And i am thrilled to be talking with rob, professor of the university of texas liberal arts and Centennial Professor of sociology, hes a sociologist, psychologist, conducts research on child and adolescent development with focus on educational inequity. We are super excited to talk about his book, the starting line today. This is his eighth book and rob, would you like to say a few words about yourself . Yeah, i will just add so that. I would start off in events like this and i grew up in texas, im a native texan and i was educated in texas public schools and not just the suburban variety either. When i came back here 20 years to start my career studying Child Development and educational inequality, i was really invested in the idea that texas might be a special place and in the broad topic. I wanted to say something else that what i read in this book and what im going to talk about today or what we are going to talk about today is very young children going to school. I dont know if an
to review a key meeting among donald trump, michael cohen, and the prosecution s first witness, former tabloid executive and trump friend david pecker. all of that key evidence about the alleged conspiracy mr. trump is accused of to unlawfully influence the 2016 election. this is pivotal, because as judge merchan noted in his jury instructions, former president trump cannot be convicted based on cohen s testimony alone, since trump s former fixer is considered, by law, an accomplice. jurors must be taking a closer look from what they have asked for at pecker s testimony in order to corroborate cohen. jurors wanted to rehear judge merchan s guidance on how much they can reasonably infer from the evidence. armed with this refresher, the jurors are now back behind closed doors where they have been told they can work as late as 6:00 p.m. tonight. while deliberations tonight, mr. trump has to stay in the courthouse. his son don junior providing a look in a tiktok video. we begin w
Internet of Things (IoT) networks have gained significant attention in recent years as it has the potential to transform various industries. A key concern, however, is that sensing devices have limited operational lifetime. Specifically, they have finite energy, which affects the amount of data they are able to collect and upload. One solution is to power these devices wirelessly, where devices harvest energy from Radio Frequency (RF) signals from transmitters such as a Hybrid Access Point (HAP). A key issue, however, is that energy delivery and data transmissions may be conducted on the same frequency band. This means a HAP has to determine a transmission schedule for energy or/and data transmissions. Another issue is that the channel gain of devices varies over time, which affects the amount of harvested energy and transmitted data. In this respect, a challenging issue is that an HAP has causal channel state information only, meaning it is not aware of energy arrivals or channel gain
This paper considers Radio Frequency (RF) energy harvesting devices that use an Irregular Slotted Aloha (IRSA) channel access protocol to transmit their data to a Hybrid Access Point (HAP). Specifically, it addresses the fundamental problem of optimizing the number of packet replicas transmitted by each device in each time frame. Unlike prior works, it considers a learning approach to optimize the number of replicas according to the energy level of devices. This paper first uses a model-based Markov Decision Process (MDP) to study the problem at hand. Then it proposes a model-free, centralized and a distributed Q-learning-based solution that aim to maximize the number of successful transmissions in each time frame. Our simulation results show that our centralized and distributed solutions respectively achieve up to 38% and 29% more successful transmissions than conventional Aloha.
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