme on financial risk protection for its enrollees. A total of 18,097 individual-level observations covering 52 prefectures were extracted from six waves of China Health and Nutrition Survey (2000-2015). The difference-in-differences model with multiple periods and event study were employed to investigate the dynamic effects of reform on catastrophic health expenditure (CHE) and impoverishing health expenditure (IHE) and potential mechanisms. Our results showed outpatient pooling scheme generated a significant effect on reducing the probability of incurring CHE (β=-0.004, 95% CI=-0.009 to -0.006) and IHE (β=-0.007, 95% CI=-0.012 to -0.001), especially for elderly people over 60years old. The realization of this effect may depend on the reduction of outpatient cost-sharing, increased outpatient care utilization, as well as decreased inpatient care utilization after reform. However, event study found the effectiveness of outpatient pooling reducing CHE and IHE occurrences appeared to be weak even insignificant in more recent years relative to the initial years of policy implementation. Establishing an outpatient pooling system iseffective to alleviate the financial risk caused by healthexpenditures in China. Optimising health service deliveryaimed at enhancing health insurance purchasing efficiencyare deemed imperative for sustaining the policy effectiveness.

Inequality in benefit distribution of reducing the outpatient cost-sharing: evidence from the outpatient pooling scheme in China.

The implementation of the outpatient pooling scheme in China has substantially elevated the compensation levels for outpatient expenses. This study aims to assess whether socioeconomically disadvantaged enrollees benefit proportionally compared to their non-disadvantaged counterparts. A cohort comprising 14,581 Urban and Rural Resident Basic Medical Insurance (URRBMI) enrollees and 830 Urban Employee Basic Medical Insurance (UEBMI) enrollees was derived from the China Health and Retirement Longitudinal Study 2018. Outpatient pooling scheme benefits were evaluated based on two metrics: the probability of obtaining benefits and the magnitude of benefits (reimbursement amounts and ratios). Two-part models were employed to adjust outpatient benefits for healthcare needs. Inequality in benefit distribution was assessed using the concentration curve and concentration index (CI). Following adjustments for healthcare needs, the CI for the probability of receiving outpatient benefits for URRBMI and UEBMI enrollees were - 0.0760 and - 0.0514, respectively, indicating an evident pro-poor pattern under the outpatient pooling scheme. However, the CIs of reimbursement amounts (0.0708) and ratio (0.0761) for URRBMI recipients were positive, signifying a discernible pro-rich inequality in the degree of benefits. Conversely, socioeconomically disadvantaged UEBMI enrollees received higher reimbursement amounts and ratios. Despite a higher likelihood of socioeconomically disadvantaged groups receiving outpatient benefits, a pro-rich inequality persists in the degree of benefits under the outpatient pooling scheme in China. Comprehensive strategies, including expanding outpatient financial benefits, adopting distinct reimbursement standards, and enhancing the accessibility of outpatient care, need to be implemented to achieve equity in benefits distribution.

PSRR-MaxpoolNMS++: Fast Non-Maximum Suppression with Discretization and Pooling.

Non-maximum suppression (NMS) is an essential post-processing step for object detection. The de-facto standard for NMS, namely GreedyNMS, is not parallelizable and could thus be the performance bottleneck in object detection pipelines. MaxpoolNMS is introduced as a fast and parallelizable alternative to GreedyNMS. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster R-CNN. To address this issue, we observe that MaxpoolNMS employs the process of box coordinate discretization followed by local score argmax calculation, to discard the nested-loop pipeline in GreedyNMS to enable parallelizable implementations. In this paper, we introduce a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module to improve the above two stages, respectively. With these two modules, our PSRR-MaxpoolNMS is a generic and parallelizable approach, which can completely replace GreedyNMS at all stages in all detectors. Furthermore, we extend PSRR-MaxpoolNMS to the more powerful PSRR-MaxpoolNMS++. As for box coordinate discretization, we propose Density-based Discretization for better adherence to the target density of the suppression. As for local score argmax calculation, we propose an Adjacent Scale Pooling scheme for mining out the duplicated box pairs more accurately and efficiently. Extensive experiments demonstrate that both our PSRR-MaxpoolNMS and PSRR-MaxpoolNMS++ outperform MaxpoolNMS by a large margin. Additionally, PSRR-MaxpoolNMS++ not only surpasses PSRR-MaxpoolNMS but also attains competitive accuracy and much better efficiency when compared with GreedyNMS. Therefore, PSRR-MaxpoolNMS++ is a parallelizable NMS solution that can effectively replace GreedyNMS at all stages in all detectors.

Deep Learning Model Aids Breast Cancer Detection

Breast cancer, a lumpy nodule or granular calcified tissue caused by cancerous changes in chest tissue, has become one of the most prevalent cancers. Due to the location and structure of the tumor, it can be detected directly by ultrasound or X-ray and is less likely to spread to other parts of the body than tumors in other parts of the body. Considering the huge number of sick people, the resources required for a full census would be enormous, but thanks to the rapid development of medical image processing technology in recent years, assisted diagnosis through deep learning models has gradually become more widely accepted. For detection models, higher accuracy means lower misdiagnosis rates and timely treatment for patients. Therefore, in this paper, we first specify the diagnose as a binary classification problem and then introduce a new pooling scheme and training method to achieve better results compared to the traditional network backbone in the past.

Assessing Memory in Convection Schemes Using Idealized Tests

AbstractTwo assumptions commonly applied in convection schemes—the diagnostic and quasi‐equilibrium assumptions—imply that convective activity (e.g., convective precipitation) is controlled only by the large‐scale (macrostate) environment at the time. In contrast, numerical experiments indicate a “memory” or dependence of convection also on its own previous activity whereby subgrid‐scale (microstate) structures boost but are also boosted by convection. In this study we investigated this memory by comparing single‐column model behavior in two idealized tests previously executed by a cloud‐resolving model (CRM). Conventional convection schemes that employ the diagnostic assumption fail to reproduce the CRM behavior. The memory‐capable org and Laboratoire de Météorologie Dynamique Zoom cold pool schemes partially capture the behavior, but fail to fully exhibit the strong reinforcing feedbacks implied by the CRM. Analysis of this failure suggests that it is because the CRM supports a linear (or superlinear) dependence of the subgrid structure growth rate on the precipitation rate, while the org scheme assumes a sublinear dependence. Among varying versions of the org scheme, the growth rate of the org variable representing subgrid structure is strongly associated with memory strength. These results demonstrate the importance of parameterizing convective memory, and the ability of idealized tests to reveal shortcomings of convection schemes and constrain model structural assumptions.

Random Number Generation Based on Heterogeneous Entropy Sources Fusion in Multi-Sensor Networks.

The key system serves as a vital foundation for ensuring the security of information systems. In the presence of a large scale of heterogeneous sensors, the use of low-quality keys directly impacts the security of data and user privacy within the sensor network. Therefore, the demand for high-quality keys cannot be underestimated. Random numbers play a fundamental role in the key system, guaranteeing that generated keys possess randomness and unpredictability. To address the issue of random number requirements in multi-sensor network security, this paper introduces a new design approach based on the fusion of chaotic circuits and environmental awareness for the entropy pool. By analyzing potential random source events in the sensor network, a high-quality entropy pool construction is devised. This construction utilizes chaotic circuits and sensor device awareness technology to extract genuinely random events from nature, forming a heterogeneous fusion of a high-quality entropy pool scheme. Comparatively, this proposed scheme outperforms traditional random entropy pool design methods, as it can meet the quantity demands of random entropy sources and significantly enhance the quality of entropy sources, ensuring a robust security foundation for multi-sensor networks.

Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective.

Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person's activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP-TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).

Temporal Data Pooling With Meta-Initialization for Individual Short-Term Load Forecasting

The growth of advanced metering infrastructure (AMI) deployment has enabled intelligent power control and management at the customer level; Highly accurate individual short-term load forecasting is crucial for this precise control per customer. Recently, deep learning has been widely adopted to improve forecasting accuracy. However, training an individual deep network (local training) has overfitting issues due to data paucity per customer. Thus, a pooling scheme has been introduced to augment a training dataset by batching several customers’ data. Nevertheless, there is room for existing pooling approaches to further improve accuracy by considering distribution heterogeneity within a customer dataset. In addition, their static pool assignment only with a customer’s training dataset may cause accuracy degradation under concept drift in serving time. To overcome these, we propose a Temporal Data Pooling (TDP) that constructs data pools at the data sample level with a novel distribution inference method and theoretical analysis. It allows the most probable forecasting model to serve predictions while resolving data shortage issues in local training. The TDP outperforms the other six competing methods for point and probabilistic forecasting; it shows robust accuracy under concept drift. Moreover, it demonstrates superior accuracy for unseen customers without additional training, proving its scalability.

Dorfman pooling enhances SARS-CoV-2 large-scale community testing efficiency.

PCR-based analysis is the gold standard for detection of SARS-CoV-2 and was used broadly throughout the pandemic. However, heightened demand for testing put strain on diagnostic resources and the adequate amount of PCR-based testing required exceeded existing testing capacity. Pooled testing strategies presented an effective method to increase testing capacity by decreasing the number of tests and resources required for laboratory PCR analysis of SARS-CoV-2. We sought to conduct an analysis of SARS-CoV-2 pooling schemes to determine the sensitivity of various sized Dorfman pooling strategies and evaluate the utility of using such pooling strategies in diagnostic laboratory settings. Overall, a trend of decreasing sensitivity with larger pool sizes was observed, with modest sensitivity losses in the largest pools tested, and high sensitivity in all other pools. Efficiency data was then calculated to determine the optimal Dorfman pool sizes based on test positivity rate. This was correlated with current presumptive test positivity to maximize the number of tests saved, thereby increasing testing capacity and resource efficiency in the community setting. Dorfman pooling methods were evaluated and found to offer a high-throughput solution to SARS-CoV-2 clinical testing that improve resource efficiency in low-resource environments.

Deep Learning-Based Apple Detection with Attention Module and Improved Loss Function in YOLO

Horticulture and agriculture are considered as the important pillars of any economy. Current technological advancements have led to the development of several new technologies which are useful in atomizing the agriculture process. Apple farming has a significant role in Italy’s agriculture domain where manual labor is widely employed for apple picking which can be replaced by automated robot mechanisms. However, these mechanisms are based on computer vision methods. These methods focus on detection, localization and tracking the apple fruits in given video frames. Later, appropriate actions can be taken to enhance the production and harvesting. Several techniques have been presented for apple detection, but complex background, noise and image blurriness are the major causes which can deteriorate the performance of the system. Thus, in this work, we present a deep learning-based scheme to detect apples which uses Yolov5 architecture in live apple farm images. We further improve the Yolov5 architecture by incorporating an adaptive pooling scheme and attribute augmentation model. This model detects the smaller objects and improves the feature quality to detect the apples in complex backgrounds. Moreover, a loss function is also incorporated to obtain the accurate bounding box which helps to maximize the detection accuracy. The comparative study shows that the proposed approach with the improved Yolov5 architecture achieves overall accuracy of 0.97, 0.99, and 0.98 in terms of precision, recall, and F1-score, respectively.

Image classification using graph neural network and multiscale wavelet superpixels

Prior studies using graph neural networks (GNNs) for image classification have focused on graphs generated from a regular grid of pixels or similar-sized superpixels. In the latter, a single target number of superpixels is defined for an entire dataset irrespective of differences across images and their intrinsic multiscale structure. On the contrary, this study investigates image classification using graphs generated from an image-specific number of multiscale superpixels. We propose WaveMesh, a new wavelet-based superpixeling algorithm, where the number and sizes of superpixels in an image are systematically computed based on its content. WaveMesh superpixel graphs are structurally different from similar-sized superpixel graphs. We use SplineCNN, a state-of-the-art network for image graph classification, to compare WaveMesh and similar-sized superpixels. Using SplineCNN, we perform extensive experiments on three benchmark datasets under three local-pooling settings: 1) no pooling, 2) GraclusPool, and 3) WavePool, a novel spatially heterogeneous pooling scheme tailored to WaveMesh superpixels. Our experiments demonstrate that SplineCNN learns from multiscale WaveMesh superpixels on-par with similar-sized superpixels. In all WaveMesh experiments, GraclusPool performs poorer than no pooling / WavePool, showing that poor cluster assignment negatively affects the performance of the network while learning from multiscale superpixels.

A joint use of pooling and imputation for genotyping SNPs

BackgroundDespite continuing technological advances, the cost for large-scale genotyping of a high number of samples can be prohibitive. The purpose of this study is to design a cost-saving strategy for SNP genotyping. We suggest making use of pooling, a group testing technique, to drop the amount of SNP arrays needed. We believe that this will be of the greatest importance for non-model organisms with more limited resources in terms of cost-efficient large-scale chips and high-quality reference genomes, such as application in wildlife monitoring, plant and animal breeding, but it is in essence species-agnostic. The proposed approach consists in grouping and mixing individual DNA samples into pools before testing these pools on bead-chips, such that the number of pools is less than the number of individual samples. We present a statistical estimation algorithm, based on the pooling outcomes, for inferring marker-wise the most likely genotype of every sample in each pool. Finally, we input these estimated genotypes into existing imputation algorithms. We compare the imputation performance from pooled data with the Beagle algorithm, and a local likelihood-aware phasing algorithm closely modeled on MaCH that we implemented.ResultsWe conduct simulations based on human data from the 1000 Genomes Project, to aid comparison with other imputation studies. Based on the simulated data, we find that pooling impacts the genotype frequencies of the directly identifiable markers, without imputation. We also demonstrate how a combinatorial estimation of the genotype probabilities from the pooling design can improve the prediction performance of imputation models. Our algorithm achieves 93% concordance in predicting unassayed markers from pooled data, thus it outperforms the Beagle imputation model which reaches 80% concordance. We observe that the pooling design gives higher concordance for the rare variants than traditional low-density to high-density imputation commonly used for cost-effective genotyping of large cohorts.ConclusionsWe present promising results for combining a pooling scheme for SNP genotyping with computational genotype imputation on human data. These results could find potential applications in any context where the genotyping costs form a limiting factor on the study size, such as in marker-assisted selection in plant breeding.

A No-Reference Quality Assessment Method for Screen Content Images Based on Human Visual Perception Characteristics

The widespread application of screen content images (SCIs) has met the needs of remote display and online working. It is a topic that is challenging and worthwhile discussing in research on quality assessment for SCIs. However, existing methods focus on extracting artificial features to predict image quality, which are subjective and incomplete, or lack good interpretability. To overcome these problems, we propose an effective quality assessment method for SCIs based on human visual perceptual characteristics. The proposed method simulates the multi-channel working mechanism of the human visual system (HVS) through pyramid decomposition and the information extraction process of brains with the help of dictionary learning and sparse coding. The input SCIs are first decomposed at multiple scales, and then dictionary learning and sparse coding are applied to the images at each scale. Furthermore, the sparse representation results are analyzed from multiple perspectives. First, a pooling scheme about generalized Gaussian distribution and log-normal distribution is designed to describe the sparse coefficients with and without zero values, respectively. Then the sparse coefficients are used to characterize the energy characteristics. Additionally, the probability of each atom is calculated to describe the statistical property of SCIs. Since the above process only deals with brightness, color-related features are also added to make the model more general and robust. Experimental results on three public SCI databases show that the proposed method can achieve better performance than existing methods.

Partition pooling for convolutional graph network applications in particle physics

Convolutional graph networks are used in particle physics for effective event reconstructions and classifications. However, their performances can be limited by the considerable amount of sensors used in modern particle detectors if applied to sensor-level data. We present a pooling scheme that uses partitioning to create pooling kernels on graphs, similar to pooling on images. Partition pooling can be used to adopt successful image recognition architectures for graph neural network applications in particle physics. The reduced computational resources allow for deeper networks and more extensive hyperparameter optimizations. To show its applicability, we construct a convolutional graph network with partition pooling that reconstructs simulated interaction vertices for an idealized neutrino detector. The pooling network yields improved performance and is less susceptible to overfitting than a similar network without pooling. The lower resource requirements allow the construction of a deeper network with further improved performance.

Tensorizing GAN With High-Order Pooling for Alzheimer's Disease Assessment.

It is of great significance to apply deep learning for the early diagnosis of Alzheimer's disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess mild cognitive impairment (MCI) and AD. By tensorizing a three-player cooperative game-based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of holistic magnetic resonance imaging (MRI). To the best of our knowledge, the proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis. Extensive experimental results on Alzheimer's disease neuroimaging initiative (ADNI) data set are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semisupervised learning purpose.

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling.

Identification and isolation of contagious individuals along with quarantine of close contacts, is critical for slowing the spread of COVID-19. Large-scale testing in a surveillance or screening capacity for asymptomatic carriers of COVID-19 provides both data on viral spread and the follow-up ability to rapidly test individuals during suspected outbreaks. The COVID-19 early detection program at Michigan State University has been utilizing large-scale testing in a surveillance or screening capacity since fall of 2020. The methods adapted here take advantage of the reliability, large sample volume, and self-collection benefits of saliva, paired with a cost-effective, reagent conserving two-dimensional pooling scheme. The process was designed to be adaptable to supply shortages, with many components of the kits and the assay easily substituted. The processes outlined for collecting and processing SARS-CoV-2 samples can be adapted to test for future viral pathogens reliably expressed in saliva. By providing this blueprint for universities or other organizations, preparedness plans for future viral outbreaks can be developed.

Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks

Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous works typically focus on using the graph pooling scheme in a linear manner. In this paper, we propose the real quadratic-form-based graph pooling framework for graph neural networks in graph classification. The quadratic form can capture a pairwise relationship, which brings a stronger expressive power than existing linear forms. Experiments on benchmarks verify the effectiveness of the proposed graph pooling scheme based on the quadratic form in graph classification tasks.

The Listening Barriers Scale for University Students: A Study of Validity and Reliability

Being the most used skill in the school environment as well as being the first skill to be acquired, listening can be defined as making sense of the sounds heard in the communication process and responding as a result of this interpretation. The fact that the response given at the end of a listening process is appropriate for the context is associated with completing the listening process without encountering any obstacles. However, the conducted studies emphasize that students encounter many listening barriers during the listening process. It is considered that it is important for the development of listening skills to detect the barriers to effective listening, which is a major source of school learning, at every education level from pre-school to university. In this context, the aim of the study is to develop a scale to determine the listening barriers encountered by university students in the course process. The data of the research were obtained from 587 students studying in different departments of Kırıkkale University in Turkey. In the development of the measurement tool, literature review, item pooling, content validity (consulting to expert opinion), and pilot scheme stages were followed, and then necessary reliability calculations were carried out. In line with the scores obtained from the tool, exploratory and confirmatory factor analysis was carried out in order to test the construct validity of the draft scale. As a result of the factor analysis, it was determined that the scale revealed a three-factor structure classified as “teacher related listening barriers”, “student related listening barriers”, and “classroom environment and course related listening barriers”, and the Cronbach’s Alpha internal consistency coefficient was detected as .93 for the overall scale. As a result of the research, a 30-item scale was developed to determine the listening barriers encountered by university students.

Near-Optimal Sparsity-Constrained Group Testing: Improved Bounds and Algorithms

Recent advances in noiseless non-adaptive group testing have led to a precise asymptotic characterization of the number of tests required for high-probability recovery in the sublinear regime <inline-formula> <tex-math notation="LaTeX">$k = n^{\theta }$ </tex-math></inline-formula> (with <inline-formula> <tex-math notation="LaTeX">$\theta \in (0,1)$ </tex-math></inline-formula>), with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> individuals among which <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> are infected. However, the required number of tests may increase substantially under real-world practical constraints, notably including bounds on the maximum number <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> of tests an individual can be placed in, or the maximum number <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> of individuals in a given test. While previous works have given recovery guarantees for these settings, significant gaps remain between the achievability and converse bounds. In this paper, we substantially or completely close several of the most prominent gaps. In the case of <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>-divisible items, we show that the definite defectives (DD) algorithm coupled with a random regular design is asymptotically optimal in dense scaling regimes, and optimal to within a factor of e more generally; we establish this by strengthening both the best known achievability and converse bounds. In the case of <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-sized tests, we provide a comprehensive analysis of the regime <inline-formula> <tex-math notation="LaTeX">$\Gamma = \Theta (1)$ </tex-math></inline-formula>, and again establish a precise threshold proving the asymptotic optimality of SCOMP (a slight refinement of DD) equipped with a tailored pooling scheme. Finally, for each of these two settings, we provide near-optimal adaptive algorithms based on sequential splitting, and provably demonstrate gaps between the performance of optimal adaptive and non-adaptive algorithms.