Search papers, labs, and topics across Lattice.
100 papers published across 8 labs.
Disciplinary siloing in research is starkly revealed through a novel citation graph that links claims to their sources, reshaping our understanding of knowledge evolution in AI fields.
Funding for IoMT research not only boosts scholarly output but also correlates with better health outcomes, highlighting the critical role of financial support in advancing healthcare technology.
Tracking marine animals with just one hydrophone per AUV achieves unprecedented accuracy, reducing data loss and operational complexity in aquatic research.
Electronic noise spectroscopy reveals nanoscale molecular parameters with unprecedented resolution, opening new avenues for chemical sensing in quantum devices.
Finer grain sizes may mitigate pitting but paradoxically accelerate defect propagation under mechanical stress, complicating corrosion management strategies.
Disciplinary siloing in research is starkly revealed through a novel citation graph that links claims to their sources, reshaping our understanding of knowledge evolution in AI fields.
Funding for IoMT research not only boosts scholarly output but also correlates with better health outcomes, highlighting the critical role of financial support in advancing healthcare technology.
Tracking marine animals with just one hydrophone per AUV achieves unprecedented accuracy, reducing data loss and operational complexity in aquatic research.
Electronic noise spectroscopy reveals nanoscale molecular parameters with unprecedented resolution, opening new avenues for chemical sensing in quantum devices.
Finer grain sizes may mitigate pitting but paradoxically accelerate defect propagation under mechanical stress, complicating corrosion management strategies.
Surprisingly, lower bulk thermal diffusivities in liquids can lead to faster interfacial thermal spreading, challenging conventional understanding of heat transport.
Achieving 0.8% error in glottal volume flow, this PINO-based method revolutionizes speech production analysis by eliminating the need for pre-computed training data.
Contextual tunneling in LLMs can be overcome, leading to more reliable and physically grounded materials discovery through the innovative ARIA framework.
Conventional computing systems are ill-equipped to handle the explosive growth of biological data, necessitating a paradigm shift in computer architecture for healthcare applications.
A single multimodal model outperforms specialized approaches across 80 biological tasks, redefining the landscape of biological AI applications.
Agentic models may resolve citations, but they still mislink to the wrong papers 15.9% of the time, exposing a critical flaw in current AI evaluation benchmarks.
Static reports are out; BioInsight's interactive system empowers researchers to dynamically explore and refine biomedical evidence like never before.
Transforming dense scientific papers into reader-friendly formats could revolutionize how researchers engage with complex literature.
Achieving 98.6% tracking efficiency with a 0.8% fake rate, HEPTv2 revolutionizes particle tracking by eliminating the need for graph construction and auxiliary processing.
Uncertainty-quantified neural surrogates can drastically enhance Bayesian inference efficiency for complex inverse problems, outperforming traditional methods in high-dimensional settings.
Evolutionary algorithms can drastically improve the accuracy of Physics-Informed Neural Networks by efficiently navigating the complex hyperparameter landscape.
CNNs can achieve high entropy estimation accuracy in multi-qutrit systems with only a fraction of the measurements needed for traditional methods, marking a significant leap in scalability.
ASYS reveals a novel way to automate the discovery of analytical forms for PDEs, producing interpretable solutions where none existed before.
A trajectory-based event detection method using topological data analysis outperforms traditional techniques in monitoring high-dimensional dynamic processes.
Integrating genomic data with ecological constraints allows for unprecedented accuracy in predicting microbial dynamics and carbon cycling in soil systems.
Multimodal ensemble approaches for cfDNA analysis could revolutionize early cancer detection, but standardization is crucial for progress.
TESSERA embeddings outperform traditional methods in fine-scale urban climate mapping, achieving remarkable accuracy and scalability.
SO-RaNN achieves exact mass matching and positivity in PNP systems while ensuring incompressibility in velocity fields, setting a new standard for accuracy in neural network-based simulations.
Achieving FEM-level accuracy with a 60x reduction in evaluation time, this framework revolutionizes localized response prediction for long-span bridges.
A neural network that accelerates PDE solutions by learning HODLR matrix inverses outperforms traditional solvers and existing neural operators in speed and accuracy.
Context-aware features can slash IVF prediction errors by over 60%, revealing critical insights hidden in environmental data.
Unsupervised algorithms can slash semantic segmentation labeling time from 170 hours to just 37 hours, revolutionizing data annotation in materials science.
Evaluation design choices can drastically alter the perceived risks of AI systems in biological research, revealing a hidden complexity that demands attention.
A structured coarse space can dramatically improve the efficiency of solving complex fluid dynamics equations on exascale systems, outperforming traditional AMG methods.
An ideal model of amorphous silicon reveals a pristine electronic structure that aligns perfectly with experimental observations, challenging traditional approximations.
Achieving Hartree-Fock precision in solid-state energy calculations could redefine benchmarks for computational materials science.
A new thermal-based definition of slip velocity clarifies the complexities of fluid transport at the nanoscale, offering a more robust framework for understanding solid-fluid interactions.
Ground states in ultrastrong light-matter interactions can be accurately modeled with a new nonperturbative variational approach that outperforms traditional methods.
The authors reveal that the Hamiltonian emerges naturally from a minimal set of axioms, reshaping our understanding of quantum dynamics.
Contour-constrained deformable registration can cut target registration errors in head and neck surgeries by nearly half, revolutionizing intraoperative guidance.
QCPIKAN accelerates PDE solutions with exponential convergence rates while embedding physical laws directly into the learning process.
TerraMARS transforms unstructured Mars research into actionable insights, paving the way for advanced terraforming strategies.
Encrypted genomic queries can now be conducted without revealing sensitive data, significantly enhancing privacy in genomic research.
Achieving state-of-the-art LVFP classification while enhancing interpretability, HypOProto transforms how we leverage echocardiography in heart failure diagnostics.
Achieving Hamiltonian cycle decompositions with minimal switches in non-coprime Eisenstein--Jacobi networks could redefine how we approach cycle-splicing problems in advanced interconnection networks.
The stationary spin entanglement entropy serves as a crucial observable that reveals unexpected relationships between entanglement structures and dynamical phases in the sub-Ohmic spin-boson model.
DFT-trained neural network potentials can accurately model hydration and exchange kinetics of magnesium ions, but struggle with solvation free energy predictions.
The nonlinear index of entangled polymers reveals a surprising geometric limit, challenging traditional views on shear deformation behavior.
Claim drift in automated research can lead to significant discrepancies, but Xcientist ensures that every generated mechanism remains accountable and traceable back to its evidential roots.
Machine learning resolves 20,000 ambiguous X-ray source matches, revealing the limitations of traditional spatial cross-matching methods.
Achieving high-resolution cardiac simulations with a method that guarantees reduced super-resolution error through physics-based constraints and Koopman regularization.
AI agents struggle to make reliable preclinical pharmacology decisions, with the best models achieving less than 60% accuracy in real-world evaluations.
Achieving up to 37% faster convergence on complex flow problems by harnessing graph neural networks to optimize algebraic multigrid solvers.
Interactive candidate selection in bioprocess optimization reveals trade-offs between performance, uncertainty, and robustness, empowering experts to refine criteria as models evolve.
Tailoring follow-up intervals for Type 2 Diabetes patients can cut costs by over 34% while enhancing care based on individual risk profiles.
DIPHINE reveals the intricate information dynamics of complex systems, outperforming traditional methods and enabling insights from real-world data without distributional constraints.
Achieving a 93% recovery rate for Earth-size transits, TransitNet outperforms traditional methods while maintaining a compact model size and high inference speed.
Unsupervised reward optimization allows protein language models to self-improve, achieving near-oracle performance without the need for labeled data.
Significant architectural differences in machine learning models only emerge during active sudden stratospheric warming events, revealing the crucial role of vertical coupling in accurate predictions.
TTPFTS not only outperforms existing methods in efficiency but also introduces a groundbreaking uncertainty quantification metric that enhances decision-making in complex environments.
Efficiently computing geodesic-like curves on complex parametric surfaces could revolutionize applications in computer graphics and robotics.
Equivariant graph neural networks can dramatically improve optical spectra predictions, outperforming traditional models and enhancing materials screening for solar cells.
AdsMind achieves a staggering 100% success rate in discovering adsorption configurations while slashing computational costs by 14-fold compared to traditional methods.
scGTN not only captures the complex intercellular relationships in single-cell RNA sequencing data but also achieves superior clustering performance compared to traditional methods.
Automated ply distinction in CFRP micrographs reveals critical insights into manufacturing-induced inhomogeneities and their impact on mechanical properties.
Achieving 260GB/s decoding speeds on genomic data, this work revolutionizes how we access and process massive biological datasets.
Machine learning can transform 2DES by extracting maximum insights from limited data while guiding experimental design for improved accuracy.
TOTEN outperforms state-of-the-art tokenization methods, achieving a remarkable 0.775-0.904 in numerical reconstruction accuracy, compared to just 0.627-0.703 for the best existing approach.
Mainstream LLMs struggle to navigate safety risks in scientific applications, revealing critical vulnerabilities in AI4Science workflows.
Passive users can securely establish keys without quantum detectors, relying solely on a single active station's entangled-state infrastructure.
Well-rounded lattices could revolutionize security protocols in cryptography and wireless communications by leveraging their unique mathematical properties.
Multi-equalization reveals that local reactivity can be constrained by global electron density, fundamentally changing how we understand charge redistribution in molecules.
Lower semicontinuity of the kinetic-energy functional unlocks a robust inversion scheme for recovering exchange-correlation potentials in periodic systems.
The interplay of hydrogen bonding and chain dynamics reveals four distinct relaxation times that govern the behavior of supramolecular alcohols.
Efficiently preparing CAS wavefunctions could revolutionize quantum algorithms for electronic structure problems by reducing complexity from exponential to polynomial scaling.
Sequential REST not only accelerates the modeling of supramolecular polymers but also enhances efficiency by optimizing monomer binding positions one at a time.
Fluctuations in gas can create strikingly similar patterns on reactive surfaces, revealing a profound connection between gas dynamics and surface chemistry.
PhySciBench reveals that top LLMs struggle with scientific reasoning, achieving only 33.5% accuracy, while DelveAgent demonstrates a promising 7.5% improvement in performance.
LiL-Q achieves machine precision solutions for nonlinear PDEs in just a single solve, outperforming traditional PINNs with far fewer parameters and no gradient-based optimization.
TSCD reveals causal structures with logarithmic efficiency, outperforming traditional methods even in noisy environments.
Achieving 16^3 times higher resolution in predicting mechanical properties for 3D objects could revolutionize the fidelity of physics simulations in digital environments.
Achieving higher accuracy and faster convergence in multi-material problems without the complexity of additional loss terms could revolutionize how we approach neural network training for physics-based simulations.
ASTEROID achieves superior accuracy in multi-step predictions while slashing the computational costs of molecular dynamics simulations.
Hybrid models that blend physics and advanced neural architectures can achieve up to 22% better accuracy in short-term weather forecasting without sacrificing physical realism.
ST-CND reveals that traditional spatial indicators can miss critical tipping points, offering a more accurate and interpretable approach to early warning in complex ecosystems.
BBMF uncovers interpretable patterns in cancer genomics that traditional methods miss, linking patient subsets to critical chromosomal alterations.
Validating metamorphic relations for SciML surrogates can transform testing from a checklist approach into a domain-aware, artifact-driven process that enhances reliability and interpretability.
Operator Boosting achieves up to 95% reduction in parameters while enhancing accuracy in neural PDE surrogates, challenging the conventional approach of large model training.
Geometry-aware uncertainty quantification can be achieved in neural operators without sacrificing predictive accuracy or incurring high computational costs.
Achieving a 45.2% boost in prediction accuracy, the Multi-Adapter PPO framework revolutionizes wavelength selection in LIBS by leveraging reinforcement learning and cross-attention techniques.
Nearly half of FT50 journals shift quartiles when policy and patent impacts are considered, challenging the notion of uniform excellence in academic publishing.
NeRFs can revolutionize forest mapping by delivering high-fidelity 3D reconstructions that mitigate the flaws of traditional methods.
HLS-GPT achieves unprecedented accuracy in reconstructing satellite reflectance data, even with significant missing observations.
Real-time adaptation to catastrophic damage in soft robots is now achievable in under a minute, transforming their operational resilience.
A new thermodynamically consistent model for ion exchange membranes accurately predicts their behavior in complex ionic environments, paving the way for optimized designs in critical technologies.
Achieving 98% structural validity and a four-fold improvement in binding energy conditioning, this model revolutionizes how we design catalysts by directly generating structures tailored to desired properties.
BrainWorld generates stable 4D fMRI trajectories by integrating structural MRI, outperforming existing models in both fidelity and multimodal representation learning.
Achieving global smoothness and explicit representation without input parametrization, Blended Chart Surfaces redefine surface fitting in geometry processing.
Rejection sampling can eliminate bias in mixed-radix key generation, ensuring perfect secrecy from quantum key distribution sources.
End-to-end AI-assisted workflow management can empower non-experts to design complex scientific workflows with expert precision while drastically reducing debugging time.
Optimizing activation periods in quantum networks could significantly enhance entanglement distribution efficiency, revealing a new pathway to maintain link fidelity over time.
Achieving over 71% peak drain efficiency in Doherty amplifiers using a groundbreaking deep learning approach could redefine power amplifier design.
AI-generated microwave filters not only match traditional performance but also unveil new electric field patterns that challenge conventional design paradigms.
The choice of ensemble-counting measure can make or break the validity of Bayesian estimates in molecular simulations, with the Jeffreys measure proving to be a game-changer.
The nitrogen site in fluoropyridine reveals a surprising sensitivity to local vibrations that is dramatically enhanced by electronic excitation, while the fluorine site remains largely unaffected.
