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UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
No full textNeural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications. We introduce UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling joint hyperspectral novel view synthesis and unsupervised material segmentation. Our method models spectral reflectance via diffuse and specular components, where a learned dictionary of global endmembers represents pure material signatures, and per-point abundances capture their distribution. For material segmentation, we use spectral signature predictions along learned endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF enables scene editing by modifying learned endmember dictionaries for flexible material-based appearance manipulation. Extensive experiments validate our approach, demonstrating superior spectral reconstruction and material segmentation to existing methods. Project page: https://www.factral.co/UnMix-NeRF.This work is supported by the KAUST Center of Excellence for Generative AI under award number 5940. The computational resources are provided by
IBEX, which is managed by the Supercomputing Core Laboratory at KAUST
The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency
No full textIn finance, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficiency drops when it undergoes extreme events. Many models for multivariate extremes focus on positive dependence, making them unsuitable for studying extremal dependence in financial markets where data often exhibit both positive and negative extremal dependence. To this end, we construct regular variation models on the entirety of ℝ and develop a bivariate measure for asymmetry in the strength of extremal dependence between adjacent orthants. Our directional tail dependence (DTD) measure allows us to define the Efficient Tail Hypothesis (ETH)—an analogue of the Efficient Market Hypothesis—for the extremal behavior of the market. Asymptotic results for estimators of DTD are described, and we discuss testing of the ETH via permutation-based methods and present novel tools for visualization. An empirical study of China’s futures market leads to a rejection of the ETH and we identify potential profitable investment opportunities during extreme episodes. To promote the research of microstructure in China’s derivatives market, we open-source our high-frequency data, which are being collected continuously from multiple derivative exchanges.We thank the Editor, the Associate Editor, and the two anonymous referees for their insightful comments, which have significantly improved the paper.
The authors gratefully acknowledge funding from the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2020-4394
An integrated framework for achieving sustained restoration at scale: community-driven restoration of the Great Barrier Reef
No full textThe Evolution of Gas Sensors Into Neuromorphic Systems
No full textGas sensors are essential in applications ranging from environmental monitoring and industrial safety to healthcare diagnostics and consumer devices, where reliable and selective detection is critical. With growing demands for sensitivity, selectivity, and energy efficiency, sensor technology has evolved significantly. Historically, the field advanced from sentinel organisms and gas lamps to a range of sophisticated mechanisms. Yet, conventional sensors remain limited to passive detection, relying on separate units for memory and processing, which leads to higher power consumption, slower response, and reduced adaptability in dynamic environments. Neuromorphic sensing provides a compelling alternative by integrating sensing, memory, and computation in a single device, enabling compact, energy-efficient, and adaptive gas detection inspired by biological olfactory systems. This review begins with a concise overview of traditional semiconductor metal oxide gas sensors, providing a baseline for introducing memristor-based gas sensors, or “gasistors.” These devices represent a transformative shift, offering improved efficiency, reliability, and versatility in gas sensing electronics. We then highlight the neuromorphic in-memory gas sensing paradigm, with examples including electronic noses, bio-inspired olfactory systems, and spike-based computational frameworks. Finally, we discuss progress in materials, device architectures, and algorithms, and outline opportunities and challenges for realizing the full potential of neuromorphic gas sensing.The authors would like to acknowledge funding for this work by the King Abdullah University of Science and Technology (KAUST). Authors would also like to acknowledge KAUST Research Funding (KRF) under Award No. ORA-2022-5314 and Transition Award in Semiconductors, Award No. FCC/1/5939
Combined TIRF and 3D Super-Resolution Microscopy for Nanoscopic Spatiotemporal Characterization of Adhesion Molecules on Microvilli
No full textThe homing of hematopoietic stem/progenitor cells (HSPCs) and leukemic cells is a multistep process governed by complex spatiotemporal interactions between adhesion molecules under shear stress. While the molecular and biological mechanisms of this process have been extensively studied, the precise spatial and temporal organization of adhesion molecules that influences homing efficiency remains relatively poorly understood. In particular, the roles of the cell surface topography and its morphological changes during homing in shaping the spatial organization of adhesion molecules remain elusive. This is partly due to the lack of imaging techniques that simultaneously capture both nanoscopic cell surface morphology and the spatial distribution of the adhesion molecules. Here, we develop a microfluidics-based super-resolution (SR) imaging platform that enables the three-dimensional (3D) mapping of the cell surface morphology and the spatial distribution of the adhesion molecules during HSPC and leukemic cell rolling by integrating total internal reflection fluorescence microscopy (TIRFM) with single-molecule localization microscopy (SMLM). We reconstruct the cell surface morphology, which is critical to the homing, using TIRFM, and precisely overlay the spatial distribution of adhesion molecules, including CD44, PSGL-1, and actin cytoskeleton, determined by 3D-SMLM, on the topographic map. We show distinct nanoscopic localizations of adhesion molecules on the microvilli of HSPCs/leukemic cells and their reorganization under shear stress during cell rolling, at a spatial resolution of approximately 30 nm. The approach offers a powerful means to elucidate the complicated interplay between cell surface morphology and ligand-receptor interactions
Effects of lower floating-point precision on scale-resolving numerical simulations of turbulence
No full textModern computing clusters offer specialized hardware for reduced-precision arithmetic, which can significantly speed up the time to solution. This is possible due to a decrease in data movement, as well as the ability to perform arithmetic operations at a faster rate. However, for high-fidelity simulations of turbulence, such as direct and large-eddy simulation, the impact of reduced precision on the computed solution and the resulting uncertainty across flow solvers and different flow cases has not been explored in detail, and limits the optimal utilization of new high-performance computing systems. In this work, the effect of reduced precision is studied using four diverse computational fluid dynamics (CFD) solvers (two incompressible, Neko and Simson, and two compressible, PadeLibs and SSDC) using four test cases: turbulent channel flow at
and higher, forced transition in a channel, flow over a cylinder at
, and compressible flow over a wing section at
. We observe that the flow physics are remarkably robust with respect to reductions in lower floating-point precision, and that often other forms of uncertainty, due to, for example, time averaging, often have a much larger impact on the computed result. Our results indicate that different terms in the Navier–Stokes equations can be computed to a lower floating-point accuracy without affecting the results. In particular, standard IEEE single precision can be used effectively for the entirety of the simulation, showing no significant discrepancies from double-precision results across the solvers and cases considered. Potential pitfalls are also discussed.This project was initiated during the CTR Summer Program 2024 in Stanford [34], for which we gratefully acknowledge the financial support that enabled our participation. Computer time was provided by the National Infrastructure for Computing in Sweden (NAISS). The authors gratefully acknowledge the HPC resources provided for this collaborative effort by the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. This project has received funding from KAUST under grant No. BAS/1/1663-01-01. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the German Research Foundation (DFG). This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No. 101092621. The JU receives support from the European Union’s Horizon Europe research and innovation programme and Germany, Italy, Slovenia, Spain, Sweden and France
Stability and Reliability of van der Waals High-κ SrTiO<sub>3</sub> Field-Effect Transistors with Small Hysteresis.
No full textSingle-crystal SrTiO3 (STO) is an ultrahigh-κ insulator with an expected low interface trap density that promises high breakdown strength and has great potential to boost the reliability of two-dimensional (2D) field-effect transistors (FETs). Here we provide a detailed study of the performance, stability, and reliability of MoS2 FETs with STO gate insulators. Most importantly, we observe a small hysteresis for electric fields up to 8 MV cm-1 at a sweep rate range spanning 0.01-1 V s-1 and sweep times of kiloseconds. Interestingly, the hysteresis is counterclockwise and bias temperature instability (BTI) is often anomalous, both likely caused by the diffusion of oxygen vacancies. We also show that the hysteresis dynamics in MoS2/STO FETs are reproducible over a long time, which underlines their high reliability. Our findings show that STO is a promising gate insulator that might help overcome critical obstacles to highly reliable 2D nanoelectronics.We would like to express our gratitude to Huawei Technologies R&D Belgium for their generous financial support, which was instrumental in advancing this research. Furthermore, this work was supported by the European Research Council under Grant Agreement no. 101055379 (F2GO). Also, X.R.W. acknowledges support from the Singapore Ministry of Education (MOE) Academic Research Fund Tier 3 grant (MOE-MOET32023-0003) entitled “Quantum Geometric Advantage”. Eventually, S.M.S-E acknowledges the Vienna University of Technology Library for financial support through its Open Access Funding Programme
VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets
No full textThis paper introduces a model for coordinating prosumers with heterogeneous distributed energy resources (DERs), participating in the local energy market (LEM) that interacts with the market-clearing entity. The proposed LEM scheme utilizes a data-driven, model-free reinforcement learning approach based on the multi-agent deep deterministic policy gradient (MADDPG) framework, enabling prosumers to make real-time decisions on whether to buy, sell, or refrain from any action while facilitating efficient coordination for optimal energy trading in a dynamic market. In addition, we investigate a price manipulation strategy using a variational auto encoder-generative adversarial network (VAE-GAN) model, which allows utilities to adjust price signals in a way that induces financial losses for the prosumers. Our results show that under adversarial pricing, heterogeneous prosumer groups, particularly those lacking generation capabilities, incur financial losses. The same outcome holds across LEMs of different sizes. As the market size increases, trading stabilizes and fairness improves through emergent cooperation among agents.This publication is based upon work supported by King Abdullah University of Science and Technology under Award No. RFS-OFP2023-5505
Deep learning accelerated inverse modeling and forecasting for large-scale geologic CO2 sequestration
No full textTraditional physics-simulation based approaches for inverse modeling and forecasting in geologic CO2 sequestration (GCS) are very time consuming. For example, a single inverse modeling may take a few weeks for a large-scale CO2 storage model without leveraging any high-performance computing. To speed up this process, we developed a novel approach that employs machine learning methods to integrate monitoring data into subsurface forecasts more rapidly than current physics-based inverse modeling workflows allow. These updated forecasts with the updated models from the inverse modeling process will be used to provide site operators with decision support by generating real-time performance metrics of CO2 storage (e.g., CO2 plume and pressure area of review). First, we developed a deep learning (DL) model to predict the pressure/saturation evolution in large-scale storage reservoirs. A feature coarsening technique was applied to extract the most representative information and perform the training and prediction at the coarse scale, and to further recover the resolution at the fine scale by 2D piecewise cubic interpolation. The accuracy of the feature coarsening-based DL model is validated with a reservoir model built upon a Clastic Shelf storage site. Thereafter, the feature coarsening-based DL model was utilized as forward model in the inverse modeling process where a classical data assimilation approach, ES-MDA-GEO, was applied. The efficiency and effectiveness of the proposed DL-assisted workflow for large-scale inverse modeling and forecasting was demonstrated with the Clastic Shelf storage model.This work was supported by the US Department of Energy (DOE) through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. DOE (Contract No. 89233218CNA000001). The authors acknowledge the financial support by US DOE's Fossil Energy and Carbon Management through the project, Science-informed Machine Learning to Accelerate Real Time (SMART) Decisions in Subsurface Applications. Funding for SMART is managed by the National Energy Technology Laboratory (NETL)
CCDC 2418686: Experimental Crystal Structure Determination :
No full textAn entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures