arXiv subjects

Son Le

Publications and source records attributed to Son Le.

6 recordsLinked to original sources

arXiv2026

Amorphous and Nanocrystalline Topological Semimetal YPtBi/W/CoFeB Heterostructures for BEOL-Compatible Spin-Orbit Torque Devices

Spin-orbit torque (SOT) devices require spin-source materials that combine efficient charge-to-spin conversion with back-end-of-line (BEOL) thermal compatibility. Here, we show that YPtBi/W/CoFeB heterostructures deposited directly on Si/SiOx remain predominantly amorphous or weakly nanocrystalline from room temperature to 400 {\deg}C while preserving a large effective damping-like SOT response. Anomalous Hall and harmonic Hall measurements, together with X-ray diffraction, cross-sectional transmission electron microscopy, X-ray reflectivity, and electron energy-loss spectroscopy, show that the response does not correlate with bulk crystallization of YPtBi. Instead, the interfacial analysis indicates that the strongest trend of the spin Hall angle is associated with the chemistry of the upper YPtBi/W boundary: the effective SOT response tracks the integrated W concentration at that YPtBi surface. Meanwhile, a two-spin source analysis shows that the Pt-W-rich interlayer provides only a small positive correction, insufficient to explain the large negative effective spin Hall angle by itself. The dominant control variable is therefore inferred to be the incorporation of W into the upper YPtBi interface, which plausibly modifies the local electronic structure of YPtBi and amplifies the stack-level response. These results provide a more physically constrained interpretation of the stack behavior and identify a BEOL-compatible route to disordered topological spin-source layers for scaled SOT memory and compute-in-memory hardware.

cond-mat.mtrl-sci↗

arXiv2026

Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents

Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and preferences across sessions, and accumulation of procedural knowledge from prior outcomes. These requirements extend beyond document retrieval: a memory layer must determine which interactions become durable state, how that state is scoped, how it is retrieved under latency constraints, and how it is revised or removed over time. This report studies Oracle Agent Memory as a database-native memory substrate built on Oracle Database. Three themes organize the discussion: memory as a lifecycle spanning ingestion, extraction, consolidation, retrieval, summarization, and revision or removal; a layered architecture that separates an active memory core from a passive memory-store interface with explicit scope control across users, agents, and threads; and evaluation methodology in which downstream task accuracy is complemented by memory-centric measures such as evidence retrieval, recall, latency, and estimated token use. The report summarizes LongMemEval results, reaching 93.8% accuracy, compares Oracle Agent Memory against flat-history baselines, using about 10.7x fewer tokens, and published or reported external baselines where available, and closes with implementation-oriented appendix material covering setup, thread lifecycle, and search semantics.

cs.AI↗

arXiv2026

Environmental Sound Deepfake Detection Using Deep-Learning Framework

In this paper, we propose a deep-learning framework for Environmental Sound Deepfake Detection (ESDD) - the task of identifying whether the sound scene and sound event in an input audio recording is fake or real. To this end, we first conduct extensive experiments to explore how individual spectrograms, a wide range of network architectures, and pre-trained models affect the performance of an ESDD model. The experimental results on the benchmark datasets of EnvSDD indicate that detecting deepfake audio of sound scenes and detecting deepfake audio of sound events should be considered as individual tasks. We also show that fine-tuning a pre-trained model is more effective than training a model from scratch for ESDD. Ultimately, our best model, which fine-tunes the pre-trained BEATs model using the proposed two-phase training strategy, achieves an Accuracy of 0.98, F1 score of 0.95, and AUC score of 0.99 on the Test subset of the EnvSDD dataset. Our best model also achieves an Accuracy of 0.86, F1 score of 0.80, and AUC of 0.93 when evaluated cross-dataset on the ESD-Challenge-TestSet dataset.

cs.SD↗

arXiv2025

Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images

Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond na\"ive upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages of single-image and multi-image techniques. It combines temporal oversampling from repeated Sentinel-2 acquisitions with a learned prior from high-resolution Pl\'eiades Neo data. In doing so, SEN4X upgrades Sentinel-2 imagery to 2.5 m ground sampling distance. We test the super-resolved images on urban land-cover classification in Hanoi, Vietnam. We find that they lead to a significant performance improvement over state-of-the-art super-resolution baselines.

eess.IV↗

arXiv2025

Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network

Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly motion capture systems and specialized personnel, restricting its accessibility and broader application. Recent advancements in deep neural networks have enabled quantitative movement analysis using single-camera videos, offering an accessible alternative to conventional motion capture systems. In this paper, we present an efficient approach for clinical gait analysis through a dual-pattern input convolutional Transformer network. The proposed system leverages a dual-input Transformer model to estimate essential gait parameters from single RGB videos captured by a single-view camera. The system demonstrates high accuracy in estimating critical metrics such as the gait deviation index (GDI), knee flexion angle, step length, and walking cadence, validated on a dataset of individuals with movement disorders. Notably, our approach surpasses state-of-the-art methods in various scenarios, using fewer resources and proving highly suitable for clinical application, particularly in resource-constrained environments.

cs.CV↗

arXiv2018

Algorithmic Trading with Fitted Q Iteration and Heston Model

We present the use of the fitted Q iteration in algorithmic trading. We show that the fitted Q iteration helps alleviate the dimension problem that the basic Q-learning algorithm faces in application to trading. Furthermore, we introduce a procedure including model fitting and data simulation to enrich training data as the lack of data is often a problem in realistic application. We experiment our method on both simulated environment that permits arbitrage opportunity and real-world environment by using prices of 450 stocks. In the former environment, the method performs well, implying that our method works in theory. To perform well in the real-world environment, the agents trained might require more training (iteration) and more meaningful variables with predictive value.

q-fin.TR↗