Standard Model Mass Spectrum in Inflationary Universe

We work out the Standard Model (SM) mass spectrum during inflation with quantum corrections, and explore its observable consequences in the squeezed limit of non-Gaussianity. Both non-Higgs and Higgs inflation models are studied in detail. We also illustrate how some inflationary loop diagrams can be computed neatly by Wick-rotating the inflation background to Euclidean signature and by dimensional regularization.Comment: 62 pages, JHEP accepted versio

Neutrino Signatures in Primordial Non-Gaussianities

We study the cosmological collider phenomenology of neutrinos in an effective field theory. The mass spectrum of neutrinos and their characteristic oscillatory signatures in the squeezed limit bispectrum are computed. Both dS-covariant and slow-roll corrections are considered, so is the scenario of electroweak symmetry breaking during inflation. Interestingly, we show that the slow-roll background of the inflaton provides a chemical potential for the neutrino production. The chemical potential greatly amplifies the oscillatory signal and makes the signal observably large for heavy neutrinos without the need of fine tuning.Comment: 31 pages, JHEP accepted versio

Submillisecond-response polymer network liquid crystal phase modulators at 1.06-mu m wavelength

A fast-response and scattering-free polymer network liquid crystal (PNLC) light modulator is demonstrated at lambda = 1.06 mu m wavelength. A decay time of 117 mu s for 2 pi phase modulation is obtained at 70 degrees C, which is similar to 650 x faster than that of the host nematic LCs. The major tradeoff is the increased operating voltage. Potential applications include spatial light modulators and adaptive optics

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.Comment: Accepted by AAAI-202

Iodide‐Mediated Rapid and Sensitive Surface Etching of Gold Nanostars for Biosensing

Iodide‐mediated surface etching can tailor the surface plasmon resonance of gold nanostars through etching of the high‐energy facets of the nanoparticle protrusions in a rapid and sensitive way. By exploring the underlying mechanisms of this etching and the key parameters influencing it (such as iodide, oxygen, pH, and temperature), we show its potential in a sensitive biosensing system. Horseradish peroxidase‐catalyzed oxidation of iodide enables control of the etching of gold nanostars to spherical gold nanoparticles, where the resulting spectral shift in the surface plasmon resonance yields a distinct color change of the solution. We further develop this enzyme‐modulated surface etching of gold nanostars into a versatile platform for plasmonic immunoassays, where a high sensitivity is possible by signal amplification via magnetic beads and click chemistry