1645542 results (page 8 of 65822)
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Revisiting Metastable Dark Energy in Light of DESI DR2 BAO and DESI DR1 Full-Shape Measurements
We revisit metastable dark energy (DE) models described by a radioactive-like decay law. We consider three scenarios: an effective, exponentially decaying DE component; decay of DE into non-baryonic dark matter (DM); and decay of DE into dark radiation (DR). We constrain the metastable DE models using DESI DR2 baryon acoustic oscillation (BAO) data, Type Ia supernovae (SNIa), cosmic microwave back…
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UART for Wearables (U4We): DC Power and Carrierless Signal Transfer over Conductive Textiles
This brief presents a conductive-textile interconnection scheme for batteryless distributed wearable modules. Two conductive textile layers separated by an insulating fabric layer are used as a transmission line that simultaneously conveys DC power and pulse-based data signals without point-to-point wiring. To minimize the circuit overhead of each module, universal asynchronous receiver/transmitte…
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A Geometry-based Stochastic Wireless Channel Model using Generative Neural Networks
Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate system performance of wireless communications. This paper introduces a new method to model a GBSM by training a generative neural network using images formed by channel parameters. Toward thi…
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tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins
Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberrations. Numerical solvers are accurate but computationally expensive for digital twins, where the field must be re-estimated repeatedly as treatment conditions change. Existing deep-learning surrogates are fast but typically use voxel-to-voxel regression …
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A noncommunicative Kalman condition for null controllability of backward stochastic parabolic systems
We study a system of $n$ backward stochastic heat equations with constant deterministic coupling matrices, scalar spatial diffusion, and a localized drift control. The coupling acts both on the first state and on the martingale integrand. We establish a necessary and sufficient algebraic condition for null controllability: the columns of the control matrix must generate the state space under the j…
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Reinforcement-Learned Electric-Field Sensing with Asymmetrically Blockaded Rydberg Arrays
We present a reinforcement learning-optimized Rydberg electrometer based on the asymmetric blockade effect and achieve high-sensitivity electric field sensing in Rydberg arrays. Microwave dressing induces asymmetric blockade to suppress interactions between target atoms, while keeping the coupling between the central control atom and target atoms field-tunable near Förster resonance. The field-reg…
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Symmetry-Projected Compatible Multiparameter Quantum Sensing
We establish a general symmetry-projection framework for multiparameter quantum sensing. Decomposing encoding generators into subspace-preserving and subspace-changing components relative to a symmetry sector identically eliminates all cross-sector elements of the quantum Fisher information matrix (QFIM) and the mean symmetric logarithmic derivative (SLD) commutator matrix. When projected subspace…
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Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was dev…
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The first law of black hole mechanics in conformal Einstein-Power-Yang-Mills theory
In the framework of the Iyer-Wald formalism, the first law of black hole mechanics is examined within the context of conformal Einstein-power-Yang-Mills (CEPYM) theory. By comparing two inffnitesimally neighbouring stationary black hole solutions, we obtain the explicit analytical expression for the first law of black hole thermodynamics in CEPYM theory.
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A Quantum Dominant Energy Condition
We propose a quantum dominant energy condition (QDEC) for the stress tensor in the context of quantum field theory in curved spacetimes. A rigorous proof is given for the case of Rindler wedges, and a heuristic discussion about possible generalizations to more general geometric setups, including curved spacetime, is provided. In Minkowski spacetime, we establish a connection with state recovery bo…
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Multi-Hop RIS ISAC for Target Positioning: A Tensor Decomposition-based Approach
Reconfigurable intelligent surface (RIS) has demon- strated remarkable potential to enhance the performance of integrated sensing and communication (ISAC), particularly when the line-of-sight (LoS) paths are obstructed. By controlling the reconfigurable elements on the surface, RIS can establish virtual LoS paths and provide considerable passive beamforming gains, thereby significantly improving t…
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The Burgers' Equation in the Hermite-Sobolev Spaces
In this paper, we show existence and uniqueness of solutions to the viscous Burgers' equation in $\mathbb{R}^d$, when the initial condition $u_0$ is in Hermite-Sobolev space of index $p$, for suitable non-negative integers $p$. Our solutions are local in time. We also have a regularity result, viz. if $u_0$ belongs to Schwartz space, then so does the solution.
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Hybrid Impedance-Admittance Control with Multi-Link Aerial Robot for Contact-Rich Surface Sliding Task
Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However, they still face substantial challenges in contact-rich aerial manipulation tasks such as surface sliding, which requires both disturbance robustness and compliance to uncertain surface geometry. Force-control strategies such as impedance and admitt…
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Logarithmic Resonance at Mixed Boundary Junctions in Gradient-Dependent Semilinear Equationsv
The regularity of solutions to elliptic partial differential equations degrades severely at mixed Dirichlet-Neumann boundary junctions, characterized classically by an $\mathcal{O}(r^{1/2})$ leading singular function. While this linear behavior is well documented, the introduction of gradient-dependent semilinear perturbations alters the local asymptotic profile. This article proves that gradient …
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Validation and calibration of quantum hardware through the many-body quantum Mpemba effect
We introduce a validation process that harnesses engineered many-body relaxation to control and calibrate quantum hardware. On two independently developed neutral-atom processors, we realize the many-body quantum Mpemba effect in an open system for the first time. Initial-state engineering creates fast and slow relaxation pathways: the fast pathway opens access to unreachable mixed-state physics b…
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Radar Detection in the CBRS Band: Techniques, Challenges, and Future Directions
The 3.5 GHz Citizens Broadband Radio Service (CBRS) is a shared wireless band that allows both government systems and commercial networks (such as private LTE/5G) to use the same spectrum. To prevent interference with critical government systems, especially naval radars, CBRS uses a monitoring system called the Environmental Sensing Capability (ESC). ESC acts like a network of sensors that continu…
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Comparative Validation of GPT-4o-mini and Teacher Mean Scores for Automated Scoring of Music Analysis Responses: Single-Pass Deployment, Repeatability, and Strategy-Specific Bias
Scoring open-ended music analysis responses is time-consuming and requires nuanced judgments of harmonic knowledge and formal understanding. This study evaluates the validity and repeatability of GPT-4o-mini for rubric-based scoring of music analysis essays, using teacher mean scores as the benchmark. A dataset of 300 university-level student responses was scored by teachers on four dimensions: Ha…
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Factorial residues modulo a prime: beyond the square-root bound
For a prime \(p\), let \(A_p=\{k!\pmod p:1\leq k<p\}\). We prove \(|A_p|\gg p^{8/15}\), improving the general lower bound \((\sqrt{2}-o(1))p^{1/2}\). The proof begins with the identity \((n+2)!=(n+1)!+((n+1)!)^2/n!\) in \(\mathbb{F}_p\), which produces many incidences for a family of fractional-linear maps. After Cauchy--Schwarz, the transition maps between two members of this family become affine…
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Machine-Learning-Accelerated Metallene Stabilization from High-Throughput Sandwich Modeling
Metallenes have appealing properties, but stabilizing them in a monolayer phase poses challenges for their synthesis. A recent experiment showed that the van der Waals squeezing method can stabilize certain metallenes in a MoS2 sandwich. This pioneering work motivates systematic studies, but such studies are experimentally impractical, while first-principles modeling remains prohibitive. Here, arm…
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At least seven modes in a heteroscedastic three-component bivariate Gaussian mixture
A Gaussian mixture density can have more modes than components. It has been conjectured that the maximum number of modes of a $d$-variate $k$-component Gaussian mixture density is $\binom{d+k-1}{d}$, which equals six for $(d,k)=(2,3)$. We construct an explicit family of equally weighted heteroscedastic three-component bivariate Gaussian mixture densities with at least seven distinct nondegenerate …
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High-Post-Newtonian-Order Dynamics Induced by Tail-of-Tail Interactions: The Non-Geodesic Terms
We compute the tail-of-tail contribution to the conservative dynamics of eccentric, non-spinning compact binaries to relative 1PN order and to $\mathcal O(e_t^{12})$. Using the $1$PN quasi-Keplerian dynamics in harmonic coordinates, we derive the Delaunay-averaged Hamiltonian at $5.5$PN and $6.5$PN order and match it to the effective-one-body description, allowing us to determine the corresponding…
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NeuroWorld: A Latent Brain World Model for Stimulus-Conditioned Human Brain Dynamics
Forecasting human brain activity during naturalistic experience requires modeling how endogenous neural states evolve causally under continuous sensory drive. Existing brain encoding models instead frame this as stimulus-to-response regression without strict temporal constraints, allowing future stimuli to leak into current predictions. We introduce NeuroWorld, to our knowledge the first brain wor…
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The Sample Complexity of Fidelity Estimation to a Known Rank-$r$ Reference State Is $\widetildeΘ(r^2/\varepsilon^2)$
We settle the sample complexity of estimating the root Uhlmann fidelity $F(ρ,σ)=\operatorname{tr}\sqrt{\sqrtσρ\sqrtσ}$ between an unknown state $ρ$ and a known rank-$r$ reference state $σ$. Writing $S(r,\varepsilon)$ for the sample complexity at additive error $\varepsilon$, we resolve the open problem posed by Wang by closing, up to logarithmic factors, the gap between the previously known bounds…
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Quantum Simulation of Nuclear Shell Model Using GCM-Based Methods on NISQ Devices
Based on the Generator Coordinate Method (GCM), we use a Quantum GCM (QuGCM) within a hybrid quantum-classical framework to simulate low-lying eigenstates of nuclear systems on quantum devices. The generator basis states are constructed from Hartree-Fock (HF) reference states, excited via symmetry-adapted unitary coupled-cluster (UCC) operators. These states are prepared as non-orthogonal quantum …
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Graph Signal Surrogate Generation for Statistical Testing of Covariance Structure on Directed Graphs
Non-parametric statistical testing is based on surrogate data generation that randomizes chosen features in the empirical data. In the graph setting, graph signal processing (GSP) brings forward versatile schemes; e.g., to preserve smoothness of graph signals as measured by the Dirichlet energy. However, how to deal with directed graphs remains an active area of research. We begin by revisiting th…