Absolute Estimates Research Articles

Enabling FEM-based absolute permeability estimation in giga-voxel porous media with a single GPU

The characterization of porous media via digital testing usually relies on intensive numerical computations that can be parallelized in GPUs. For absolute permeability estimation, Stokes flow simulations are carried out at the micro-structure to recover velocity fields that are used in upscaling with Darcy’s law. Digital models of samples can be obtained via micro-computed tomography (μCT) scans. As μCT data is three-dimensional, meshes grow cubically with image dimensions, causing the numerical problem at hand to become compute- and memory-bound as either resolution improves or larger fields-of-view are considered. While the usual focus is on accelerating solvers, memory usage continues to be a significant limitation for analyses of representative volumes in relatively accessible hardware. In this work, we explore the possibility of implementing MINRES solvers in GPU that favor a reduction in memory allocation. These solvers are applied to matrix-free FEM-based permeability characterization of μCT images. Our goal is to enable the study of 10003 voxel images in single GPU machines. Implementations that only require five, three, or two n-sized vectors of variables are presented, with n being the number of unknowns. Further, we employ a mesh numbering strategy that enables node-by-node massively parallel operations within a non-monolithic voxel-based pore space without storing connectivity tables. The proposed solvers, available through the open-source chfem software, are verified against analytical models for simple three-dimensional micro-structures, then are validated against numerical Digital Petrophysics benchmarks. A consumer-grade graphics card with 12GB of RAM is employed for the characterization of images with up to roughly 540 million voxels in a matter of tens of minutes. Stokes flow FEM-based simulations in meshes with 449 million degrees-of-freedom (DOFs) are carried out in 9 to 15 min, allocating less than 10GB in global memory. Finally, simulations on three 10003 carbon fiber domains, amounting to more than 3.7 billion DOFs, were run on a high-end GPU with 80GB of RAM in under 2.5 h, achieving very close agreement with flow-tube permeability experiments.

Ridgecrest Aftershock Stress Drops from P- and S-Wave Spectral Decomposition

ABSTRACT Seismic moment and stress drop are crucial for understanding earthquake rupture processes, but their estimates often have large uncertainties for small earthquakes. Stress drop is typically inferred from an earthquake’s source spectrum based on theoretical models, but poorly constrained path corrections and other modeling assumptions limit the accuracy of stress-drop estimates. Here, we compute stress drops using both P and S waves for the 2019 Ridgecrest earthquake sequence, compare their estimates, and evaluate the associated uncertainties. We use spectral decomposition and apply the analysis to both types of waves for the same set of earthquakes, adjusting some S-wave parameter choices to obtain overall consistency with our P-wave results. Our approach fixes the corner frequency of small calibration earthquakes to reduce scatter in the estimated source parameters of the larger earthquakes. We find that assuming a lower high-frequency fall-off rate for S waves yields more consistent absolute stress-drop estimates between P and S waves. Our stress-drop estimates appear to increase slightly with magnitude for earthquakes with magnitudes >∼3.4. Furthermore, we find that the stress-drop estimates using both types of data exhibit coherent spatial variations. Earthquakes near the Coso geothermal field tend to have lower stress drops, and earthquakes near the M 7.1 hypocenter have higher stress-drop estimates. This spatial pattern is consistently observed in both the P- and S-wave results. We find no strong correlation between our stress-drop estimates and the M 7.1 Ridgecrest earthquake slip distribution, suggesting a heterogeneous stress environment for the Ridgecrest fault system.

Exploring the characteristics, methods and reporting of systematic reviews with meta-analyses of time-to-event outcomes: a meta-epidemiological study

BackgroundTime-to-event analysis is associated with methodological complexities. Previous research identified flaws in the reporting of time-to-event analyses in randomized trial publications. These hardships impose challenges for meta-analyses of time-to-event outcomes based on aggregate data. We examined the characteristics, reporting and methods of systematic reviews including such analyses.MethodsThrough a systematic search (02/2017-08/2020), we identified 50 Cochrane Reviews with ≥ 1 meta-analysis based on the hazard ratio (HR) and a corresponding random sample (n = 50) from core clinical journals (Medline; 08/02/2021). Data was extracted in duplicate and included outcome definitions, general and time-to-event specific methods and handling of time-to-event relevant trial characteristics.ResultsThe included reviews analyzed 217 time-to-event outcomes (Median: 2; IQR 1–2), most frequently overall survival (41%). Outcome definitions were provided for less than half of time-to-event outcomes (48%). Few reviews specified general methods, e.g., included analysis types (intention-to-treat, per protocol) (35%) and adjustment of effect estimates (12%). Sources that review authors used for retrieval of time-to-event summary data from publications varied substantially. Most frequently reported were direct inclusion of HRs (64%) and reference to established guidance without further specification (46%). Study characteristics important to time-to-event analysis, such as variable follow-up, informative censoring or proportional hazards, were rarely reported. If presented, complementary absolute effect estimates calculated based on the pooled HR were incorrectly calculated (14%) or correct but falsely labeled (11%) in several reviews.ConclusionsOur findings indicate that limitations in reporting of trial time-to-event analyses translate to the review level as well. Inconsistent reporting of meta-analyses of time-to-event outcomes necessitates additional reporting standards.

Prediction of respiratory failure and prolonged mechanical ventilation in Guillain-Barré syndrome: A prospective cohort study in Bangladesh.

The aim of this study is to validate and perform a region-specific adjustment of the Erasmus GBS Respiratory Insufficiency Score (EGRIS) and identify potential predictors of prolonged mechanical ventilation (PMV) among Guillain-Barré syndrome (GBS) patients from Bangladesh. We enrolled GBS patients from four prospective observational cohort studies conducted in Bangladesh. Accuracy of EGRIS to predict the requirement of MV in <7 days of study entry was evaluated. Model performance was assessed by discrimination (ability of the model to differentiate between patients who needed MV or not) and calibration (accuracy of absolute risk estimates). PMV was defined as duration of MV >14 days. Potential predictors for PMV were evaluated by Cox regression. A total of 594 GBS patients aged ≥6 years old were enrolled; of whom 541 patients had complete EGRIS data prior to MV and were included in validation analysis. EGRIS correctly distinguished between patients requiring MV or not in 81% pairs (AUC = 0.81). EGRIS overestimated the probability of MV than the observed probability (41% vs. 20%) which was resolved by updating of the model intercept. Inability to flex hip at day 7 of start of MV was the strongest predictor for PMV with predicted probabilities of 82%. EGRIS accurately predicts the need for MV in GBS patients from Bangladesh. This study developed a region-specific version of EGRIS and identified predictors of PMV. These findings can assist clinicians to identify patients at high risk of developing respiratory failure and requiring PMV to ensure timely intubation and tracheostomy of the patients in low resource settings.

Binocular camera-based visual localization with optimized keypoint selection and multi-epipolar constraints

In recent years, visual localization has gained significant attention as a key technology for indoor navigation due to its outstanding accuracy and low deployment costs. However, it still encounters two primary challenges: the requirement for multiple database images to match the query image and the potential degradation of localization precision resulting from the keypoints clustering and mismatches. In this research, a novel visual localization framework based on a binocular camera is proposed to estimate the absolute positions of the query camera. The framework integrates three core methods: the multi-epipolar constraints-based localization (MELoc) method, the Optimal keypoint selection (OKS) method, and a robust measurement method. MELoc constructs multiple geometric constraints to enable absolute position estimation with only a single database image, while OKS and the robust measurement method further enhance localization accuracy by refining the precision of these geometric constraints. Experimental results demonstrate that the proposed system consistently outperforms existing visual localization systems across various scene scales, database sampling intervals, and lighting conditions

State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm

The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.

Multi-type feature fusion visual navigation for asteroid landing

In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features, such as significant recognition and extraction errors, low computational efficiency, and limited navigation accuracy, a novel approach for multi-type fusion visual navigation is proposed. This method aims to overcome the limitations of single-type features and enhance navigation accuracy. Analytical criteria for selecting multi-type features are introduced, which simultaneously improve computational efficiency and system navigation accuracy. Concerning pose estimation, both absolute and relative pose estimation methods based on multi-type feature fusion are proposed, and multi-type feature normalization is established, which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation. The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.

People of the Sea, or of the Soil? How the Balance of Marine and Terrestrial Resource Availability Informs Maximum Population on Four Polynesian Islands

AbstractMost studies of the food resource potential of early Polynesian populations focus exclusively on agricultural potential, and specifically starchy staples, despite the importance of marine resources to the Polynesians. To more accurately estimate total precontact food resource availability, we characterized the terrestrial and near-shore marine environments of four Polynesian islands: Moʻorea, Maupiti, Mangareva and Taravai. We estimate the agricultural potential of each island after a consideration of the ecological factors related to productivity. We also estimate the productivity of the near-shore marine environment as a function of surface area. Using a range of fish yield scenarios from Pacific subsistence systems we scale relative measures of faunal food biomass derived from local archaeological excavations to generate absolute biomass estimates. We convert our estimates of agricultural and fished/foraged food potential into a maximum population size based on calorie availability. Moʻorea’s fertile valleys and wetlands would potentially generate sufficient food energy, mostly from starchy staples, to support a considerable population, many times larger than the other islands. In the most likely fish yield scenario Moʻorea gets only 0.4% of its calories from marine sources, while the others range from 7–18%. These relative inputs reflect the vast superiority in productivity (in terms of calories per km2) of agricultural vs near-shore marine zones. We also find that population size on islands with smaller fringing reefs, such as Moʻorea, may have been limited by a lack of fish protein. Moʻorea’s maximum population is approximately halved when diet breadth is considered.

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Mathematical consideration of massive estimation of dissociation rate constant for genotype–phenotype linking molecules bound to targets through washing/selection and next-generation sequencing

As one of methods for in vitro selection, a flow reactor type washing/selection system seems to be effective, where a ligand library is composed of “genotype–phenotype linking molecules”. In this system, high affinity ligands are selected by their respective “residual ratio” given by exp(−koff×t), where koff is the dissociation rate constant and t is the washing time. In this paper, we mathematically considered the following possibility. When the washing/selection dynamics obeys the residual ratio exp(−koff×t) deterministically and mole fraction measurement for sampled sequences by next-generation sequencing (NGS) is performed ideally, the “relative value” of koff for each of high-ranking sequences can be estimated simultaneously. In addition to these, when the residual ratio for the whole ligand population is measured correctly, the “absolute value” for each sequence can be estimated. We deduced formulas to present the relative and absolute estimates, and mathematically analyzed the effect of fluctuations in the number of NGS reads on the estimates in details. These were confirmed by numerical simulations.

DEMOGRAPHIC PATTERNS IN HYLLIE MOSSE (SCANIA, SWEDEN): ESTIMATING ABSOLUTE POPULATION BETWEEN THE LATE NEOLITHIC AND THE EARLY BRONZE AGES

SummaryThis paper reconstructs the population dynamics within the Hyllie Mosse region (Scania, Sweden) from 2400 to 1600 BC. South‐western Scania is particularly well‐known thanks to extensive archaeological work in the past decades, making it one of the most thoroughly investigated areas in Sweden. The Late Neolithic and Early Bronze Age periods are of paramount importance in understanding the socio‐cultural transformations within the Baltic region. By integrating a comprehensive database of longhouses from Scania with the available archaeological data from the Hyllie Mosse area, this study provides absolute demographic estimates through a transparent and replicable methodology. It highlights that the region experienced a significant peak in human occupation between 2000 and 1800 BC, followed by a decline. In prehistoric times, the area was covered by a relatively large bog, and was therefore not uniformly settled. At its peak, it is estimated that the region, approximately 9.7 km2 in size – 4 km2 of which have been archaeologically investigated – could have supported 75 to 150 inhabitants.

Abstract IA004: Risk of therapy-related subsequent cancers in individuals with germline TP53 variants

Abstract Background. Based primarily on in vitro and animal studies, germline TP53 variants are considered to confer sensitivity to ionizing radiation-induced DNA damage, but data in humans are sparse. We sought to quantify risk of developing radiotherapy and chemotherapy-related subsequent neoplasms by pooling individual-level data from nine cohorts of individuals with confirmed pathogenic/likely pathogenic germline TP53 variants. Methods. 891 individuals were followed from six months after first cancer until diagnosis of a subsequent neoplasm, death or date of last follow-up. Radiotherapy and chemotherapy for each cancer were considered as time-dependent covariates in Cox regression models with age as the time-scale, adjusted for sex and occurrence of prior malignant or benign tumors. Subsequent cancers were classified as in- or out-of-field based on the body region of previous cancers. Results. Among 891 individuals, 643 (72.2%) were female and 297 (33.3%) were diagnosed with a first cancer before age 20 years. Breast cancer was the most commonly diagnosed first cancer (N=371), followed by soft tissue sarcoma (N=131) and central nervous system cancers (N=84). First cancer treatment included radiotherapy for 237 (26.6%) and/or chemotherapy for 421 (47.3%) individuals. Analyses are underway to define the characteristics of subsequent neoplasms (including age at diagnosis, latency, and distribution of diagnosis type) and to calculate relative and absolute risk estimates for radiotherapy and chemotherapy overall and by subsequent neoplasm type. Conclusions. This study will quantify the contribution of radiotherapy and chemotherapy to the risk of subsequent cancers among individuals with Li-Fraumeni syndrome. Understanding the magnitude of this risk will support health care providers and patients in making informed decisions about cancer management. Citation Format: Anita Villani, Payal Khincha, Jessica Hatton, Sara Schonfeld, Catherine Goudie, Leatrisse Oba, Kishan Pithadia, Birte Sänger, Christian Kratz, Anh N. Le, Caitlin Orr, Kara N. Maxwell, Anne Naumer, Jennie Vagher, Wendy Kohlmann, Joshua Schiffman, Elizabeth Santana dos Santos, Orli Michaeli, Jacob J. Carson, Derek S. Tsang, Raymond Kim, Jamie L. Maciaszek, Kim E. Nichols, Victoria E. Goodman, Suzanne P. MacFarland, Lara Reichman, Maria Isabel Achatz, Sharon Savage, David Malkin, Lindsay M. Morton. Risk of therapy-related subsequent cancers in individuals with germline TP53 variants [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA004.

Immuno-pathogenesis study of local infectious bronchitis virus G1-1 lineage variant showed altered tissue tropism in experimental broiler chickens.

Infectious bronchitis (IB) is an acute contagious disease of poultry caused by infectious bronchitis virus (IBV). This study investigated the immunopathogenesis and tissue tropism of an Indian IBV field isolate (IBV/Chicken/India/IVRI/Rajasthan/01/2023) in experimental broiler chickens. This isolate belongs to the G1-1 lineage and is closely associated with the Mass genotype. 106.23 EID50/0.2 mL of the virus was administered intranasally and intraocularly to the IBV-challenge group, whereas uninoculated allantoic fluid was administered to the control group. Clinical signs, gross and histopathological lesions, immunohistochemistry (IHC), viral load, humoral responses, and the relative expression of immune response genes were evaluated at seven observation points. The infected group showed a significant reduction in weight gain from 3 dpi onwards, with clinical signs of varying severity from 3 to - 11 dpi. Gross lesions and microscopic changes were observed in the nasal turbinates, trachea, lungs, and kidneys, mainly representing epithelial degeneration and necrosis with mononuclear infiltrates. The caecal tonsils also showed microscopic lesions at 7-9 dpi. Absolute viral load estimation in the organs corroborated the lesion severity scores and IHC results. The expression of innate immune responses broadly demonstrated higher expression in the trachea and lungs of the IBV-infected group during the early phase of infection, whereas similar responses were observed in the kidneys and caecal tonsils during the later phases of infection. This study suggests that the given IBV isolate may cause significant production losses in broilers and exhibit tissue tropism for both respiratory and non-respiratory tissues, triggering varying innate and adaptive immune responses.

Longitudinal assessment of knee joint proprioception using weight-bearing and non-weight-bearing tests throughout rehabilitation after anterior cruciate ligament reconstruction

ObjectiveEvaluate active knee joint position sense (JPS) throughout rehabilitation after anterior cruciate ligament reconstruction (ACLR). DesignLongitudinal. SettingMotion laboratory. ParticipantsTwenty-two individuals post-ACLR and 22 activity-matched non-injured controls performed weight-bearing and non-weight-bearing knee JPS tests. The ACLR participants performed at three functional timepoints: T1, able to perform single-leg sit-to-stand; T2, able to hop maximally; T3, cleared for return to sports. Controls performed on one occasion. Main outcome measuresConstant, absolute, variable errors (CE, AE, VE) and interlimb symmetry estimates (100% signifying perfect symmetry), for 40° and 65° knee flexion target angles. ResultsFor the weight-bearing 40° condition, CE and AE of the ACLR knee significantly increased from T1 to T2 (P = 0.010) and T1 to T3 (P = 0.002). Consequently, interlimb asymmetry for AE significantly increased from T1 (AEsym% = 101.2% ± 55.4%) to T3 (AEsym% = 139.7% ± 54.8%). Compared to controls, AE for the ACLR knee was significantly smaller at T1 (P = 0.016). No other significant differences were observed. ConclusionsSmaller JPS errors at early rehabilitation while weight-bearing may have been due to heightened quadriceps activation and increased γ motor neuron sensitivity, compensating for deficient ACL mechanoreceptors. In contrast, non-weight-bearing testing did not reveal changes over time. Clinicians are advised to consider these distinctions when assessing proprioception at different rehabilitation stages.

Sodium-glucose co-transporter-2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes: systematic review and meta-analysis

ObjectiveTo examine cardiovascular and kidney benefits and harms of sodium-glucose co-transporter-2 (SGLT-2) inhibitors stratified by risk in adults with chronic kidney disease regardless of diabetes status.DesignSystematic review and meta-analysis.Data sourcesOvid...

Differences among the total electron content derived by radio occultation, global ionospheric maps and satellite altimetry

In recent years, significant progress has been in ionospheric modeling research through data ingestion and data assimilation from a variety of sources, including ground-based global navigation satellite systems, space-based radio occultation and satellite altimetry (SA). Given the diverse observing geometries, vertical data coverages and intermission biases among different measurements, it is imperative to evaluate their absolute accuracies and estimate systematic biases to determine reasonable weights and error covariances when constructing ionospheric models. This study specifically investigates the disparities among the vertical total electron content (VTEC) derived from SA data of the Jason and Sentinel missions, the integrated VTEC from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and global ionospheric maps (GIMs). To mitigate the systematic bias resulting from differences in satellite altitudes, the vertical ranges of various VTECs are mapped to a standardized height. The results indicate that the intermission bias of SA-derived VTEC remains relatively stable, with Jason-1 serving as a benchmark for mapping other datasets. The mean bias between COSMIC and SA-derived VTEC is minimal, suggesting good agreement between these two space-based techniques. However, COSMIC and GIM VTEC exhibit remarkable seasonal discrepancies, influenced by the solar activity variations. Moreover, GIMs demonstrate noticeable hemispheric asymmetry and a degradation in accuracy ranging from 0.7 to 1.7 TECU in the ocean-dominant Southern Hemisphere. While space-based observations effectively illustrate phenomena such as the Weddell Sea anomaly and longitudinal ionospheric characteristics, GIMs tend to exhibit a more pronounced mid-latitude electron density enhancement structure.

Redesigning automated market power mitigation in electricity markets

Electricity markets are prone to the abuse of market power. Several U.S. markets employ algorithms to monitor and mitigate market power abuse in real-time. The performance of automated mitigation procedures is contingent on precise estimates of firms' marginal production costs. Currently, marginal cost is inferred from the past offers of a plant. We present new estimation approaches and compare them to the currently applied benchmark method. We test the performance of all approaches on auction data from the Iberian power market. The results show that our novel approaches outperform the benchmark approach significantly, reducing the mean (median) absolute estimation error from 11.53 (6.08) €/MWh in the benchmark to 4.03 (2.64) €/MWh for our preferred approach. This approach also performs best in our subsequent simulation of mitigation procedures. Here we find large welfare transfers from supplier to buyer surplus as well as a robust overall welfare gain, stemming from both productive and allocative efficiency gains. Our research contributes to accurate monitoring of market power and improved automated mitigation. Although we focus on power markets, our findings are applicable to monitoring of renewable energy tenders or market power surveillance in rail and air traffic.

CRENet: Crowd region enhancement network for multi-person 3D pose estimation

Recovering multi-person 3D poses from a single image is a challenging problem due to inherent depth ambiguities, including root-relative depth and absolute root depth. Current bottom-up methods show promising potential to mitigate absolute root depth ambiguity through explicitly aggregating global contextual cues. However, these methods treat the entire image region equally during root depth regression, ignoring the negative impact of irrelevant regions. Moreover, they learn shared features for both depths, each of which focuses on different information. This sharing mechanism may result in negative transfer, thus diminishing root depth prediction accuracy. To address these challenges, we present a novel bottom-up method, Crowd Region Enhancement Network (CRENet), incorporating a Feature Decoupling Module (FDM) and a Global Attention Module (GAM). FDM explicitly learns the discriminative feature for each depth through adaptively recalibrating its channel-wise responses and fusing multi-level features, which makes the model focus on each depth prediction separately and thus avoids the adverse effect of negative transfer. GAM highlights crowd regions while suppressing irrelevant regions using the attention mechanism and further refines the attention regions based on the confidence measure about the attention, which is beneficial to learn depth-related cues from informative crowd regions and facilitate root depth estimation. Comprehensive experiments on benchmarks MuPoTS-3D and CMU Panoptic demonstrate that our method outperforms the state-of-the-art bottom-up methods in absolute 3D pose estimation and is applicable to in-the-wild images, which also indicates that learning depth-specific features and suppressing the noise signals can significantly benefit multi-person absolute 3D pose estimation.

Addressing maximization bias in reinforcement learning with two-sample testing

Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can sometimes lead to dramatic performance decreases or even complete algorithmic failure. We frame the bias problem statistically and consider it an instance of estimating the maximum expected value (MEV) of a set of random variables. We propose the T-Estimator (TE) based on two-sample testing for the mean, that flexibly interpolates between over- and underestimation by adjusting the significance level of the underlying hypothesis tests. We also introduce a generalization, termed K-Estimator (KE), that obeys the same bias and variance bounds as the TE and relies on a nearly arbitrary kernel function. We introduce modifications of Q-Learning and the Bootstrapped Deep Q-Network (BDQN) using the TE and the KE, and prove convergence in the tabular setting. Furthermore, we propose an adaptive variant of the TE-based BDQN that dynamically adjusts the significance level to minimize the absolute estimation bias. All proposed estimators and algorithms are thoroughly tested and validated on diverse tasks and environments, illustrating the bias control and performance potential of the TE and KE.

Agreement and Consistency of Absolute and Relative Corticospinal Stimulus-Response Curves for Upper, Lower, and Axial Musculature in Healthy Adults.

To assess the agreement and consistency of absolute and relative stimulus-response curve (SRC) parameter estimates for upper extremity, lower extremity, and axial muscles. Thirty (15 W, age: 27.0 ± 6.3 y, height: 171.9 ± 8.9 cm, weight: 80.2 ± 19.3 kg) healthy adults completed absolute (5% to 100% stimulator output) and relative (65% to 160% motor threshold) SRCs of the first dorsal interosseous, vastus lateralis, and rectus abdominis during submaximal isometric contractions. Mean motor-evoked potential amplitudes were fit with nonlinear regression to derive MEPmax, V50, and slope. Absolute agreement and consistency were assessed with ICCs, Cronbachs alphas, and Bland-Altman plots. Independent t-tests were used to examine differences in motor threshold, physical activity, strength, and muscle activity among participants with valid and invalid SRC parameters. Absolute and relative SRCs displayed good agreement and consistency for MEPmax and V50 but not slope. Motor thresholds were lower among participants with valid absolute SRCs for the rectus abdominis and vastus lateralis. Motor threshold, physical activity, strength, and muscle activity did not differ among those with valid and invalid parameters for all relative SRCs and absolute SRCs for the first dorsal interosseous. Absolute and relative SRCs produce similar MEPmax and V50 estimates in the first dorsal interosseous, vastus lateralis, and rectus abdominis. The validity of absolute and relative SRC results may differ depending on individual characteristics and tested muscles.