Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Expression and function of ATP-dependent potassium channels in zebrafish islet β-cells

ATP-sensitive potassium channels (K(ATP) channels) are critical nutrient sensors in many mammalian tissues. In the pancreas, K(ATP) channels are essential for coupling glucose metabolism to insulin secretion. While orthologous genes for many components of metabolism–secretion coupling in mammals are present in lower vertebrates, their expression, functionality and ultimate impact on body glucose homeostasis are unclear. In this paper, we demonstrate that zebrafish islet β-cells express functional K(ATP) channels of similar subunit composition, structure and metabolic sensitivity to their mammalian counterparts. We further show that pharmacological activation of native zebrafish K(ATP) using diazoxide, a specific K(ATP) channel opener, is sufficient to disturb glucose tolerance in adult zebrafish. That β-cell K(ATP) channel expression and function are conserved between zebrafish and mammals illustrates the evolutionary conservation of islet metabolic sensing from fish to humans, and lends relevance to the use of zebrafish to model islet glucose sensing and diseases of membrane excitability such as neonatal diabetes

Electroplating lithium transition metal oxides.

Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten salt electrodeposition approach to directly electroplate the important lithium-ion (Li-ion) battery cathode materials LiCoO2, LiMn2O4, and Al-doped LiCoO2. The crystallinities and electrochemical capacities of the electroplated oxides are comparable to those of the powders synthesized at much higher temperatures (700° to 1000°C). This new growth method significantly broadens the scope of battery form factors and functionalities, enabling a variety of highly desirable battery properties, including high energy, high power, and unprecedented electrode flexibility

The influence of TikTok on tourism destination decision in the post-pandemic era

This dissertation analyzes the influence of the social media Tik Tok on a tourism destination decision, mainly among particpants that belong to the Generation Z, who are heavy users of social media. The recognition of social media as a relevant communication channel has been increasing by companies and citizens. In this context, the awareness of the Chinese Tik Tok has fast increased in China, as well as expanding internationally. A quantitative study was designed expecting to cover not only Chinese participants. An online questionnaire in two languages, Chinese (Wenjuanxing 问卷星) and English (Google Form) was submitted to a convenience sample. Data could be obtained from 259 participants, with 177 valid participants. The multiple linear regression was applied to estimate the theoretical model. Results show the absence of mediating effects, but the influence of Tik Tok on destination decision, mainly for Generation Z, on five determinants explain it: Trust on TikTok, Travel influencer on TikTok, Types of travel videos on TikTok, and the main information sources to get information. Only the perceived risk from accidents, such as airplane hijacking or terrorist attack, and with common problems, such as health, political stability and security explain destination decision. Tik Tok main users belong to the Generation Z in China, but it is still not popular to Portuguese young adult. Future studies should analyze more determinants and mediating effects, as consider short videos using new technologies (AI, AR and VR).Esta dissertação analisa a influência da rede social Tik Tok na decisão de destino turistico, identificando os determinantes mais relevantes principalmente para os que pertencem à Geração Z, que são os maiores utilizadores de redes sociais. O reconhecimento das redes sociais enquanto importante canal de comunicação tem vindo a aumentar por empresas e cidadãos. Neste contexto, a notoriedade da Chinesa Tik Tok tem aumentado rapidamente na China e internacionalmente. Foi desenhado um estudo quantitativo esperando atingir não apenas participantes Chineses. Um questionário online redigido em chinês (Wenjuanxing 问卷星) e inglês (Google Form) foi submetido a uma amostra de conveniência. Obtiveram-se 259 respostas, das quais 177 válidas. A regressão linear múltipla foi usada para a estimação do modelo. Os resultados mostram ausência de efeitos mediadores, mas que o Tik Tok influencia a decisão de destino sobretudo para a Geração Z em cinco determinantes: Confiança no Tik Tok, do influenciador de viagem no Tik Tok, do tipo de vídeos no Tik Tok e das fontes de informação obtidas. Apenas o risco percebido com acidentes na viagem, como sequestro de avião ou ataque terrorista, e com problemas comuns como saúde, estabilidade política e segurança explicam a decisão do destino. Os maiores utilizadores do Tik Tok pertencem á Geração Z na China, mas para os outros jovens adultos esta rede social ainda não é popular. Estudos futuros devem analizar mais determinantes e efeitos mediadores, bem como considerar pequenos vídeos feitos com as novas tecnologias (IA, RA e RV)