Evaluation and Optimization of Rendering Techniques for Autonomous Driving Simulation

In order to meet the demand for higher scene rendering quality from some autonomous driving teams (such as those focused on CV), we have decided to use an offline simulation industrial rendering framework instead of real-time rendering in our autonomous driving simulator. Our plan is to generate lower-quality scenes using a game engine, extract them, and then use an IQA algorithm to validate the improvement in scene quality achieved through offline rendering. The improved scenes will then be used for training

Realtime routing and messaging in wireless networks

Wireless technology has rapidly expanded in recent years; however, until now, it is only used to extend services in wired networks, applications that can take full advantage of mobile communication have not appeared yet. In this thesis, a practical messaging application for mobile computing is presented and a realtime Zone Routing Protocol (ZRP) for underlying wireless networks is evaluated. Mobile Yellow page Messaging and Retrieval (MYMAR) system is a new application for self-organized IEEE 802.11 compliant WLANs which consist of one Base Station (BS) and several Mobile Stations (MS) in each WLAN segment. In this distributed free public-access mobile network, BSs provide yellow page-like local information, such as digital maps, business advertisements, and business locations, to enable mobile receivers to quickly determine driving direction and navigate facilities of an intended service. Users retrieve and navigate such information via a user-friendly, voice recognition based hand-free command system. Other services available are voice messaging between users and voice-activated global web wireless access. The basic architecture and system operation of this wireless application system is shown. A suitable routing strategy is also necessary to forward packets between WLAN segments. A realtime Zone Routing Protocol (ZRP), which combines proactive routing for in-zone traffic and on-demand routing for out-zone traffic, was implemented. Some optimal configurations, such as Early Detection/Termination and Backward Route Accumulation, were made to improve the ZRP performance in mobile wireless network. The processing time for each ZRP subtask was evaluated

Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.Comment: AAAI202

Entanglement-Enhanced Matter-Wave Interferometry in a High-Finesse Cavity

Entanglement is a fundamental resource that allows quantum sensors to surpass the standard quantum limit set by the quantum collapse of independent atoms. Collective cavity-QED systems have succeeded in generating large amounts of directly observed entanglement involving the internal degrees of freedom of laser-cooled atomic ensembles. Here we demonstrate cavity-QED entanglement of external degrees of freedom to realize a matter-wave interferometer of 700 atoms in which each individual atom falls freely under gravity and simultaneously traverses two paths through space while also entangled with the other atoms. We demonstrate both quantum non-demolition measurements and cavity-mediated spin interactions for generating squeezed momentum states with directly observed metrological gain $3.4^{+1.1}_{-0.9}$ dB and $2.5^{+0.6}_{-0.6}$ dB below the standard quantum limit respectively. An entangled state is for the first time successfully injected into a Mach-Zehnder light-pulse interferometer with $1.7^{+0.5}_{-0.5}$ dB of directly observed metrological enhancement. Reducing the fundamental quantum source of imprecision provides a new resource that can be exploited to directly enhance measurement precision, bandwidth, and accuracy or operate at reduced size. These results also open a new path for combining particle delocalization and entanglement for inertial sensors, searches for new physics, particles, and fields, future advanced gravitational wave detectors, and accessing beyond mean-field quantum many-body physics.Comment: Main Text with Supporting Material, 13 pages, 4 figure