arXiv subjects

Minh Nguyen

Publications and source records attributed to Minh Nguyen.

At least 37 records · Page 2Linked to original sources

arXiv2025

GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments

We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs to design hierarchical or joint policies of multiple agents that control the movement of a real-life robot towards the goal in mapless environment, i.e. an environment where the map of the environment is not available apriori, and needs to be acquired through sensors. The sensors are invoked from the path learner, i.e. navigator, through active query responses to sensory agents, and along the motion path. GRL-SNAM differs from preemptive navigation algorithms and other reinforcement learning methods by relying exclusively on local sensory observations without constructing a global map. Our approach formulates path navigation and mapping as a dynamic shortest path search and discovery process using controlled Hamiltonian optimization: sensory inputs are translated into local energy landscapes that encode reachability, obstacle barriers, and deformation constraints, while policies for sensing, planning, and reconfiguration evolve stagewise via updating Hamiltonians. A reduced Hamiltonian serves as an adaptive score function, updating kinetic/potential terms, embedding barrier constraints, and continuously refining trajectories as new local information arrives. We evaluate GRL-SNAM on two different 2D navigation tasks. Comparing against local reactive baselines and global policy learning references under identical stagewise sensing constraints, it preserves clearance, generalizes to unseen layouts, and demonstrates that Geometric RL learning via updating Hamiltonians enables high-quality navigation through minimal exploration via local energy refinement rather than extensive global mapping. The code is publicly available on \href{https://github.com/CVC-Lab/GRL-SNAM}{Github}.

cs.LG↗

arXiv2025

Real-time time-dependent density functional theory for high-energy density physics

Electronic response properties of high-energy density (HED) systems influence planetary structure, drive evolution of fusion targets, and underpin diagnostics in laboratory astrophysics. Real-time time-dependent density functional theory (TDDFT) offers a versatile modeling framework capable of accurately predicting the dynamic response of HED materials -- including free-free, bound-free, and bound-bound contributions without requiring ad hoc state partitioning; capturing both collective and non-collective behavior; and applicable within the linear-response regime and beyond. We review the theoretical formalism of real-time TDDFT as applied to HED systems, provide a practical tutorial for computing relevant response properties (dynamic structure factors, conductivity, and stopping power), and comment on avenues for further development of this powerful computational method in service of HED science.

physics.plasm-ph↗

arXiv2025

Energy consumption assessment of a Virtual Reality Remote Rendering application over 5G networks

This paper investigates the energy implications of remote rendering for Virtual Reality (VR) applications within a real 5G testbed. Remote rendering enables lightweight devices to access high-performance graphical content by offloading computationally intensive tasks to Cloud-native Network Functions (CNFs) running on remote servers. However, this approach raises concerns regarding energy consumption across the various network components involved, including the remote computing node, the 5G Core, the Radio Access Network (RAN), and the User Equipment (UE). This work proposes and evaluates two complementary energy monitoring solutions, one hardware-based and one software-based, to measure energy consumption at different system levels. A VR remote renderer, deployed as CNF and leveraging the Media over QUIC (MoQ) protocol, is used as test case for assessing its energy footprint under different multimedia and network configurations. The results provide critical insights into the trade-off between energy consumption and performance of a real-world VR application running in a 5G environment.

cs.NI↗

arXiv2025

SpareCodeSearch: Searching for Code Context When You Have No Spare GPU

Retrieval-Augmented Generation (RAG) frameworks aim to enhance Code Language Models (CLMs) by including another module for retrieving relevant context to construct the input prompt. However, these retrieval modules commonly use semantic search, requiring substantial computational resources for training and hosting these embedded models, making them infeasible to integrate into lightweight applications such as in-IDE AI-based code completion. In this solution paper, we prove that using keyword-search is sufficient to retrieve relevant and useful code context inside large codebases, without the need for extensive GPU resources. The usefulness of code contexts found by our solution is demonstrated through their completion results on the Code Context Competition's benchmark, reaching 0.748 and 0.725 chRF scores on Kotlin and Python tracks, respectively.

cs.SE↗

arXiv2025

Characterizing and Optimizing Real-Time Optimal Control for Embedded SoCs

Resource-limited robots face significant challenges in executing computationally intensive tasks, such as locomotion and manipulation, particularly for real-time optimal control algorithms like Model Predictive Control (MPC). This paper provides a comprehensive design space exploration to identify optimal hardware computation architectures for these demanding model-based control algorithms. We profile and optimize representative architectural designs, including general-purpose scalar CPUs, vector processors, and specialized accelerators. By characterizing kernel-level benchmarks and end-to-end robotic scenarios, including a hardware-in-the-loop evaluation on a fabricated RISC-V multi-core vector SoC, we present a quantitative comparison of performance, area, and utilization across distinct architectural design points. Our findings demonstrate that targeted architectural modifications, coupled with deep software and system optimizations, enable up to 3.71x speedups for MPC, resulting in up to 27% system-level power reductions while completing robotic tasks. Finally, we propose a code generation flow designed to simplify the complex engineering effort required for mapping robotic workloads onto specialized architectures.

cs.RO↗

arXiv2025

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data

We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesian inference. Although conservative, dissipative, and port-Hamiltonian systems might share the same initial total energy of a closed system, it is challenging for a single Hamiltonian network model to capture the distinctive and varying motion dynamics and physics of a phase space, from sampled observational phase space trajectories. To address this complicated Hamiltonian manifold learning challenge, we extend sparse symplectic, random Fourier Gaussian processes learning with predictive successive numerical estimations of the Hamiltonian landscape, using a generalized form of state and conjugate momentum Hamiltonian dynamics, appropriate to different classes of conservative, dissipative and port-Hamiltonian physical systems. In addition to the kernelized evidence lower bound (ELBO) loss for data fidelity, we incorporate stability and conservation constraints as additional hyper-parameter balanced loss terms to regularize the model's multi-gradients, enforcing physics correctness for improved prediction accuracy with bounded uncertainty.

cs.LG↗

arXiv2025

VN-MTEB: Vietnamese Massive Text Embedding Benchmark

Vietnam ranks among the top countries in terms of both internet traffic and online toxicity. As a result, implementing embedding models for recommendation and content control duties in applications is crucial. However, a lack of large-scale test datasets, both in volume and task diversity, makes it tricky for scientists to effectively evaluate AI models before deploying them in real-world, large-scale projects. To solve this important problem, we introduce a Vietnamese benchmark, VN-MTEB for embedding models, which we created by translating a large number of English samples from the Massive Text Embedding Benchmark using our new automated framework. We leverage the strengths of large language models (LLMs) and cutting-edge embedding models to conduct translation and filtering processes to retain high-quality samples, guaranteeing a natural flow of language and semantic fidelity while preserving named entity recognition (NER) and code snippets. Our comprehensive benchmark consists of 41 datasets from six tasks specifically designed for Vietnamese text embeddings. In our analysis, we find that bigger and more complex models using Rotary Positional Embedding outperform those using Absolute Positional Embedding in embedding tasks. Datasets are available at HuggingFace: https://huggingface.co/collections/GreenNode/vn-mteb-68871433f0f7573b8e1a6686

cs.CL↗

arXiv2025

Knockout: A simple way to handle missing inputs

Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inference. Current popular solutions include marginalization, imputation, and training multiple models. Marginalization achieves calibrated predictions, but it is computationally expensive and only feasible for low dimensional inputs. Imputation may result in inaccurate predictions, particularly when high-dimensional data, such as images, are missing. Training multiple models, where each model is designed to handle different subsets of inputs, can work well but requires prior knowledge of missing input patterns. Furthermore, training and retaining multiple models can be costly. We propose an efficient method to learn both the conditional distribution using full inputs and the marginal distributions. Our method, Knockout, randomly replaces input features with appropriate placeholder values during training. We provide a theoretical justification for Knockout and show that it can be interpreted as an implicit marginalization strategy. We evaluate Knockout across a wide range of simulations and real-world datasets and show that it offers strong empirical performance.

cs.LG↗

arXiv2025

Automated Behaviour-Driven Acceptance Testing of Robotic Systems

The specification and validation of robotics applications require bridging the gap between formulating requirements and systematic testing. This often involves manual and error-prone tasks that become more complex as requirements, design, and implementation evolve. To address this challenge systematically, we propose extending behaviour-driven development (BDD) to define and verify acceptance criteria for robotic systems. In this context, we use domain-specific modelling and represent composable BDD models as knowledge graphs for robust querying and manipulation, facilitating the generation of executable testing models. A domain-specific language helps to efficiently specify robotic acceptance criteria. We explore the potential for automated generation and execution of acceptance tests through a software architecture that integrates a BDD framework, Isaac Sim, and model transformations, focusing on acceptance criteria for pick-and-place applications. We tested this architecture with an existing pick-and-place implementation and evaluated the execution results, which shows how this application behaves and fails differently when tested against variations of the agent and environment. This research advances the rigorous and automated evaluation of robotic systems, contributing to their reliability and trustworthiness.

cs.RO↗

arXiv2025

Chip Placement with Diffusion Models

Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2D chip. Because key performance metrics of the chip are determined by the placement, optimizing it is crucial. Existing learning-based methods typically fall short because of their reliance on reinforcement learning (RL), which is slow and struggles to generalize, requiring online training on each new circuit. Instead, we train a diffusion model capable of placing new circuits zero-shot, using guided sampling in lieu of RL to optimize placement quality. To enable such models to train at scale, we designed a capable yet efficient architecture for the denoising model, and propose a novel algorithm to generate large synthetic datasets for pre-training. To allow zero-shot transfer to real circuits, we empirically study the design decisions of our dataset generation algorithm, and identify several key factors enabling generalization. When trained on our synthetic data, our models generate high-quality placements on unseen, realistic circuits, achieving competitive performance on placement benchmarks compared to state-of-the-art methods.

cs.LG↗

arXiv2025

Constrained Design of a Binary Instrument in a Partially Linear Model

We study the question of how best to assign an encouragement in a randomized encouragement study. In our setting, units arrive with covariates, receive a nudge toward treatment or control, acquire one of those statuses in a way that need not align with the nudge, and finally have a response observed. The nudge can be modeled as a binary instrument if one assumes that it affects the response only via the treatment status. Our goal is to assign the nudge as a function of covariates in a way that best estimates the local average treatment effect (LATE). We assume a partially linear model, wherein the baseline model is non-parametric and the treatment term is linear in the covariates. Under this model, we outline a two-stage procedure to consistently and optimally estimate the LATE. Though the variance of the LATE is intractable, we derive a finite sample approximation and thus a design criterion to minimize. This criterion is convex, allowing for constraints that might arise for budgetary or ethical reasons. We prove conditions under which our solution asymptotically recovers the lowest true variance among all possible nudge propensities. A one-stage version of the algorithm is consistent but not necessarily optimal. We apply our method to a semi-synthetic example involving triage in an emergency department and find significant gains relative to a regression discontinuity design.

stat.ME↗

arXiv2025

Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL

This work addresses the challenge of enabling a team of quadrupedal robots to collaboratively tow a cable-connected load through cluttered and unstructured environments while avoiding obstacles. Leveraging cables allows the multi-robot system to navigate narrow spaces by maintaining slack when necessary. However, this introduces hybrid physical interactions due to alternating taut and slack states, with computational complexity that scales exponentially as the number of agents increases. To tackle these challenges, we developed a scalable and decentralized system capable of dynamically coordinating a variable number of quadrupedal robots while managing the hybrid physical interactions inherent in the load-towing task. At the core of this system is a novel multi-agent reinforcement learning (MARL)-based planner, designed for decentralized coordination. The MARL-based planner is trained using a centralized training with decentralized execution (CTDE) framework, enabling each robot to make decisions autonomously using only local (ego) observations. To accelerate learning and ensure effective collaboration across varying team sizes, we introduce a tailored training curriculum for MARL. Experimental results highlight the flexibility and scalability of the framework, demonstrating successful deployment with one to four robots in real-world scenarios and up to twelve robots in simulation. The decentralized planner maintains consistent inference times, regardless of the team size. Additionally, the proposed system demonstrates robustness to environment perturbations and adaptability to varying load weights. This work represents a step forward in achieving flexible and efficient multi-legged robotic collaboration in complex and real-world environments.

cs.RO↗

arXiv2024

Modular Probabilistic Models via Algebraic Effects

Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers.

cs.PL↗

arXiv2024

Effect Handlers for Programmable Inference

Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date. This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms' high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-a-vis monad transformers as a structuring mechanism for modular imperative algorithm design.

cs.PL↗

arXiv2024

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies

The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled timeseries data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over time. Longitudinal studies commonly track several biomarkers, which are likely governed by nonlinear dynamics that might have subject-specific idiosyncrasies and exhibit both direct and indirect causes. Furthermore, real-world longitudinal data often suffer from widespread missingness. GC methods are not well-suited to handle these issues. In this paper, we propose an approach named GLACIAL (Granger and LeArning-based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model. GLACIAL treats subjects as independent samples and uses the model's average prediction accuracy on hold-out subjects to probe causal links. Input dropout and model interpolation are used to efficiently learn nonlinear dynamic relationships between a large number of variables and to handle missing values respectively. Extensive simulations and experiments on a real longitudinal medical imaging dataset show GLACIAL beating competitive baselines and confirm its utility. Our code is available at https://github.com/mnhng/GLACIAL.

cs.LG↗

arXiv2024

Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning

Despite extensive research, time series classification and forecasting on noisy data remain highly challenging. The main difficulties lie in finding suitable mathematical concepts to describe time series and effectively separate noise from the true signals. Unlike traditional methods treating time series as static vectors or fixed sequences, we propose a novel framework that views each time series, regardless of length, as a realization of a continuous-time stochastic process. This mathematical approach captures dependencies across timestamps and detects hidden, time-varying signals within the noise. However, real-world data often involves multiple distinct dynamics, making it insufficient to model the entire process with a single stochastic model. To address this, we assign each dynamic a unique signature vector and introduce the concept of "most informative timestamps" to infer a sparse approximation of the individual dynamics from these vectors. The resulting model, called Motion Code, includes parameters that fully capture diverse underlying dynamics in an integrated manner, enabling simultaneous classification and forecasting of time series. Extensive experiments on noisy datasets, including real-world Parkinson's disease sensor tracking, demonstrate Motion Code's strong performance against established benchmarks for time series classification and forecasting.

cs.LG↗

arXiv2024

One STEP at a time: Language Agents are Stepwise Planners

Language agents have shown promising adaptability in dynamic environments to perform complex tasks. However, despite the versatile knowledge embedded in large language models, these agents still fall short when it comes to tasks that require planning. We introduce STEP, a novel framework designed to efficiently learn from previous experiences to enhance the planning capabilities of language agents in future steps. Concretely, STEP functions through four interconnected components. First, the Planner takes on the task, breaks it down into subtasks and provides relevant insights. Then the Executor generates action candidates, while the Evaluator ensures the actions align with learned rules from previous experiences. Lastly, Memory stores experiences to inform future decisions. In the ScienceWorld benchmark, our results show that STEP consistently outperforms state-of-the-art models, achieving an overall score of 67.4 and successfully completing 12 out of 18 tasks. These findings highlight STEP's potential as a framework for enhancing planning capabilities in language agents, paving the way for more sophisticated task-solving in dynamic environments.

cs.CL↗

arXiv2024

Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach

In this paper, we present a novel reinforcement learning framework designed to optimize molecular dynamics by focusing on the entire trajectory rather than just the final molecular configuration. Leveraging a stochastic version of Pontryagin's Maximum Principle (PMP) and Soft Actor-Critic (SAC) algorithm, our framework effectively explores non-convex molecular energy landscapes, escaping local minima to stabilize in low-energy states. Our approach operates in continuous state and action spaces without relying on labeled data, making it applicable to a wide range of molecular systems. Through extensive experimentation on six distinct molecules, including Bradykinin and Oxytocin, we demonstrate competitive performance against other unsupervised physics-based methods, such as the Greedy and NEMO-based algorithms. Our method's adaptability and focus on dynamic trajectory optimization make it suitable for applications in areas such as drug discovery and molecular design.

math.OC↗