Robust Analysis Framework Research Articles

Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization.

Dysregulation of microRNAs (miRNAs) is closely associated with refractory human diseases, and the identification of potential associations between small molecule (SM) drugs and miRNAs can provide valuable insights for clinical treatment. Existing computational techniques for inferring potential associations suffer from limitations in terms of accuracy and efficiency. To address these challenges, we devise a novel predictive model called RPCA$\Gamma $NR, in which we propose a new Robust principal component analysis (PCA) framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and design an Augmented Lagrange Multiplier method to optimize it, thereby deriving the association scores. The Gaussian Interaction Profile Kernel Similarity is calculated to capture the similarity information of SMs and miRNAs in known associations. Through extensive evaluation, including Cross Validation Experiments, Independent Validation Experiment, Efficiency Analysis, Ablation Experiment, Matrix Sparsity Analysis, and Case Studies, RPCA$\Gamma $NR outperforms state-of-the-art models concerning accuracy, efficiency and robustness. In conclusion, RPCA$\Gamma $NR can significantly streamline the process of determining SM-miRNA associations, thus contributing to advancements in drug development and disease treatment.

Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

Online product reviews have become a valuable resource for consumers seeking detailed information and making informed choices. The process of automatically extracting sentiment or opinions from these reviews heavily relies on sentiment analysis, a branch of Natural Language Processing (NLP). This research article focuses on sentiment categorization in online product evaluations, utilizing innovative techniques for mining consumer opinions. The project aims to establish a robust framework for sentiment analysis that accurately classifies emotions expressed in these reviews. The proposed system incorporates advanced deep learning and machine learning methods to enhance data classification and extract fine-grained sentiment information. The study addresses the unique challenges of sentiment analysis in the context of online product evaluations, including polarity changes, sarcasm, and domain-specific sentiment expressions, which often pose significant obstacles to precise sentiment classification. The approach combines feature engineering and deep learning techniques, extracting lexical, syntactic, and semantic features such as part-of-speech tags, n-grams, sentiment lexicons, and word embeddings from the review texts. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed as sophisticated neural network architectures to leverage these features, creating robust representations and capturing contextual information. The suggested architecture is extensively evaluated on a large dataset of online product reviews, demonstrating superior performance in sentiment categorization compared to existing approaches. The evaluation encompasses various sentiment classes, measuring metrics like accuracy, recall, and F1-score, and assessing the framework's adaptability to different product domains. The study showcases the effectiveness of advanced machine learning and deep learning algorithms in sentiment categorization, advancing the field of sentiment analysis for online product evaluations. Businesses can gain valuable insights into customer sentiment and make well-informed decisions regarding product enhancements and marketing strategies by leveraging the proposed framework

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

Exploring walkability research trends based on systematic literature review (SLR) by applying PRISMA

PurposeThis study intends to analyze the trend of walkability research over two years (2021–2022) in several aspects, including the research problem, gap and aim underlying walkability issues, research setting, the scale of analysis, the type of data, the data collection tool, the analysis method and contribution.Design/methodology/approachPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is employed to conduct a systematic literature review from three leading databases including Web of Science (WoS), Scopus and Sage.FindingsFrom the review, it can be concluded that the research focuses on two aspects, namely, the built environment and people. The studies in this field are conducted at different scales of the built environment, from macroscale to microscale, including region, city, neighborhood and street-level studies that are conducted around the world including Asia, Europe and America which receive high attention from researchers. Meanwhile, the study of walkability in Australia and Africa gains less attention. It is revealed that there are four factors of walkability including land use and accessibility for macroscale which cover region, city and neighborhood, while street network and pedestrian infrastructure and facility are mainly analyzed for street-level. Most of the studies widely utilize a quantitative approach for analyzing physical walkability factors in a measurement-based approach by using certain tools/software, and a perception-based approach by using questionnaires.Research limitations/implicationsThe findings can assist to keep up with the rapid pace of scientific publications and help accelerate the understanding of particular ideas in the field of walkability for the planning and design practice and policy that can be used by researchers/academia, urban designers, urban planners, architects, engineers and policymakers. Also, this study hopefully could assist further research direction for the future and could add value to a more advanced way of understanding the research on walkability.Originality/valueThis research provides a robust framework for walkability trend analysis and a comprehensive understanding of the main ideas from the existing research on walkability worldwide.

Robust SSRL analysis framework for intervention strategy construction in CSCL environment

While the importance of socially shared regulatory of learning (SSRL) in computer-supported collaborative learning (CSCL) environments has increasingly been emphasized, a surge of research has been conducted to identify socially shared regulation activities and their transition sequences. However, little research has been carried out on constructing a systematic framework in which significant regulation activities and transition sequences can be mined automatically with high reliability. Moreover, though efforts have been made, the current SSRL analysis neither serves the construction of downstream teaching intervention strategy nor explores how SSRL analysis results can be utilized conversely for refining the intervention strategy. Based on advanced machine learning techniques, this work proposes a robust framework on SSRL analysis, aiming to find the optimal teaching intervention strategy to improve learners’ performance in CSCL by analyzing the SSRL process. In particular, our framework can automatically identify significant SSRL regulation activities along with high-contribution activity transition sequences. The proposed Ensemble Learning-based classification model with four distilled additional regulation activities can ensure the high reliability of our framework. The framework serves to construct a downstream teaching intervention strategy, while the strategy is updated and verified based on empirical and experimental statistical results within five rounds of iterative experiments. Extensive theoretical analysis and experimental results both confirm the effectiveness of our framework. Meanwhile, the attempt to leverage advanced machine learning algorithms to enhance SSRL analysis in this work can provide a nontrivial contribution to the literature.

Exact and stochastic methods for robustness analysis in the context of Imprecise Data Envelopment Analysis

We consider the problem of measuring the efficiency of decision-making units with a ratio-based model. In this perspective, we introduce a framework for robustness analysis that admits both interval and ordinal performances on inputs and outputs. The proposed methodology exploits the uncertainty related to the imprecise data and all feasible input/output weight vectors delimited through linear constraints. We offer methods for verifying the robustness of three types of outcomes: efficiency scores, efficiency preference relations, and efficiency ranks. On the one hand, we formulate mathematical programming models to compute the extreme, necessary, and possible results. On the other hand, we incorporate the stochastic analysis driven by the Monte Carlo simulations to derive the probability distribution of different outcomes. The framework is implemented in R and made available on open-source software. Its use is illustrated in two case studies concerning Chinese ports or industrial robots.

A computationally efficient moving object detection technique using tensor QR decomposition based TRPCA framework

Advancements in high-quality video cameras and the consequent capture of minute details of the scene have led the field of computer vision to remarkable heights. This paper develops a tensor QR decomposition-based approach for Moving Object Detection (MOD), which aims to reduce the computational complexity without disturbing the structural framework of the input video frames. The increased performance and efficiency of the proposed method lie in the usage of tensor QR decomposition along with l2,1 norm and l1/2 norm. It is designed on top of a tensor-based Robust Principal Component Analysis (TRPCA) framework. In addition, this work safeguards the variation along the spatio-temporal directions with the effective use of Tensor Total Variation (TTV) regularization. The results and the analysis prove that the proposed method improves the F-measure by 15%–45% and reduces the computational complexity by 75%–85% with respect to the counterparts.

A domain-theoretic framework for robustness analysis of neural networks

AbstractA domain-theoretic framework is presented for validated robustness analysis of neural networks. First, global robustness of a general class of networks is analyzed. Then, using the fact that Edalat’s domain-theoreticL-derivative coincides with Clarke’s generalized gradient, the framework is extended for attack-agnostic local robustness analysis. The proposed framework is ideal for designing algorithms which are correct by construction. This claim is exemplified by developing a validated algorithm for estimation of Lipschitz constant of feedforward regressors. The completeness of the algorithm is proved over differentiable networks and also over general position${\mathrm{ReLU}}$networks. Computability results are obtained within the framework of effectively given domains. Using the proposed domain model, differentiable and non-differentiable networks can be analyzed uniformly. The validated algorithm is implemented using arbitrary-precision interval arithmetic, and the results of some experiments are presented. The software implementation is truly validated, as it handles floating-point errors as well.

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

Automated vehicles are a revolutionary step in mobility, providing a safe and convenient riding experience while keeping the human-driving task minimal to none. Therefore, these intelligent vehicles are equipped with sophisticated perception sensors (e.g., cameras and radars), high-performance computers, artificial intelligence (AI)-driven algorithms, and connectivity with other internet-of-things (IoT) devices. This makes autonomous vehicles (AVs) a special kind of cyber-physical system (CPS) that is moving at speed in highly interactive and dynamic environments (e.g., public roads). Thus, AV is a potential target for cyber attackers to weaponize, compromising safety and mobility on the road. The first step in addressing this problem is to have a robust threat modeling framework that can address the evolving cyber-physical threats, especially to AV applications. In this regard, two areas are studied in this paper: the common practice of threat modeling in automotive and the ISO/SAE 21434 standard, and sensors and machine learning (ML) algorithms for AV perception systems and potential cyber-physical attacks. A comparative threat analysis for an AV perception system with the ISO/SAE 21434 standard and a system-theoretic process analysis for security (STPA-Sec) approach is also demonstrated in this paper. Based on the analysis, this paper proposes a robust threat analysis and risk assessment framework with mathematical modeling to identify cyber-physical threats to AV perception systems that are critical for the driving behaviors and complex interactions of AVs in their operational design domain.

Robustness Analysis on Graph Neural Networks Model for Event Detection

Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial for many real-world applications. Most of the current SOTA (state-of-the-art) models for ED are based on Graph Neural Networks (GNN). However, a few studies focus on the issue of GNN-based ED models’ robustness towards text adversarial attacks, which is a challenge in practical applications of EDs that needs to be solved urgently. In this paper, we first propose a robustness analysis framework for an ED model. Using this framework, we can evaluate the robustness of the ED model with various adversarial data. To improve the robustness of the GNN-based ED model, we propose a new multi-order distance representation method and an edge representation update method based on attention weights, then design an innovative model named A-MDL-EEGCN. Extensive experiments illustrate that the proposed model can achieve better performance than other models both on original data and various adversarial data. The comprehensive robustness analysis according to experimental results in this paper brings new insights into the evaluation and design of a robust ED model.

Exploring the robustness of public transportation system on augmented network: A case from Nanjing China

A highly robust urban public transportation network is the foundation for the stable and sustainable development of a city. Managers may benefit from a reasonable investigation into the robustness of the public transportation system to recognize and deliver improved public transportation services. This paper developed a robustness analysis framework for public transportation systems on augmented network (AN). Specifically, an AN was first built to treat the public transit network from a multi-layer perspective. The AN carefully considers the boarding, alighting, riding and transfer behaviors, and develops cost functions under the congestion effect, thus sufficiently simulating the real passengers’ behaviors. Then, the user equilibrium (UE) assignment model, a model widely used for passenger flow forecasting, is applied to guide the robustness analysis under cascading failures. Finally, a real large-scale bus–rail hybrid network in Nanjing is used as a case study. The results show that the initial station capacities are significant to the impact of the cascading failure, from which we conclude that a larger initial capacity of stations can prevent the cascading failures. By investigating the relationship between passenger flow and cascade size, the comparative results further explain the mechanisms of cascading failures. Thanks to the AN and UE, three types of travel times can be compared to reflect the quality of public passenger trips, which should provide traffic managers with pertinent advice for improving service.

AN ADVERSARIAL ROBUSTNESS ANALYSIS FRAMEWORK FOR CLASSIFICATION MODEL

In the ML/DL algorithms developed by researchers/developers, the only constraint faced by them is the robustness of the model. The model may be experimented with certain data sets but for the model to get tested with all the permutation and combination of data sets under different conditions is a challenge. For the model to give efficient and accurate results under all circumstances, the users are demanding a more robust model that has been analyzed thoroughly. We aim to create an algorithm that is unique, efficient and solves a real-world problem and build an Adversarial Robustness analysis framework that works on reinforcement learning, applies utility metrics of attacks and defense to give a comprehensive robustness report

A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages.

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

Development and Fidelity Assessment of Potential Flow based Framework for Aerodynamic Modeling of High Lift Devices

High lift devices play a vital role in dictating the accelerated performance of an aircraft for different flight phases such as takeoff, landing, and aerobatic maneuvers. The aerodynamic design of high lift devices for any particular aircraft is an iterative process and is achieved through extensive aerodynamic Analysis of the aircraft for various flap configurations. Computational Fluid Dynamics (CFD) and Wind tunnel testing are highly effective techniques for performing the required Analysis, yet they have high computational costs and time. To overcome this shortcoming, a robust framework based on potential flow solver (PFS) and geometry parameterization is required without compromising the fidelity of the Analysis. This research aims to develop a highly robust aerodynamic analysis framework based on the Vortex Lattice Method (VLM) coupled with Polhamus Suction Analogy and parametric modeling of high lift devices. The fidelity of the framework is validated through experimental testing and is quantified by developing a fidelity assessment matrix. It is established that the computational cost of CFD has been reduced three times with only a 10% to 20% loss in accuracy when the developed framework is used. The developed PFS framework gives results from 80% to 90%. The framework results for a reference aircraft are thoroughly compared with CFD analyses. The framework provides values that agree with corresponding CFD analyses in a fraction of the time.

Smartphone-based measurements of the optical properties of snow.

Snow is a highly complex medium composed of ice crystals of various shapes and sizes. Knowledge of its intrinsic optical properties such as scattering and absorption coefficients is tantamount to radiative transfer models in climate research. The absorption coefficient, in particular, allows us to access information about light-absorbing particles contained in the snow. In contrast to snow's apparent properties such as the albedo, measuring the intrinsic properties is challenging. Here, we present a simple apparatus that can measure bulk optical properties of snow using readily available components and a smartphone camera, and a robust diffuse-optical framework for data analysis. We demonstrate the instrument both on scattering phantoms with known scattering and absorption coefficients and in the field. Its low cost, simplicity, and portability uniquely qualify this setup for large-scale field work, undergraduate education, and citizen science.

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19.

Covid-19 has become a pandemic that affects lots of individuals daily, worldwide, and, particularly, the widespread disruption in numerous countries, namely, the US, Italy, India, Saudi Arabia. The timely detection of this infectious disease is mandatory to prevent the quick spread globally and locally. Moreover, the timely detection of COVID-19 in the coming time is significant to well cope with the disease control by Governments. The common symptoms of COVID are fever as well as dry cough, which is similar to the normal flu. The disease is devastating and spreads quickly, which affects individuals of all ages, particularly, aged people and those with feeble immune systems. There is a standard method employed to detect the COVID, namely, the real-time polymerase chain reaction (RT-PCR) test. But this method has shortcomings, i.e., it takes a long time and generates maximum false-positive cases. Consequently, we necessitate to propose a robust framework for the detection as well as for the estimation of COVID cases globally. To achieve the above goals, we proposed a novel technique to analyze, predict, and detect the COVID-19 infection. We made dependable estimates on significant pandemic parameters and made predictions of infection as well as potential washout time frames for numerous countries globally. We used a publicly available dataset composed by Johns Hopkins Center for estimation, analysis, and predictions of COVID cases during the time period of 21 April 2020 to 27 June 2020. We employed a simple circulation for fast as well as simple estimates of the COVID model and estimated the parameters of the Gaussian curve, utilizing a parameter, namely, the least-square parameter curve fitting for numerous countries in distinct areas. Forecasts of COVID depend upon the potential results of Gaussian time evolution with a central limit theorem of data the Covid prediction to be justified. For gaussian distribution, the parameters, namely, extreme time and thickness are regulated using a statistical Y2 fit for the aim of doubling times after 21 April 2020. Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. We fed the extracted features into the proposed COVIDDetectorNet to detect COVID-19, viral pneumonia, and other lung infections. Our method obtained an accuracy of 96.51, 92.62, and 86.53% for two, three, and four classes, respectively. Experimental outcomes illustrate that our method is reliable to be employed for the forecast and detection of COVID-19 disease.

Ultra-deep whole genome bisulfite sequencing reveals a single methylation hotspot in human brain mitochondrial DNA

ABSTRACT While DNA methylation is established as a major regulator of gene expression in the nucleus, the existence of mitochondrial DNA (mtDNA) methylation remains controversial. Here, we characterized the mtDNA methylation landscape in the prefrontal cortex of neurological healthy individuals (n=26) and patients with Parkinson’s disease (n=27), using a combination of whole-genome bisulphite sequencing (WGBS) and bisulphite-independent methods. Accurate mtDNA mapping from WGBS data required alignment to an mtDNA reference only, to avoid misalignment to nuclear mitochondrial pseudogenes. Once correctly aligned, WGBS data provided ultra-deep mtDNA coverage (16,723 ± 7,711) and revealed overall very low levels of cytosine methylation. The highest methylation levels (5.49 ± 0.97%) were found on CpG position m.545, located in the heavy-strand promoter 1 region. The m.545 methylation was validated using a combination of methylation-sensitive DNA digestion and quantitative PCR analysis. We detected no association between mtDNA methylation profile and Parkinson’s disease. Interestingly, m.545 methylation correlated with the levels of mtDNA transcripts, suggesting a putative role in regulating mtDNA gene expression. In addition, we propose a robust framework for methylation analysis of mtDNA from WGBS data, which is less prone to false-positive findings due to misalignment of nuclear mitochondrial pseudogene sequences.

Determining allowable parametric uncertainty in an uncommon quadrotormodel for closed loop stability

In this article, control oriented uncertainty modeling of an uncommon quadrotor in hover is discussed. This quadrotor consists of two counter-rotating big rotors on longitudinal axis and two counter-rotating small tilt rotors on lateral axis. Firstly, approximate linear model of this vehicle around hover is obtained by using Newton--Euler formulation. Secondly, specific uncertainty is assigned to each parameter. Resulting uncertain model is converted into a linear fractional transformation framework for robustness analysis. Next, the most critical uncertain parameters in terms of robust stability in a proposed quadrotor model are investigated using $ \mu $ sensitivities. Finally, skewed-$ \mu $ analysis determines maximum possible uncertainty bounds for model parameters that are difficult to identify accurately.

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

Thethesanproject: properties of the intergalactic medium and its connection to reionization-era galaxies

ABSTRACTThe high-redshift intergalactic medium (IGM) and the primeval galaxy population are rapidly becoming the new frontier of extragalactic astronomy. We investigate the IGM properties and their connection to galaxies at z ≥ 5.5 under different assumptions for the ionizing photon escape and the nature of dark matter, employing our novel thesan radiation-hydrodynamical simulation suite, designed to provide a comprehensive picture of the emergence of galaxies in a full reionization context. Our simulations have realistic ‘late’ reionization histories, match available constraints on global IGM properties, and reproduce the recently observed rapid evolution of the mean free path of ionizing photons. We additionally examine high-z Lyman-α transmission. The optical depth evolution is consistent with data, and its distribution suggests an even-later reionization than simulated, although with a strong sensitivity to the source model. We show that the effects of these two unknowns can be disentangled by characterizing the spectral shape and separation of Lyman-α transmission regions, opening up the possibility to observationally constrain both. For the first time in simulations, thesan reproduces the modulation of the Lyman-α flux as a function of galaxy distance, demonstrating the power of coupling a realistic galaxy formation model with proper radiation hydrodynamics. We find this feature to be extremely sensitive on the timing of reionization, while being relatively insensitive to the source model. Overall, thesan produces a realistic IGM and galaxy population, providing a robust framework for future analysis of the high-z Universe.