vrAIn: a deep learning approach tailoring computing and radio resources in virtualized RANs

Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map (encoded) contexts into resource control decisions. We have implemented vrAIn using an open-source LTE stack over different platforms. Our results show that vrAIn successfully derives appropriate compute and radio control actions irrespective of the platform and context: (i) it provides savings in computational capacity of up to 30% over CPU-unaware methods; (ii) it improves the probability of meeting QoS targets by 25% over static allocation policies using similar CPU resources in average; (iii) upon CPU capacity shortage, it improves throughput performance by 25% over state-of-the-art schemes; and (iv) it performs close to optimal policies resulting from an offline oracle. To the best of our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs, and the first approach to a model-free solution that does not need to assume any particular vRAN platform or system conditions.The work of University Carlos III of Madrid was supported by H2020 5GMoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GTRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701.Publicad

Minimal dataset for post-registration surveillance of new drugs in hemophilia: communication from the SSC of the ISTH

Clinical epidemiolog

Vector-based navigation using grid-like representations in artificial agents

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex. Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space and is critical for integrating self-motion (path integration) and planning direct trajectories to goals (vector-based navigation). Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types12. We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments

Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.Comment: AIAA SciTech 201

Musicians as Researchers - Insight or Insanity?

In the current university context, many highly-proficient music performers enrol in higher education degrees by research. While at first glance those enrolled may seem to be moving from an area of expertise to an area of inexperience, in many cases the individual may in fact have already developed a range of research skills in the course of becoming highly proficient in their chosen field. Many expert musicians seek to further develop their craft through embarking on research degrees and/or seek inspiration through what they aim to discover. Research is a highly valued skill among many musicians pursuing fine music making. In this paper, we will investigate the motivations of musicians for enrolling in a higher degree by research, including the reasons why they choose research as a way of expanding their skills as performers, and the expected outcomes of their research studies

Knowledge Hub on the Integrated Assessment of Chemical Contaminants and their Effects on the Marine Environment

In a time of environmental awareness, spurred on by the possibility that our world is threatened by climate change, it is important to remember that there are other anthropogenic pressures, which are also essential for addressing the protection of the marine and coastal environment. Pollution is a global, complex issue that contributes to biodiversity loss and poor environmental health and comes from the production and release of many of the synthetic chemicals that we use in our daily lives. Chemical contaminants are often underrepresented as a major contributor of environmental deterioration. The Joint Programming Initiative Healthy and Productive Seas and Oceans (JPI Oceans) established in 2018 the JPI Oceans Knowledge Hub on the integrated assessment of chemical contaminants and their effects on the marine environment. The purpose of the Knowledge Hub was to provide recommendations on how to improve the methodological basis for marine chemical status assessment. The work has resulted in the following policy paper which focuses on improving the efficiency and implementation of integrated assessment methodology of effects of chemicals of emerging concern. Substantial additional knowledge of biological effects is needed to achieve Good Environmental Status (GES) of our oceans and coastal areas. The Knowledge Hub is represented by highly skilled scientists and policy makers, appointed by the JPI Oceans Management Board, to ensure that the recommendations provided are useful for policy making

Validation of the GALS musculoskeletal screening exam for use in primary care: a pilot study

<p>Abstract</p> <p>Background</p> <p>As the proportion of the Canadian population ≥65 grows, so too does the prevalence of musculoskeletal (MSK) conditions. Approximately 20% of visits to family physicians occur as a result of MSK complaints. The GALS (Gait, Arms, Legs, and Spine) screening examination was developed to assist in the detection of MSK abnormalities. Although MSK exams are primarily performed by rheumatologists or other MSK specialists, expanding their use in primary health care may improve the detection of MSK conditions allowing for earlier treatment. The primary goal of this study was to evaluate the use of the GALS locomotor screen in primary care by comparing the results of assessments of family physicians with those of rheumatologists. The secondary goal was to examine the incidence of MSK disorders and assess the frequency with which new diagnoses not previously documented in patients' charts were identified.</p> <p>Methods</p> <p>Patients ≥65 years old recruited from an academic family health centre were examined by a rheumatologist and a family physician who recorded the appearance of each participant's gait and the appearance and movement of the arms, legs and spine by deeming them normal or abnormal. GALS scores were compared between physicians with the proportion of observed (P_obs), positive (P_pos) and negative (P_neg) agreement being the primary outcomes. Kappa statistics were also calculated. Descriptive statistics were used to describe the number of "new" diagnoses by comparing rheumatologists' findings with each patient's family practice chart.</p> <p>Results</p> <p>A total of 99 patients consented to participate (92 with previously diagnosed MSK conditions). Results showed reasonable agreement between family physicians and rheumatologists; P_obs= 0.698, P_pos= 0.614 and P_neg= 0.752. The coefficient of agreement (estimated Kappa) was 0.3675 for the composite GALS score. For individual components of the GALS exam, the highest agreement between family physicians and rheumatologists was in the assessment of gait and arm movement.</p> <p>Conclusion</p> <p>Previously reported increases in undiagnosed signs and symptoms of musculoskeletal conditions have highlighted the need for a simple yet sensitive screening exam for the identification of musculoskeletal abnormalities. Results of this study suggest that family physicians can efficiently use the GALS examination in the assessment of populations with a high proportion of musculoskeletal issues.</p