Transverse momentum resummation for color sextet and antitriplet scalar production at the LHC

We study the factorization and resummation of the transverse momentum spectrum of the color sextet and antitriplet scalars produced at the LHC based on soft-collinear effective theory. Compared to Z boson and Higgs production, a soft function is required to account for the soft gluon emission from the final-state colored scalar. The soft function is calculated at the next-to-leading order, and the resummation is performed at the approximate next-to-next-to-leading logarithmic accuracy. The non-perturbative effects and PDF uncertainties are also discussed.Comment: 20 pages, 7 figure

Origin of platelike granular structure for the ultrananocrystalline diamond films synthesized in H2-containing Ar/CH4 plasma

[[abstract]]The modification on microstructure of diamond films due to the incorporation of H2 species into the Ar/CH4 plasma was systematically investigated. While the hydrogen-free plasma produced the ultrananocrystalline diamond films with equiaxed grains (about 5 nm in size), the hydrogen-containing plasma resulted in platelike grains (about 100×300 nm2 in size). The size of the platelike grains increased with the H2 content in the plasma. Transmission electron microscopy and optical emission spectroscopy reveal that only 0.1%H2 incorporated in the Ar/CH4 plasma is sufficient for inducing the formation of platelike grains, suggesting that the platelike grains are formed via the competition between the attachment and the etching of hydrocarbons onto the existing diamond surfaces. In Ar plasma, the diamond grains were always passivated with hydrocarbons and the active carbon species in the plasma can only renucleate to form nanocrystalline diamond grains. Incorporation of H2 species in the plasma leads to partial etching of hydrocarbons adhered onto the diamond grains, such that active carbon species in the plasma can attach to diamond surface anisotropically, resulting in diamond flakes and dendrites geometry.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Growth behavior of nanocrystalline diamond films on ultrananocrystalline diamond nuclei: The transmission electron microscopy studies

[[abstract]]Micron-crystalline diamond (MCD) films with a unique microstructure were synthesized using a modified nucleation and growth process, in which a thin layer of ultrananocrystalline diamond (UNCD) was used as nucleation layer for growing diamond films in H2-plasma. Thus obtained (MCD)UNCD diamond films consist of nanosized diamond clusters ( ∼ 10 nm in size) surrounding the large diamond grains ( ∼ 300 nm in size), exhibiting better electron field emission (EFE) properties than the conventional diamond materials with faceted grains. The EFE of these (MCD)UNCD films can be turned on at E0 = 11.1 V/μm, achieving EFE current density as large as (Je) = 0.7 mA/cm2 at 25 V/μm applied field, which can be attributed to the presence of large proportion of UNCD grains lying in between the MCD grains, forming an electron conduction path and thus facilitating the EFE process. Transmission electron microscopy examinations reveal that such a unique microstructure was formed by agglomeration and coalescence of the nanosized UNCD grains.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙

Tax policy and innovation performance: Evidence from enactment of the Alternative Simplified Credit

We examine how direct tax incentives affect firm innovation performance using a new U.S. R&D tax credit regime enacted in 2007, the Alternative Simplified Credit (ASC). A difference-in- differences analysis indicates that innovation performance is poorer for ASC users than for firms using the original R&D tax credit method following the ASC enactment. The results are stronger for firms with poorer governance and greater innovation diversity. ASC users suffer from poorer profitability and lower valuations. The findings remain robust to self-selection bias and various robustness checks. Our evidence favors a dark-side view of R&D tax credit effects under the ASC

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive uncertainty in the target domain, a crucial aspect of the safe deployment of UDA models, has received limited attention. The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data. Recent approaches have employed importance-weighting techniques to estimate the target-optimal temperature based on re-weighted labeled source data. Nonetheless, these methods require source data and suffer from unreliable density estimates under severe domain shifts, rendering them unsuitable for source-free UDA settings. To overcome these limitations, we propose PseudoCal, a source-free calibration method that exclusively relies on unlabeled target data. Unlike previous approaches that treat UDA calibration as a \textit{covariate shift} problem, we consider it as an unsupervised calibration problem specific to the target domain. Motivated by the factorization of the negative log-likelihood (NLL) objective in TempScal, we generate a labeled pseudo-target set that captures the structure of the real target. By doing so, we transform the unsupervised calibration problem into a supervised one, enabling us to effectively address it using widely-used in-domain methods like TempScal. Finally, we thoroughly evaluate the calibration performance of PseudoCal by conducting extensive experiments on 10 UDA methods, considering both traditional UDA settings and recent source-free UDA scenarios. The experimental results consistently demonstrate the superior performance of PseudoCal, exhibiting significantly reduced calibration error compared to existing calibration methods

Using a Hybrid Evolutionary Algorithm for Solving Signal Transmission Station Location and Allocation Problem with Different Regional Communication Quality Restriction

This study aims to investigate the signal transmission station location-allocation problems with the various restricted regional constraints. In each constraint, the types of signal transmission stations and the corresponding numbers and locations are to be decided at the same time. Inappropriate set up of stations is not only causing the unnecessary cost but also making the poor service quality. In this study, we proposed a hybrid evolutionary approach integrating the immune algorithm with particle swarm optimization (IAPSO) to solve this problem where each of the regions is with different maximum failure rate restrictions. We compared the performance of the proposed method with commercial optimization software LINGO®. According to the experimental results, solutions obtained by our IAPSO are better than or as well as the best solutions obtained by LINGO®. It is expected that our research can provide the telecommunication enterprise the optimal/near-optimal strategies for the setup of signal transmission stations