Dynamic Scoring: Alternative Financing Schemes

Neoclassical growth models predict that reductions in capital or labor tax rates are expansionary when lump-sum transfers are used to balance the government budget. This paper explores the consequences of bond-financed tax reductions that bring forth a range of possible offsetting policies, including future government consumption, capital tax rates, or labor tax rates. Through the resulting intertemporal distortions, current tax cuts can be contractionary. The paper also finds that more aggressive responses of offsetting policies to debt engender less debt accumulation and less costly tax cuts.Revenue feedback, capital tax, labor tax, debt management

Dynamic Scoring: Alternative Financing Schemes

Neoclassical growth models predict that reductions in capital or labor tax rates are expansionary when lump-sum transfers are used to balance the government budget. This paper explores the consequences of bond-financed tax reductions that bring forth a range of possible offsetting policies, including future government consumption, capital tax rates, or labor tax rates. Through the resulting intertemporal distortions, current tax cuts can be contractionary. The paper also finds that more aggressive responses of offsetting policies to debt engender less debt accumulation and less costly tax cuts.

FISCAL FORESIGHT AND INFORMATION FLOWS

Fiscal foresight---the phenomenon that legislative and implementation lags ensure that private agents receive clear signals about the tax rates they face in the future---is intrinsic to the tax policy process. This paper develops an analytical framework to study the econometric implications of fiscal foresight. Simple theoretical examples show that foresight produces equilibrium time series with nonfundamental representations, which misalign the agents' and the econometrician's information sets. Economically meaningful shocks to taxes, therefore, cannot generally be extracted from statistical innovations in conventional ways. Econometric analyses that fail to align agents' and the econometrician's information sets can produce distorted inferences about the effects of tax policies. The paper documents the sensitivity of econometric inferences of tax effects to details about how tax information flows into the economy. We show that alternative assumptions about the information flows that give rise to fiscal foresight can reconcile the diverse empirical findings in the literature on anticipated tax changes.

Towards offering more useful data reliably to mobile cloudfrom wireless sensor network

The integration of ubiquitous wireless sensor network (WSN) and powerful mobile cloud computing (MCC) is a research topic that is attracting growing interest in both academia and industry. In this new paradigm, WSN provides data to the cloud, and mobile users request data from the cloud. To support applications involving WSN-MCC integration, which need to reliably offer data that are more useful to the mobile users from WSN to cloud, this paper first identifies the critical issues that affect the usefulness of sensory data and the reliability of WSN, then proposes a novel WSN-MCC integration scheme named TPSS, which consists of two main parts: 1) TPSDT (Time and Priority based Selective Data Transmission) for WSN gateway to selectively transmit sensory data that are more useful to the cloud, considering the time and priority features of the data requested by the mobile user; 2) PSS (Priority-based Sleep Scheduling) algorithm for WSN to save energy consumption so that it can gather and transmit data in a more reliable way. Analytical and experimental results demonstrate the effectiveness of TPSS in improving usefulness of sensory data and reliability of WSN for WSN-MCC integration

Pure spin current in a two-dimensional topological insulator

We predict a mechanism to generate a pure spin current in a two-dimensional topological insulator. As the magnetic impurities exist on one of edges of the two-dimensional topological insulator, a gap is opened in the corresponding gapless edge states but another pair of gapless edge states with opposite spin are still protected by the time-reversal symmetry. So the conductance plateaus with the half-integer values $e^2/h$ can be obtained in the gap induced by magnetic impurities, which means that the pure spin current can be induced in the sample. We also find that the pure spin current is insensitive to weak disorder. The mechanism to generate pure spin currents is generalized for two-dimensional topological insulators.Comment: 5 pages, 6 figure

Cucurbitacin E inhibits the Yes‑associated protein signaling pathway and suppresses brain metastasis of human non‑small cell lung cancer in a murine model.

Human non‑small cell lung cancer (NSCLC) is associated with an extremely poor prognosis especially for the 40% of patients who develop brain metastasis, and few treatment strategies exist. Cucurbitacin E (CuE), an oxygenated tetracyclic triterpenoid isolated from plants particularly of the family Cucurbitaceae, has shown anti‑tumorigenic properties in several types of cancer, yet the mechanism remains unclear. Yes‑associated protein (YAP), a main mediator of the Hippo signaling pathway, promotes tumorigenesis, drug resistance and metastasis in human NSCLC. The present study was designed to ascertain whether CuE inhibits YAP and its downstream gene expression in the human NSCLC cell lines H2030‑BrM3 (K‑rasG12C mutation) and PC9‑BrM3 (EGFRΔexon19 mutation), which have high potential for brain metastasis. The efficacy of CuE in suppressing brain metastasis of H2030‑BrM3 cells in a murine model was also investigated. It was found that after CuE treatment in H2030‑BrM3 and PC9‑BrM3 cells, YAP protein expression was decreased, and YAP signaling GTIIC reporter activity and expression of the downstream genes CTGF and CYR61 were significantly (P<0.01) decreased. CuE treatment also reduced the migration and invasion abilities of the H2030‑BrM3 and PC9‑BrM3 cells. Finally, our in vivo study showed that CuE treatment (0.2 mg/kg) suppressed H2030‑BrM3 cell brain metastasis and that mice treated with CuE survived longer than the control mice treated with 10% DMSO (P=0.02). The present study is the first to demonstrate that CuE treatment inhibits YAP and the signaling downstream gene expression in human NSCLC in vitro, and suppresses brain metastasis of NSCLC in a murine model. More studies to verify the promising efficacy of CuE in inhibiting brain metastasis of NSCLC and various other cancers may be warranted

Fiscal Foresight: Analytics and Econometrics

Fiscal foresight---the phenomenon that legislative and implementation lags ensure that private agents receive clear signals about the tax rates they face in the future---is intrinsic to the tax policy process. This paper develops an analytical framework to study the econometric implications of fiscal foresight. Simple theoretical examples show that foresight produces equilibrium time series with a non-invertible moving average component, which misaligns the agents' and the econometrician's information sets in estimated VARs. Economically meaningful shocks to taxes, therefore, cannot be extracted from statistical innovations in conventional ways. Econometric analyses that fail to align agents' and the econometrician's information sets can produce distorted inferences about the effects of tax policies. Because non-invertibility arises as a natural outgrowth of the fact that agents' optimal decisions discount future tax obligations, it is likely to be endemic to the study of fiscal policy. In light of the implications of the analytical framework, we evaluate two existing empirical approaches to quantifying the impacts of fiscal foresight. The paper also offers a formal interpretation of the narrative approach to identifying fiscal policy

Residual magnifier: A dense information flow network for super resolution

© 2019 IEEE. Recently, deep learning methods have been successfully applied to single image super-resolution tasks. However, some networks with extreme depth failed to achieve better performance because of the insufficient utilization of the local residual information extracted at each stage. To solve the above question, we propose a Dense Information Flow Network (DIF-Net), which can fully extract and utilize the local residual information at each stage to accomplish a better reconstruction. Specifically, we present a Two-stage Residual Extraction Block (TREB) to extract the shallow and deep local residual information at each stage. The dense connection mechanism is introduced throughout the model and within TREBs to dramatically increase the information flow. Meanwhile this mechanism prevents the shallow features extracted earlier from being diluted. Finally, we propose a lightweight subnet (residual enhancer) to efficiently recycle the overflow residual information from the backbone net for detail enhancement of the residual image. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods with relatively-less parameters