Noise source importance in linear stochastic models of biological systems that grow, shrink, wander, or persist

While noise is an important factor in biology, biological processes often involve multiple noise sources, whose relative importance can be unclear. Here we develop tools that quantify the importance of noise sources in a network based on their contributions to variability in a quantity of interest. We generalize the edge importance measures proposed by Schmidt and Thomas [1] for first-order reaction networks whose steady-state variance is a linear combination of variance produced by each directed edge. We show that the same additive property extends to a general family of stochastic processes subject to a set of linearity assumptions, whether in discrete or continuous state or time. Our analysis applies to both expanding and contracting populations, as well as populations obeying a martingale (“wandering”) at long times. We show that the original Schmidt-Thomas edge importance measure is a special case of our more general measure, and is recovered when the model satisfies a conservation constraint

A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

K-12 Educational Leadership and Autism

The Center for Disease Control (CDC) estimates that about 1 in 54 children have been identified with autism spectrum disorder (ASD). Autism occurs among all ethic, socioeconomic, and racial groups. With this nationwide prevalence, educational leadership, Principals, Vice Principals, and parents must be in a continuous state of learning about autism and the unique needs of their autistic learners. The chapter is dedicated to helping parents and educational leadership to understand each other's roles and responsibilities in regard to serving children and youth on the autism spectrum. First, the chapter will explore literature regarding unique leadership characteristics needed to support youth on the autism spectrum. Next, an overview of literature available regarding educational leaders' perspectives and strategies in supporting youth on the spectrum. Finally, suggestions and strategies for developing educational leaders that understand and cherish youth on the autism spectrum are given.

Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

Organizational preparation of central public administration bodies to manage the implementation of defense tasks Part III. Concept of tasks and organization of the organisational units of administrative offices of chief bodies of government administration for the management of the implementation of defence tasks

Such bodies as the Council of Ministers, the Prime Minister and ministers in charge of departments of government administration, in order to exercise competencies in the field of defence, should have the ability to perform administrative functions to satisfy missions, goals and tasks in this matter assigned to them by the legislator. Their authority and duties in the defence field are closely related to their authority and duties in other areas of national security, so there is a need to arrange the organisational units set up for this purpose in such a way that their scope of action includes matters corresponding to the authority’s competence in the field of national security and defence, taken as a whole. Given the rank of the Council of Ministers and the Prime Minister in Poland, and their competencies in the area of national security, urgent changes are required to adapt the organisational units of the Chancellery of the Prime Minister (KPRM), and above all the Government Centre for Security (RCB). The RCB needs to be transformed so that it is able to fulfil the role of a national security and defence headquarters under the Council of Ministers and the Prime Minister. It would be an analytical-planning-coordination office, ensuring staff coordination of coherent, uninterrupted and continuous state activities in the field of state security and defence. Innovation in this respect would be accompanied by minor changes in the jurisdiction and structure of the organisational units comprising the KPRM. Following this, given the existing needs identified in the previous articles in this series, it seems necessary to make changes in ministries to implement a unified model of a national security organisational unit (e.g. Department for Security and Defence Affairs). In principle, these units should have similar missions and composition in all ministries, but some reasonable exceptions would occur in the Ministry of National Defence and the Ministry of the Interior and Administration. In others, there are and should be separate departments specific to those ministries (e.g. combating economic crime, international security policy, nature conservation, air protection and others).

Privacy and Smart Speakers in Research With Older Adults

Advances in artificial intelligence and computational linguistics have made smart speakers, such as Amazon Alexa^TM^ and Google Home^TM^, economical and widely available. For older adults particularly, devices with voice interfaces can help to overcome accessibility challenges that often accompany interaction with today’s technologies. However, voice-activation also requires devices to be in a continuous state of ambient listening, which can create a significant privacy risk for the user, one that is often amplified as smart speakers are placed in highly personal home spaces to facilitate their utility. Deployment of these devices in research settings poses additional risk, as traces of data filter through research teams, app developers, and third-party services that support research efforts. This presentation addresses the privacy aspects of deploying Google Home Mini^TM^ speakers in research that examined their feasibility for enhancing physical activity among sedentary older adults. Interviews with participants were conducted in two studies: the first included a demonstration of the device and physical activity program (n=15); and the second included in-home use of devices and a physical activity program (n=15). Content analysis of study documentation, field notes, and interviews revealed specific areas that require additional attention when utilizing smart speakers in research, including the capture of identifying information, protocols for data handling, and requirements for informed consent. These findings are discussed in context with extant literature on individual privacy concerns and behaviors related to smart household devices. Results from this study can inform future research efforts incorporating smart speakers, to mitigate potential risks of privacy violation.

Asymptotic behavior of projections of supercritical multi-type continuous-state and continuous-time branching processes with immigration

Under a fourth-order moment condition on the branching and a second-order moment condition on the immigration mechanisms, we show that an appropriately scaled projection of a supercritical and irreducible continuous-state and continuous-time branching process with immigration on certain left non-Perron eigenvectors of the branching mean matrix is asymptotically mixed normal. With an appropriate random scaling, under some conditional probability measure, we prove asymptotic normality as well. In the case of a non-trivial process, under a first-order moment condition on the immigration mechanism, we also prove the convergence of the relative frequencies of distinct types of individuals on a suitable event; for instance, if the immigration mechanism does not vanish, then this convergence holds almost surely.