Moving P2P Live Streaming to Mobile and Ubiquitous Environment

Media streams distribution over a wired network to static hosts can be realized by Client/Server mode or Peer-to-Peer overlay networks. However, if the end hosts are mobile over heterogeneous wireless access networks, one needs to consider many operational issues such as network detection, handoff, join and leave latency, and desired level of quality of service, as well as caching. In the latest researches, one popular P2P live streaming system, called AnySee, over the wired network, has been deployed and widely used. Based on the AnySee system, this paper proposed and implemented one hybrid live streaming system, AnySee-Mobile, under wired and wireless environment. In the system, one wireless peer will be selected to act as an agent. One agent has two main functions, to request media from P2P overlay network as a normal peer, and to multicast media to WLAN as a multicast source. In this paper we study, how to elect one multicast agent in WLAN. Several experimentations have been made and proved that the system has good user experiences and performances

Hollow spherical SiO2 micro-container encapsulation of LiCl for high-performance simultaneous heat reallocation and seawater desalination

Energy & fresh water have both become scarce resources in the modern era of human society. Sorption-based technology is environmentally friendly and energy-efficient and can be driven by low-grade energy to transfer energy and produce fresh water. Here, we report a solid sorbent fabricated by encapsulating a hygroscopic salt, lithium chloride (LiCl), inside micro-sized hollow-structured SiO2. This composite sorbent (LiCl@HS) exhibits 6 times faster water vapor sorption kinetics than pure LiCl and a water vapor sorption capacity of 1.7 kg kg-1 at a relative humidity (RH) of 50%, which is the highest ever reported for any solid sorbent in the literature. The low regeneration temperature (<80 °C) and good cycling stability ensure the feasibility of the composite sorbent for use in practical applications. The thermodynamic calculations reveal that the sorbent is able to continuously supply 20 °C temperature lift with a maximum coefficient of performance (COP) for cooling of 0.97 and COP for heating of 1.89 while simultaneously producing 9.05 kg potable water per kilogram sorbent daily using seawater as the source water and solar energy as the sole energy source. A homemade system is developed and its practical performance in providing seasonally switchable heating and cooling along with clean water production from source water with an impaired quality is successfully verified, indicating its great potential

Rethinking Learning Rate Tuning in the Era of Large Language Models

Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverage fine-tuning to adapt LLMs for various real-world applications due to the prohibitive expenses associated with LLM training. The learning rate is one of the most important hyperparameters in LLM fine-tuning with direct impacts on both fine-tuning efficiency and fine-tuned LLM quality. Existing learning rate policies are primarily designed for training traditional deep neural networks (DNNs), which may not work well for LLM fine-tuning. We reassess the research challenges and opportunities of learning rate tuning in the coming era of Large Language Models. This paper makes three original contributions. First, we revisit existing learning rate policies to analyze the critical challenges of learning rate tuning in the era of LLMs. Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs. Third, our experimental analysis with LRBench++ demonstrates the key differences between LLM fine-tuning and traditional DNN training and validates our analysis