Vector Dark Matter Detection using the Quantum Jump of Atoms

The hidden sector U(1) vector bosons created from inflationary fluctuations can be a substantial fraction of dark matter if their mass is around $10^{-5}$eV. The creation mechanism makes the vector bosons' energy spectral density $\rho_{cdm}/\Delta E$ very high. Therefore, the dark electric dipole transition rate in atoms is boosted if the energy gap between atomic states equals the mass of the vector bosons. By using the Zeeman effect, the energy gap between the 2S state and the 2P state in hydrogen atoms or hydrogen like ions can be tuned. The $2S$ state can be populated with electrons due to its relatively long life, which is about $1/7$s. When the energy gap between the semi-ground $2S$ state and the 2P state matches the mass of the cosmic vector bosons, induced transitions occur and the 2P state subsequently decays into the 1S state. The $2P\to1S$ decay emitted Lyman-$\alpha$ photons can then be registered. The choices of target atoms depend on the experimental facilities and the mass ranges of the vector bosons. Because the mass of the vector boson is connected to the inflation scale, the proposed experiment may provide a probe to inflation.Comment: 5 pages, 3 figures; references added; matches version published in PL

Stimulated Decay of Collapsing Axion Stars and Fast Radio Bursts

The radiation mechanism of fast radio bursts (FRBs) has been extensively studied but still remains elusive. In the search for dark matter candidates, the QCD axion and axionlike particles (ALPs) have emerged as prominent possibilities. These elusive particles can aggregate into dense structures called axion stars through Bose-Einstein condensation (BEC). Such axion stars could constitute a significant portion of the mysterious dark matter in the universe. When these axion stars grow beyond a critical mass, usually through processes like accretion or merging, they undergo a self-driven collapse. Traditionally, for spherically symmetric axion clumps, the interaction between axions and photons does not lead to parametric resonance, especially when the QCD axion-photon coupling is at standard levels. Nevertheless, our study indicates that even QCD axion stars with typical coupling values can trigger stimulated decay during their collapse, rather than producing relativistic axions through self-interactions. This process results in short radio bursts, with durations of around 0.1 seconds, and can be potentially observed using radio telescopes like FAST or SKA. Furthermore, we find that collapsing axion stars for ALPs with specific parameters may emit radio bursts lasting just milliseconds with a peak luminosity of $1.60\times10^{42}\rm{erg/s}$, matching the characteristics of the observed non-repeating FRBs

Can Planet 9 be an Axion Star?

The anomalous orbits of Trans-Neptunian Objects (TNOs) can be explained by the Planet 9 hypothesis. We propose that the Planet 9 can be an axion star. Axion stars are gravitational bound clusters condensed by QCD axions or axion-like particles (ALPs), which we call axions for brevity. We find that the probability of capturing an axion star is the same order of magnitude as the probability of capturing an free floating planet (FFP), and even higher for the case of axion star, with axion star mass $5M_\oplus$ and $\Omega_{\rm{AS}}/\Omega_{\rm{DM}}\simeq 1/10$. Although axion star can emit monochromatic signals through two-photon decay, we find that the frequency of decay photon is either not within the frequency range of the radio telescope, or the decay signal is too weak to be detected. Therefore, if Planet 9 is composed by an axion star, it will be difficult to distinguish it from an isolated primordial black hole by spontaneous decay of axion

FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction

We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV.Comment: The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-U

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable

Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Delay-Doppler Waveforms in Doubly Selective Channels

Delay-Doppler waveform design has been considered as a promising solution to achieve reliable communication under high-mobility channels for the space-air-ground-integrated networks (SAGIN). In this paper, we introduce the cyclic delay-Doppler shift (CDDS) technique for delay-Doppler waveforms to extract transmit diversity in doubly selective channels. Two simple CDDS schemes, named time-domain CDDS (TD-CDDS) and modulation-domain CDDS (MD-CDDS), are proposed in the setting of multiple-input multiple-output (MIMO). We demonstrate the applications of CDDS on two representative delay-Doppler waveforms, namely orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM), by deriving their corresponding CDDS matrices. Furthermore, we prove theoretically and experimentally that CDDS can provide OTFS and AFDM with full transmit diversity gain on most occasions

Future trends and research issues of technology-enhanced language learning: A technological perspective

With recent advancements in information technologies and language learning models, rapid innovations of technology-enhanced language learning have been widely witnessed by research communities and educational institutions globally. Powerful new technologies, such as social media and networks, mobile applications, wearable computing, cloud computing, and virtual reality have been integrated into language learning to facilitate various aspects, such as interactivity, immediacy, and authenticity. In this study, we present the Future TELL Model considering learning objectives, theories, and strategies by briefly reviewing recent progresses in this area. Future trends and research issues in technology-enhanced language learning are also discussed in relation to cutting-edge technologies, such as deep neural networks, which have not yet been fully recognized by education technology communities