Observation of resonant dipolar collisions in ultracold 23^{23}Na87^{87}Rb rotational mixtures

We report the investigation on dipolar collisions in rotational state mixtures of ultracold bosonic $^{23}$Na$^{87}$Rb molecules. The large resonant dipole-dipole interaction between molecules in rotational states of opposite parities brings about significant modifications to their collisions, even when an electric field is not present. In this work, this effect is revealed by measuring the dramatically enhanced two-body loss rate constants in the mixtures. In addition, the dipolar interaction strength can be tuned by preparing the NaRb mixture in different rotational levels with microwave spectroscopy. When the rotational level combination is not of the lowest energy, contributions from hyperfine changing collisions are also observed. Our measured loss rate constants are in good agreement with a quantum close-coupling calculation which we also present in full detail.Comment: Expanded version with the theory included in detai

Subnatural-Linewidth Polarization-Entangled Photon Pairs with Controllable Temporal Length

We demonstrate an efficient experimental scheme for producing polarization-entangled photon pairs from spontaneous four-wave mixing (SFWM) in a laser-cooled $^{85}$Rb atomic ensemble, with a bandwidth (as low as 0.8 MHz) much narrower than the rubidium atomic natural linewidth. By stabilizing the relative phase between the two SFWM paths in a Mach-Zehnder interferometer configuration, we are able to produce all four Bell states. These subnatural-linewidth photon pairs with polarization entanglement are ideal quantum information carriers for connecting remote atomic quantum nodes via efficient light-matter interaction in a photon-atom quantum network.Comment: Title changed, published version, 5 pages + 3 pages Supplemental Materia

Evaluating Learning-to-Rank Models for Prioritizing Code Review Requests using Process Simulation

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based Software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software techniques

One-for-All: Towards Universal Domain Translation with a Single StyleGAN

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the P space, effectively acting as a connector between these two spaces. The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality. UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks. The source code and trained models will be released to the public