Study on space-time structure of Higgs boson decay using HBT correlation Method in e+^+e−^- collision at s\sqrt{s}=250 GeV

The space-time structure of the Higgs boson decay are carefully studied with the HBT correlation method using e$^+$e$^-$ collision events produced through Monte Carlo generator PYTHIA 8.2 at $\sqrt{s}$=250GeV. The Higgs boson jets (Higgs-jets) are identified by H-tag tracing. The measurement of the Higgs boson radius and decay lifetime are derived from HBT correlation of its decay final state pions inside Higgs-jets in the e$^+$e$^-$ collisions events with an upper bound of $R_H \le 1.03\pm 0.05$ fm and $\tau_H \le (1.29\pm0.15)\times 10^{-7}$ fs. This result is consistent with CMS data.Comment: 7 pages,3 figure

A new design methodology of highly reliable TFT based integrated circuits in display applications

Thin-film transistors (TFTs) technology is currently the dominant technology for pixel switching in display application. The new consumer electronics requires higher resolution and brightness, lower power consumption, multi-functional with new features such as flexible and foldable display. This drives TFT devices to deliver more complex functions. Owing to a sustained, enormous effort in TFT research and development and a continuous capital investment from the display industry around the world for the past three decades, the performance of TFT has not only surpassed the display requirements in most areas, but also go beyond the simply switch to more complex digital and analogue integrated circuits, for example, the flexible and narrow bezel displays integrated row drivers with TFT technology next to the pixel array. Such integrated circuits comprise thousands of switches operating together, requires an accurate analysis during design. In the recent years, new display technologies, such as organic light-emitting diode (OLED) display and light-emitting diode (LED) displays have been emerging and become commercial reality due to certain advantages like self-luminous, high contrast, and etc. However, the OLED device has relative shorted lifetime and the current driving TFTs typically suffer from the electrical instability issue under high temperature and long-time stress condition. Thus, the reliability concerns in display have generated a considerable number of experimental studies and require careful analysis for the design of its pixel and integrated drivers. Particularly, individual TFTs are exposed to various stress condition in display operation with different degradation such as threshold voltage shift (ΔVth) or mobility (μ) decreasing result in a failure of display operation, given that the performance of an aging TFT might deviate from expectation of original design, and moreover, it might influence its neighboring TFTs. Traditional design method considering device performance variation and device-level aging approach of ΔVth and μ may not appropriate given that the traditional design of display pixel and driver circuit did not consider the evolutionary effects to each TFTs and different aging rate under various stress condition. Please click Additional Files below to see the full abstract

Align Yourself: Self-supervised Pre-training for Fine-grained Recognition via Saliency Alignment

Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success on various downstream tasks such as image classification and object detection, self-supervised pre-training for fine-grained scenarios is not fully explored. In this paper, we first point out that current contrastive methods are prone to memorizing background/foreground texture and therefore have a limitation in localizing the foreground object. Analysis suggests that learning to extract discriminative texture information and localization are equally crucial for self-supervised pre-training in fine-grained scenarios. Based on our findings, we introduce cross-view saliency alignment (CVSA), a contrastive learning framework that first crops and swaps saliency regions of images as a novel view generation and then guides the model to localize on the foreground object via a cross-view alignment loss. Extensive experiments on four popular fine-grained classification benchmarks show that CVSA significantly improves the learned representation.Comment: The second version of CVSA. 10 pages, 4 figure

High density NV sensing surface created via He^(+) ion implantation of (12)^C diamond

We present a promising method for creating high-density ensembles of nitrogen-vacancy centers with narrow spin-resonances for high-sensitivity magnetic imaging. Practically, narrow spin-resonance linewidths substantially reduce the optical and RF power requirements for ensemble-based sensing. The method combines isotope purified diamond growth, in situ nitrogen doping, and helium ion implantation to realize a 100 nm-thick sensing surface. The obtained 10^(17) cm^(-3) nitrogen-vacancy density is only a factor of 10 less than the highest densities reported to date, with an observed spin resonance linewidth over 10 times more narrow. The 200 kHz linewidth is most likely limited by dipolar broadening indicating even further reduction of the linewidth is desirable and possible.Comment: 5 pages including references. 3 figure