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Measuring deeply virtual Compton scattering (DVCS) on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of generalized parton distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD$E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks’ angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the Compton form factors$\mathcal{H}$and$\mathcal{E}$.

Published by the American Physical Society2024 

The relationships between crop yields and meteorology are naturally non-stationary because of spatiotemporal heterogeneity. Many studies have examined spatial heterogeneity in the regression model, but only limited research has attempted to account for both spatial autocorrelation and temporal variation. In this article, we develop a novel spatiotemporally varying coefficient (STVC) model to understand non-stationary relationships between crop yields and meteorological variables. We compare the proposed model with variant models specialized for time or spatial, namely spatial varying coefficient (SVC) model and temporal varying coefficient (TVC) model. This study was conducted using the county-level corn yield and meteorological data, including seasonal Growing Degree Days (GDD), Killing Degree Days (KDD), Vapor Pressure Deficit (VPD), and precipitation (PCPN), from 1981 to 2018 in three Corn Belt states, including Illinois, Indiana, and Iowa. Allowing model coefficients varying in both temporal and spatial dimensions gives the best performance of STVC in simulating the corn yield responses toward various meteorological conditions. The STVC reduced the root-mean-square error to 10.64 Bu/Ac (0.72 Mg/ha) from 15.68 Bu/Ac (1.06 Mg/ha) for TVC and 16.48 Bu/Ac (1.11 Mg/ha) for SVC. Meanwhile, the STVC resulted in a higher R2 of 0.81 compared to 0.56 for SVC and 0.64 for TVC. The STVC showed better performance in handling spatial dependence of corn production, which tends to cluster estimation residuals when counties are close, with the lowest Moran’s I of 0.10. Considering the spatiotemporal non-stationarity, the proposed model significantly improves the power of the meteorological data in explaining the variations of corn yields. 

We establish the consistency of K-medoids in the context of metric spaces. We start by proving that K-medoids is asymptotically equivalent to K-means restricted to the support of the underlying distribution under general conditions, including a wide selection of loss functions. This asymptotic equivalence, in turn, enables us to apply the work of Pärna (1986) on the consistency of K-means. This general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available. We provide some numerical experiments to illustrate our theory. 

reveal the mechanism of intermittent coupling, where the nodes are connected merely in discontinuous time durations. Instead of the common weighted average technique, by proposing a direct error method and constructing piecewise Lyapunov functions, several intermittently adaptive designs are developed to update the complex-valued coupling weights. Especially, an adaptive pinning protocol is designed for ICCVNs with heterogeneous coupling weights and the synchronization is ensured by piecewise adjusting the complex-valued weights of edges within a spanning tree. For ICCVNs with homogeneous coupling weights, based on a connected dominating set, an intermittently adaptive algorithm is developed which just depends on the information of the dominating set with their neighbors. At the end, the established theoretical results are verified by providing two numerical examples. 

A non-aqueous proton electrolyte is devised by dissolving H3PO4 into acetonitrile. The electrolyte exhibits unique vibrational signatures from stimulated Raman spectroscopy. Such an electrolyte exhibits unique characteristics compared to aqueous acidic electrolytes: 1) higher (de)protonation potential for a lower desolvation energy of protons, 2) better cycling stability by dissolution suppression, and 3) higher Coulombic efficiency owing to the lack of oxygen evolution reaction. Two non-aqueous proton full cells exhibit better cycling stability, higher Coulombic efficiency, and less self-discharge compared to the aqueous counterpart. 

Abstract—Excessive power during in–field testing can cause multiple issues, including invalidation of the test results, overheating, and damage to the circuit. In this paper, we evaluate the reduction of capture power when specific segments of a scan chain can be kept from capturing data subject to values stored in a control register. The proposed approach requires no changes to the Automatic Test Pattern Generation (ATPG), no redesign of the circuitry to match a particular test set, and no additional patterns to maintain fault coverage. We will show that our approach can achieve very high capture power reduction—approaching 100% for multiple patterns. Index Terms—Design for Testability (DFT), Low Power Test, On-Chip Decompressor