One binary operator eml(x,y)=exp(x)-ln(y) plus the constant 1 generates all elementary functions including sin, cos, sqrt, log, arithmetic operations, and constants e, pi, i.
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Analysis of Antarctic Treaty documents constructs a space of concerns showing structured, persistent attention where specialized actors produce binding law at five times baseline rate.
Target dynamics provide an intrinsic source of variation equivalent to controlled illumination changes, enabling scattering-compensated reconstruction of dynamic scenes with one acquisition per frame in holographic and fluorescence imaging.
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Single-cell patterning of bacterial spores shows that parallel initial orientations create highly ordered nematic films that buckle synchronously, while orthogonal seeding yields chaos, enabling macroscopic control of alignment and optical anisotropy in living films.
Ultrafast nonlinear Hall effect is observed in centrosymmetric black phosphorus via femtosecond laser pulses polarized along the armchair direction, persisting beyond 300 fs.
LGS pretrained on 2.5M trajectories across 16 systems matches deterministic baselines at one step and halves 20-step error while using far less compute and adapting to held-out higher-resolution flows.
Establishes identifiability bounds, metric entropy estimates, and sample complexity for distinguishing linear and nonlinear ODEs from solution data via Hausdorff distance on solution sets.
A monolithic fiber-integrated tunable SPDC source using 3R-MoS2 produces two Bell states and co-polarized pairs with CAR of (8.3±1.8)×10^3, the highest reported for van der Waals materials.
Observation of complete sign reversal in transmission and reflection modulation in ENZ ITO at intensities ~5 TW/cm² and above using few-cycle pulses, with quadratic scaling attributed to intraband-enabled TPA.
High-harmonic emission in solids is made programmable by tuning effective nonlinear order and intrinsic emission phase within a photon-pathway framework, unifying modulation types across materials.
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
BOAT optical sensors co-printed into PAM-actuated soft lattices produce repeatable signal changes that discriminate compression from extension over 100 pressure cycles, enabling a digital shadow.
DoLQ employs a sampler agent, parameter optimizer, and LLM-based scientist agent to iteratively propose, refine, and evaluate ODE candidates, yielding higher success rates and better symbolic term recovery than prior symbolic regression methods on multi-dimensional benchmarks.
AMTe4 (A=Ta,Nb; M=Ir,Rh) compounds host Weyl points within a few meV of the Fermi energy, including multiple types such as type-I, II, and III in NbRhTe4, substantially revising the topological electronic structure.
DenSNet learns the Hohenberg-Kohn map to electron density with equivariant networks and delta-learning, then maps density to energy, producing stable MD trajectories whose infrared spectra match experiment and DFT on ethanol, ethanethiol, resorcinol, and polythiophene oligomers.
Continual learning robots form a significantly more stable invariant subnetwork than constant-task controls, and preserving it improves adaptation while damaging it hurts performance.
A Zr-BMG clamp cell delivers higher neutron transmission and clean background for high-pressure INS compared to conventional CuBe cells.
LEAP enables real-time proprioceptive adaptation to unseen damage in a 6DoF soft wrist using HSA actuators by combining latent damage representations with a robust ensemble method, with conditions identified for linear rather than exponential sample complexity.
A simplicial set sOb_bullet(M) of Hamiltonian forms in n-plectic geometry is shown to be a Kan complex, supplying an n-groupoid model for observables and a categorified pre-n-Hilbert space via recursive inner products.
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
A material formulation of the cross-sectional warping problem for hyperelastic beams is derived in Voigt notation to compute effective nonlinear beam stiffness efficiently.
ReaxFF simulations find N-carbophenes stable above 1000 K, exhibit negative area thermal expansion that functionalization can flip, and allow a heat-driven transition from graphenylene to gamma-graphyne.
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
citing papers explorer
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Ultrafast nonlinear Hall effect in black phosphorus
Ultrafast nonlinear Hall effect is observed in centrosymmetric black phosphorus via femtosecond laser pulses polarized along the armchair direction, persisting beyond 300 fs.
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Fermi energy Weyl nodes in $\mathbf{AM}$Te$_4$ ($\mathbf{A}$=Ta, Nb, $\mathbf{M}$=Ir, Rh)
AMTe4 (A=Ta,Nb; M=Ir,Rh) compounds host Weyl points within a few meV of the Fermi energy, including multiple types such as type-I, II, and III in NbRhTe4, substantially revising the topological electronic structure.
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Zr-based bulk metallic glass clamp cell for high-pressure inelastic neutron scattering
A Zr-BMG clamp cell delivers higher neutron transmission and clean background for high-pressure INS compared to conventional CuBe cells.
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A ReaxFF-based thermomechanical analysis of N-carbophenes: phase-change, thermal expansion, and high temperature synthesis pathway
ReaxFF simulations find N-carbophenes stable above 1000 K, exhibit negative area thermal expansion that functionalization can flip, and allow a heat-driven transition from graphenylene to gamma-graphyne.
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A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.