Biased Distribution Research Articles

Evaluation of Fengyun-4B Satellite Temperature Profile Products Using Radiosonde Observations and ERA5 Reanalysis over Eastern Tibetan Plateau

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region.

Genomic architecture in social insects is more strongly associated with phylogeny than social behavior

Abstract The evolution of sociality in insects has been predicted to reduce effective population sizes, in turn leading to changes in genome architecture, including higher recombination rates, larger genomes, increased GC-biased gene conversion (gBGC), and greater intragenomic variation in GC content to maintain castes through differential methylation. As the number of sequenced insect genomes continues to grow, it remains an open question which, if any, of these genomic features are consistent across social insect genomes. A major challenge to determining such commonalities has been the lack of phylogenetically controlled analyses across independent origins of sociality. Of the 15 Hymenoptera species for which recombination rate was available, social species had higher rates of recombination. Next, we conducted a broader analysis of genome architecture by analyzing genome assemblies for 435 species of Hymenoptera and 8 species of Blattodea to test if GC content, genome size, distribution of CpG sites or codon bias repeatedly differed between social and nonsocial species. Overall, there was little support for predictable changes in genome architecture associated with sociality across Hymenoptera, after accounting for phylogenetic relationships. However, we found a significant negative relationship between sociality and GC content within the family Apidae and a significant negative relationship between sociality and genome size within the family Halictidae. In all, these results suggest that unique origins of social behavior may produce unique trends in genomic architecture. Our study highlights the need to examine genome architecture across independent origins of social behavior.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Fostering a Synergistic Approach to Curriculum Development and TVET-Engineering Qualifications

The involvement of various stakeholders, aligning diverse interests, and evolving industry requirements are some areas that need careful consideration. The dynamics of power and authority between collaborating partners can affect the efficacy of the collaborative relationship; hence, further study is required to identify obstacles in such partnerships and propose solutions for overcoming them. This study aims to build a collaboration paradigm between the TVET engineering sector and the industry, emphasizingcurriculum development and TVET engineering credentials. This quantitative study employs a survey to collect information from 115 Malaysian technologists in three fields. Simple random selection ensures accurate demographic representation and an even distribution of bias. Four experts developed and validated a questionnaire. Data was evaluated using PLS-SEM 3.3.9 software, algorithms, bootstrapping, and blindfolding techniques. The measurement model evaluates the items' precision under validity and reliability requirements. The results indicate a significant relationship between the certification system and curriculum development. The model has a high predictive power value with a significant contribution from the free extract. The influence of size is likewise considerable, but the effect of curriculum TVET is minor. The findings provide valuable insights into the predictive validity of the model and its possible curriculum development applications. Adeliberate effort is required to overcome obstacles such as lack of coordination, scarce resources, and low public awareness.

Uncovering the first-infall history of the LMC through its dynamical impact in the Milky Way halo

ABSTRACT The gravitational interactions between the LMC and the Milky Way cause dynamical perturbations in the MW halo, leading to biased distributions of stellar density and kinematics. We run 50 high-resolution N-body simulations exploring varying masses and halo shapes of the MW and LMC to study the evolution of LMC-induced perturbations. By measuring mean velocities of simulated halo stars, we identify a discontinuity between the first-infall and second-passage scenarios of the LMC’s orbital history. In the first-infall, the Galactocentric latitudinal velocity hovers around 16 km s$^{-1}$ for stars at 50–100 kpc, while it subsides to about 8 km s$^{-1}$ in the second-passage scenario. We demonstrate that, this reduced perturbation magnitude in the second-passage scenario is mainly due to the short dynamical times of the Galactic inner halo and the lower velocity of the LMC during its second infall into the MW. Using a subset of $\sim 1100$ RR Lyrae stars located in the outer halo (50 kpc $\le R_{\mathrm{GC}}\lt $ 100 kpc) with precise distance estimates from Gaia, we find the mean latitudinal velocity ($v_{b}$) in the Galactocentric frame to be $\langle v_{b} \rangle =18.1 \pm 4.1$ km s$^{-1}$. The observation supports the first-infall scenario with a massive LMC ($\sim$$2.1 \times 10^{11} \, \mathrm{M}_{\odot }$) at infall, an oblate MW halo with a virial mass $M_{200}\lt 1.4\times 10^{12}\,\mathrm{M}_{\odot }$ and a flattening parameter $q\gt 0.7$. Our study indicates that LMC-induced kinematic disturbances can reveal its orbital history and key characteristics, as well as those of the MW. This approach shows promise in helping determine fundamental parameters of both galaxies.

Which is better? Taxonomy induction with learning the optimal structure via contrastive learning

A taxonomy represents a hierarchically structured knowledge graph that forms the infrastructure for various downstream applications, including recommender systems, web search, and question answering. The exploration of automated induction from text corpora has yielded notable taxonomies such as CN-probase, CN-DBpedia, and Zhishi.schema. Despite these efforts, existing taxonomies still face two critical issues that result in sub-optimal hierarchical structures. On the one hand, commonly observed taxonomies exhibit a coarse-grained and “flat” structure, stemming from a noticeable lack of diversity in both nodes and edges. This limitation primarily originates from the biased and homogeneous data distribution. On the other hand, the semantic granularity among “siblings” within these taxonomies remains inconsistent, presenting a challenge in accurately and comprehensively identifying hierarchical relations. To address these issues, this study introduces a novel taxonomy induction framework composed of three meticulously designed components. Initially, we established a seed schema by leveraging statistical information from external data sources as distant supervision to append nodes and edges containing “generic semantics”, thereby rectifying biased data distributions. Subsequently, a clustering algorithm is employed to group the nodes based on their similarities, followed by a refinement operation of the hierarchical relations among these nodes. Building on this seed schema, we propose a fine-grained contrastive learning method in the expansion module to strengthen the utilization of taxonomic structures, consequently boosting the precision of query-anchor matching. Finally, we meticulously scrutinized the hierarchical relations between each query and its siblings to ensure the integrity of the constructed taxonomy. Extensive experiments on real-world datasets validated the efficacy of our proposed framework for constructing well-structured taxonomies.

True random number generation based on temporal fluctuations of abalone shell coherent random lasers.

The output modes of random lasers exhibit randomness, making them a potential high-quality physical entropy source for generating random numbers. In this paper, we controlled a low-cost and easily fabricated abalone shell random laser, generating forward and backward coherent random lasers simultaneously in a single channel, resulting in highly diverse mode variations. After post-processing steps such as third-order difference calculations and exclusive-or (XOR) logic operations, we generated a random number sequence for the first time, to the best of our knowledge, based on the temporal fluctuations of biomimetic random laser coherent modes. The instantaneous generation rate reached a preliminary 40 Gbps. Moreover, the random bits satisfy requirements such as random distribution, independence, and absence of bias, successfully passing the NIST SP800-22 standard test, confirming the high quality of the random number sequence.

Rapid Impact Crater Relaxation Caused by an Insulating Methane Clathrate Crust on Titan

Titan’s impact craters are hundreds of meters shallower than expected, compared to similar-sized craters on Ganymede. Only 90 crater candidates have been identified, the majority of which have low certainty of an impact origin. Many processes have been suggested to shallow, modify, and remove Titan’s craters, including fluvial erosion by liquid from rainfall, aeolian sand infill, and topographic relaxation induced by insulating sand infill. Here we propose an additional mechanism: topographic relaxation due to an insulating methane clathrate crustal layer in Titan’s upper ice shell. We use finite element modeling to test whether a clathrate crust 5, 10, 15, or 20 km thick could warm the ice shell and relax craters to their currently observed depths or remove them completely. We model the viscoelastic evolution of crater diameters 120, 100, 85, and 40 km, with two initial depths based on depth−diameter trends of Ganymede’s craters. We find that all clathrate crustal thicknesses result in rapid topographic relaxation, despite Titan’s cold surface temperature. The 5 km thick clathrate crust can reproduce nearly all of the observed shallow depths, many in under 1000 yr. A 10 km thick crust can reproduce the observed depths of the larger craters over geologic timescales. If relaxation is the primary cause of the shallow craters, then the clathrate thickness is likely 5–10 km thick. Topographic relaxation alone cannot remove craters; crater rims and flexural moats remain. To completely remove craters and reproduce the observed biased crater distribution, multiple modification processes must act together.

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.

Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach

Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach

Importance sampling for counting statistics in one-dimensional systems.

In this paper, we consider the problem of numerical investigation of the counting statistics for a class of one-dimensional systems. Importance sampling, the cornerstone technique usually implemented for such problems, critically hinges on selecting an appropriate biased distribution. While an exponential tilt in the observable stands as the conventional choice for various problems, its efficiency in the context of counting statistics may be significantly hindered by the genuine discreteness of the observable. To address this challenge, we propose an alternative strategy, which we call importance sampling with the local tilt. We demonstrate the efficiency of the proposed approach through the analysis of three prototypical examples: a set of independent Gaussian random variables, Dyson gas, and symmetric simple exclusion process with a steplike initial condition.

Optimal Internality Taxation of Product Attributes

This paper explores how a benevolent policymaker should optimally tax (or subsidize) product attributes when consumers are behaviorally biased. We demonstrate that market choices are informative about biases, which can be exploited for targeting biased consumers via a nonlinear tax schedule. We show that the properties of this schedule depend on few parameters of the joint distribution of consumer valuations and biases. Furthermore, we provide a novel justification for behaviorally motivated product standards and derive when a combination of taxes and standards is optimal. We illustrate our findings based on a numerical example from the lightbulb market. (JEL D82, D91, H21, H25, L69)

A variational Bayesian based robust filter for unknown measurement bias and inaccurate noise statistics

In many practical fields, the unknown time-varying measurement biases (additive and multiplicative bias) and heavy-tailed measurement noise caused by some unpredictable anomalous behaviors may degrade the performance of conventional Kalman filter seriously. To solve the state estimation problem of systems with time-varying measurement biases and heavy-tailed measurement noise, this paper proposes a new variational Bayesian (VB) based robust filter. Firstly, the non-Gaussian measurement likelihood probability density function (ML-PDF) with multiplicative and additive measurement bias is built. Then, the conjugate prior distributions for unknown bias and noise scale parameters are selected, and the VB method is utilized to jointly infer the system state, unknown measurement biases and inaccurate measurement noise covariance matrix. Finally, a VB based robust filter is derived and its effectiveness is verified by the numerical simulations.

VLM-guided Explicit-Implicit Complementary novel class semantic learning for few-shot object detection

Few-shot object detection (FSOD) aims at learning a novel class object detector with abundant base class samples and a limited number of novel class samples. Some recent methods assume that base class images contain unlabeled novel class instances and mine these instances to address the data scarcity of novel classes. Despite achieving competitive results, these methods face two issues: First, mined instances are sampled around the provided few-shot instances, establishing biased data distributions to represent novel classes. Second, the rich novel class semantics contained in base class instances are largely ignored. Benefits from vision-language models (VLMs), we propose a VLM-guided Explicit-Implicit Complementary novel class semantic learning (VEIC) method for FSOD. VEIC incorporates a VLM-cooperated sample mining module that leverages the powerful semantic alignment capability of VLMs to mine representative unlabeled explicit novel instances. In addition, for implicit novel instances (ambiguous base instances contain novel class semantics), we employ a spatial-wise feature re-expression module to capture prominent novel class local patches. Then a dynamic class-transition label assign strategy is proposed to learn from implicit novel instances by utilizing deep network memorization, where implicit novel instances are regarded more as novel classes during the early training and later regarded more as base classes. Experimentally, learning novel class semantics from two complementary types of instances reaches the best performance. Furthermore, learning exclusively from implicit novel instances extends the pseudo-labeling method to a practical scenario where base class images do not contain unlabeled explicit novel instances.

Weak lensing mass bias and the alignment of centre proxies

ABSTRACT Galaxy cluster masses derived from observations of weak lensing suffer from a number of biases affecting the accuracy of mass-observable relations calibrated from such observations. In particular, the choice of the cluster centre plays a prominent role in biasing inferred masses. In the past, empirical miscentring distributions have been used to address this issue. Using hydrodynamic simulations, we aim to test the accuracy of weak lensing mass bias predictions based on such miscentring distributions by comparing the results to mass biases computed directly using intracluster medium (ICM)-based centres from the same simulation. We construct models for fitting masses to both centred and miscentred Navarro–Frenk–White profiles of reduced shear, and model the resulting distributions of mass bias with normal and lognormal distributions. We find that the standard approach of using miscentring distributions leads to an overestimation of cluster masses at levels of between 2 per cent and 6 per cent when compared to the analysis in which actual simulated ICM centres are used, even when the underlying miscentring distributions match in terms of the miscentring amplitude. While we find that neither lognormal nor normal distributions are generally reliable for accurately modelling the shapes of the mass bias distributions, both models can serve as reasonable approximations in practice.

Causality-inspired crop pest recognition based on Decoupled Feature Learning.

Ensuring the efficient recognition and management of crop pests is crucial for maintaining the balance in global agricultural ecosystems and ecological harmony. Deep learning-based methods have shown promise in crop pest recognition. However, prevailing methods often fail to address a critical issue: biased pest training dataset distribution stemming from the tendency to collect images primarily in certain environmental contexts, such as paddy fields. This oversight hampers recognition accuracy when encountering pest images dissimilar to training samples, highlighting the need for a novel approach to overcome this limitation. We introduce the Decoupled Feature Learning (DFL) framework, leveraging causal inference techniques to handle training dataset bias. DFL manipulates the training data based on classification confidence to construct different training domains and employs center triplet loss for learning class-core features. The proposed DFL framework significantly boosts existing baseline models, attaining unprecedented recognition accuracies of 95.33%, 92.59%, and 74.86% on the Li, DFSPD, and IP102 datasets, respectively. Extensive testing on three pest datasets using standard baseline models demonstrates the superiority of DFL in pest recognition. The visualization results show that DFL encourages the baseline models to capture the class-core features. The proposed DFL marks a pivotal step in mitigating the issue of data distribution bias, enhancing the reliability of deep learning in agriculture. © 2024 Society of Chemical Industry.

Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna

Abstract Large-scale databases are crucial for macroecology research, yet not entirely bias-free. Studying the biogeography of metazoan microfauna utilizing such databases is challenging, and added biases can further hinder them. Incidence data of tardigrade species from Central and South America are geographically biased, comprising mostly species erroneously considered cosmopolitan. The impact of these biases on macroecological studies is still unknown. This paper evaluates how biased distribution records affect understanding of non-marine tardigrade distribution patterns in the Neotropical region. Using two datasets (including and excluding dubious records of allegedly cosmopolitan species), we assess how well biogeographic regions are sampled, estimate tardigrade species’ richness, and analyse whether including dubious records can alter our comprehension of their macroecology. We demonstrate how biogeographic regions are still not fully sampled, and that including unreliable records influences how many species remain to be discovered. Since records of ‘false cosmopolitan species’ represent most records, their removal increases uncertainty while yielding distribution patterns more likely to be accurate. Disregarding untrustworthy distribution records of ‘false cosmopolitan species’ is the first step to better understanding tardigrade macroecology in the Neotropics, and probably worldwide. However, additional, preferably systematic sampling is required before we can infer general tardigrade biodiversity patterns in under-sampled regions.

Stochastic characterization of navigation strategies in an automated variant of the Barnes maze.

Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.

Regulatory role of Mycobacterium tuberculosis MtrA on dormancy/resuscitation revealed by a novel target gene-mining strategy.

The unique dormancy of Mycobacterium tuberculosis plays a significant role in the major clinical treatment challenge of tuberculosis, such as its long treatment cycle, antibiotic resistance, immune escape, and high latent infection rate. To determine the function of MtrA, the only essential response regulator, one strategy was developed to establish its regulatory network according to high-quality genome-wide binding sites. The complex modulation mechanisms were implied by the strong bias distribution of MtrA binding sites in the noncoding regions, and 32.7% of the binding sites were located inside the target genes. The functions of 288 potential MtrA target genes predicted according to 294 confirmed binding sites were highly diverse, and DNA replication and damage repair, lipid metabolism, cell wall component biosynthesis, cell wall assembly, and cell division were the predominant pathways. Among the 53 pathways shared between dormancy/resuscitation and persistence, which accounted for 81.5% and 93.0% of the total number of pathways, respectively, MtrA regulatory genes were identified not only in 73.6% of their mutual pathways, but also in 75.4% of the pathways related to dormancy/resuscitation and persistence respectively. These results suggested the pivotal roles of MtrA in regulating dormancy/resuscitation and the apparent relationship between dormancy/resuscitation and persistence. Furthermore, the finding that 32.6% of the MtrA regulons were essential in vivo and/or in vitro for M. tuberculosis provided new insight into its indispensability. The findings mentioned above indicated that MtrA is a novel promising therapeutic target for tuberculosis treatment since the crucial function of MtrA may be a point of weakness for M. tuberculosis.

Shift Detection Comparison For Sized Biased And Area Biased Exponential Distributions Based Control Charts

Probability distribution models are very helpful to solving many real-life problems. The development of new or extensions to an existing model is a very attractive area of research. Among many types of probability distribution models, weighted distributions are becoming more important for modeling data. Weighted distributions can help discover defects in manufacturing and outliers by weighting significant observations. Control charts are the most important tools to monitor process efficiency. In recent decades, researchers have focused on weighted distributions, especially those with size and area biased exponential distribution. This research introduces a novel control chart for a characteristic following a size-biased and area-biased exponential distribution to emphasize the relevance of weighted distributions. Charts are effectively determines size-biased and area-biased exponential distribution control chart limits also Monte Carlo simulations are used to assess different Average Run Length (ARL) values.