Thin Nguyen
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View article: Integrative multi-omics framework for causal gene discovery in Long COVID
Integrative multi-omics framework for causal gene discovery in Long COVID Open
Long COVID, or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), affects an estimated 10–20% of COVID-19 patients and presents persistent multisystemic symptoms. Although demographic and clinical factors, such as age, sex, and comorbidit…
View article: Improving Olive Yield Prediction Using Landsat Multispectral Data and Advanced Ensemble Learning in Tunisia
Improving Olive Yield Prediction Using Landsat Multispectral Data and Advanced Ensemble Learning in Tunisia Open
Olive cultivation is a key agricultural activity in Mediterranean regions, yet its yield is highly sensitive to the impacts of climate change. Accurately predicting olive yield using optical remote sensing and data‐driven models remains a …
View article: TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID
TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID Open
Long COVID affects 10-40% of COVID-19 survivors, yet early detection remains challenging. We present TACO (TabPFN Augmented Causal Outcomes), a framework that uniquely combines causal inference with foundation models for presymptomatic Lon…
View article: Universal Multi-Domain Translation via Diffusion Routers
Universal Multi-Domain Translation via Diffusion Routers Open
Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen in training, limiting their practicality and excluding man…
View article: The complete genome sequence of the cluster BE1 <i>Streptomyces Bacteriophage Riptide</i> includes genes encoding ribosome-associated proteins
The complete genome sequence of the cluster BE1 <i>Streptomyces Bacteriophage Riptide</i> includes genes encoding ribosome-associated proteins Open
This study isolated bacteriophage Riptide , a BE1 cluster siphovirus, from soil using Streptomyces mirabilis NRRL B-2400. Riptide follows a lytic life cycle contains a genome length of 132,142 bp encoding 236 protein coding genes including…
View article: Now or Never: Exploring Factors Influencing Online Impulsive Buying Behavior on Fashion Brand Websites among Generation Z Consumers
Now or Never: Exploring Factors Influencing Online Impulsive Buying Behavior on Fashion Brand Websites among Generation Z Consumers Open
Recently, online shopping has become increasingly popular, and one of the most favored product categories is fashion. This study investigates the factors influencing impulsive purchasing on fashion brand websites among Generation Z in Hano…
View article: CausalPlan: Empowering Efficient LLM Multi-Agent Collaboration Through Causality-Driven Planning
CausalPlan: Empowering Efficient LLM Multi-Agent Collaboration Through Causality-Driven Planning Open
Large language model (LLM) agents-especially smaller, open-source models-often produce causally invalid or incoherent actions in collaborative tasks due to their reliance on surface-level correlations rather than grounded causal reasoning.…
View article: Bidirectional Diffusion Bridge Models
Bidirectional Diffusion Bridge Models Open
View article: Complete genome sequences of <i>Streptomyces</i> phages HazuAndZazu and Tubberson
Complete genome sequences of <i>Streptomyces</i> phages HazuAndZazu and Tubberson Open
Bacteriophages HazuAndZazu and Tubberson, belonging to the BI1 and BC1 subclusters, are Caudoviricetes with a siphoviral morphology that infect Streptomyces species. They have GC contents of 59.5% and 71.5%, and genomes 55,823 and 39,028 b…
View article: h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform
h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform Open
We introduce a theoretical framework for diffusion-based image editing by formulating it as a reverse-time bridge modeling problem. This approach modifies the backward process of a pretrained diffusion model to construct a bridge that conv…
View article: Bidirectional Diffusion Bridge Models
Bidirectional Diffusion Bridge Models Open
Diffusion bridges have shown potential in paired image-to-image (I2I) translation tasks. However, existing methods are limited by their unidirectional nature, requiring separate models for forward and reverse translations. This not only do…
View article: Causal Discovery via Bayesian Optimization
Causal Discovery via Bayesian Optimization Open
Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayes…
View article: Utilising causal inference methods to estimate effects and strategise interventions in observational health data
Utilising causal inference methods to estimate effects and strategise interventions in observational health data Open
Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though t…
View article: SUSTAINABLE CULTURAL HERITAGE TOURISM IN HUE CITY, VIETNAM: FOCUSING ON NGUYEN DYNASTY CEREMONIES
SUSTAINABLE CULTURAL HERITAGE TOURISM IN HUE CITY, VIETNAM: FOCUSING ON NGUYEN DYNASTY CEREMONIES Open
View article: Generating Realistic Tabular Data with Large Language Models
Generating Realistic Tabular Data with Large Language Models Open
While most generative models show achievements in image data generation, few are developed for tabular data generation. Recently, due to success of large language models (LLM) in diverse tasks, they have also been used for tabular data gen…
View article: Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows Open
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility constraint required in PNL models remains challenging in exist…
View article: Scalable Variational Causal Discovery Unconstrained by Acyclicity
Scalable Variational Causal Discovery Unconstrained by Acyclicity Open
Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represented in forms of directed acyclic graphs (DAGs). However, ex…
View article: A study on hybrid recommend system combined sentiment analysis with matrix factorization
A study on hybrid recommend system combined sentiment analysis with matrix factorization Open
Contemporary research endeavors have evinced a substantial interest in integrating heterogeneous data sources within unified recommendation system frameworks. Concomitantly, the conventional two-dimensional product-user rating matrix ubiqu…
View article: Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Reinforcement Learning for Causal Discovery without Acyclicity Constraints Open
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAGs) from observational data. However, the intricate acyclici…
View article: Scalable Variational Causal Discovery Unconstrained by Acyclicity
Scalable Variational Causal Discovery Unconstrained by Acyclicity Open
Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represented in forms of directed acyclic graphs (DAGs). However, ex…
View article: Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows Open
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility constraint required in PNL models remains challenging in exist…
View article: Constraining acyclicity of differentiable Bayesian structure learning with topological ordering
Constraining acyclicity of differentiable Bayesian structure learning with topological ordering Open
Distributional estimates in Bayesian approaches in structure learning have advantages compared to the ones performing point estimates when handling epistemic uncertainty. Differentiable methods for Bayesian structure learning have been dev…
View article: Root Cause Explanation of Outliers under Noisy Mechanisms
Root Cause Explanation of Outliers under Noisy Mechanisms Open
Identifying root causes of anomalies in causal processes is vital across disciplines. Once identified, one can isolate the root causes and implement necessary measures to restore the normal operation. Causal processes are often modelled as…
View article: Variational Flow Models: Flowing in Your Style
Variational Flow Models: Flowing in Your Style Open
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+σ_{t}X_{1} into a straight constant-speed (SC) flow, reminiscent of Rectified Fl…
View article: Root Cause Explanation of Outliers under Noisy Mechanisms
Root Cause Explanation of Outliers under Noisy Mechanisms Open
Identifying root causes of anomalies in causal processes is vital across disciplines. Once identified, one can isolate the root causes and implement necessary measures to restore the normal operation. Causal processes are often modelled as…
View article: Robust Estimation of Causal Heteroscedastic Noise Models
Robust Estimation of Causal Heteroscedastic Noise Models Open
Distinguishing the cause and effect from bivariate observational data is the foundational problem that finds applications in many scientific disciplines. One solution to this problem is assuming that cause and effect are generated from a s…
View article: Domain Generalisation via Risk Distribution Matching
Domain Generalisation via Risk Distribution Matching Open
We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distributions effectively highlight differences between training d…
View article: Heteroscedastic Causal Structure Learning
Heteroscedastic Causal Structure Learning Open
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover …
View article: Differentiable Bayesian Structure Learning with Acyclicity Assurance
Differentiable Bayesian Structure Learning with Acyclicity Assurance Open
Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achieving promising outcomes, most of these methods are still stru…
View article: Normalizing flows for conditional independence testing
Normalizing flows for conditional independence testing Open
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms, yet it remains a highly challenging problem due to dimensionality and complex relationships…