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Creators/Authors contains: "Yin, Hang"

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  1. Free, publicly-accessible full text available October 26, 2023
  2. Abstract

    We propose a new observable for the measurement of the forward–backward asymmetry$$(A_{FB})$$(AFB)in Drell–Yan lepton production. At hadron colliders, the$$A_{FB}$$AFBdistribution is sensitive to both the electroweak (EW) fundamental parameter$$\sin ^{2} \theta _{W}$$sin2θW, the weak mixing angle, and the parton distribution functions (PDFs). Hence, the determination of$$\sin ^{2} \theta _{W}$$sin2θWand the updating of PDFs by directly using the same$$A_{FB}$$AFBspectrum are strongly correlated. This correlation would introduce large bias or uncertainty into both precise measurements of EW and PDF sectors. In this article, we show that the sensitivity of$$A_{FB}$$AFBon$$\sin ^{2} \theta _{W}$$sin2θWis dominated by its average value around theZpole region, while the shape (or gradient) of the$$A_{FB}$$AFBspectrum is insensitive to$$\sin ^{2} \theta _{W}$$sin2θWand contains important information on the PDF modeling. Accordingly, a new observable related to the gradient of the spectrum is introduced, and demonstrated to be able to significantly reduce the potential bias on the determination of$$\sin ^{2} \theta _{W}$$sin2θWwhen updating the PDFs using the same$$A_{FB}$$AFBdata.

  3. Abstract

    Cable bacteria that are capable of transporting electrons on centimeter scales have been found in a variety of sediment types, where their activity can strongly influence diagenetic reactions and elemental cycling. In this study, the patterns of spatial and temporal colonization of surficial sediment by cable bacteria were revealed in two-dimensions by planar pH and H2S optical sensors for the first time. The characteristic sediment surface pH maximum zones begin to develop from isolated micro-regions and spread horizontally within 5 days, with lateral spreading rates from 0.3 to ~ 1.2 cm day−1. Electrogenic anodic zones in the anoxic sediments are characterized by low pH, and the coupled pH minima also expand with time. H2S heterogeneities in accordance with electrogenic colonization are also observed. Cable bacteria cell abundance in oxic surface sediment (0–0.25 cm) kept almost constant during the colonization period; however, subsurface cell abundance apparently increased as electrogenic activity expanded across the entire surface. Changes in cell abundance are consistent with filament coiling and growth in the anodic zone (i.e., cathodic snorkels). The spreading mechanism for the sediment pH–H2S fingerprints and the cable bacteria abundance dynamics suggest that once favorable microenvironments are established, filamentous cable bacteria aggregate or locally activate electrogenic metabolism. Different development dynamicsmore »in otherwise similar sediment suggests that the accessibility of reductant (e.g., dissolved phase sulfide) is critical in controlling the growth of cable bacteria.

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  4. Loss of tidal wetlands is a world-wide phenomenon. Many factors may contribute to such loss, but among them are geochemical stressors such as exposure of the marsh plants to elevated levels on hydrogen sulfide in the pore water of the marsh peat. Here we report the results of a study of the geochemistry of iron and sulfide at different seasons in unrestored (JoCo) and partially restored (Big Egg) salt marshes in Jamaica Bay, a highly urbanized estuary in New York City where the loss of salt marsh area has accelerated in recent years. The spatial and temporal 2-dimensional distribution patterns of dissolved Fe 2+ and H 2 S in salt marshes were in situ mapped with high resolution planar sensors for the first time. The vertical profiles of Fe 2+ and hydrogen sulfide, as well as related solutes and redox potentials in marsh were also evaluated by sampling the pore water at discrete depths. Sediment cores were collected at various seasons and the solid phase Fe, S, N, C, and chromium reducible sulfide in marsh peat at discrete depths were further investigated in order to study Fe and S cycles, and their relationship to the organic matter cycling at differentmore »seasons. Our results revealed that the redox sensitive elements Fe 2+ and S 2– showed significantly heterogeneous and complex three dimensional distribution patterns in salt marsh, over mm to cm scales, directly associated with the plant roots due to the oxygen leakage from roots and redox diagenetic reactions. We hypothesize that the oxic layers with low/undetected H 2 S and Fe 2+ formed around roots help marsh plants to survive in the high levels of H 2 S by reducing sulfide absorption. The overall concentrations of Fe 2+ and H 2 S and distribution patterns also seasonally varied with temperature change. H 2 S level in JoCo sampling site could change from <0.02 mM in spring to >5 mM in fall season, reflecting significantly seasonal variation in the rates of bacterial oxidation of organic matter at this marsh site. Solid phase Fe and S showed that very high fractions of the diagenetically reactive iron at JoCo and Big Egg were associated with pyrite that can persist for long periods in anoxic sediments. This implies that there is insufficient diagenetically reactive iron to buffer the pore water hydrogen sulfide through formation of iron sulfides at JoCo and Big Egg.« less
  5. Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatiotemporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments.
  6. Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatio-temporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments.