e-Governance: Supporting pragmatic direct deliberative action through online communities of interest

Authors often report on the limited success of e-Government initiatives in developing nations. Top down, national strategies are developed to target improved government services, but maintain hierarchical, citizen-state conceptions of governance through representative democracy. An alternative conception, direct deliberative democracy, frames the potential role of the internet in governance differently. Web based platforms might support locally animated deliberations, which target pragmatic outcomes, while the resulting social networks afford collective learning through connections across traditional boundaries. This paper presents an investigation of direct deliberative governance as it occurs in online 'communities of interest', and is based on research with such a community in southern Africa. We investigate contributions to the online governance process and develop an action typology distinguishing between degrees of 'agency freedom'. Network analytic techniques are then used to understand how acts of varying degree are expressed in terms of the structure of a social network. The aim, more broadly, is to understand how the environment shapes acts of direct deliberative governance, and, in turn, how the acts shape the evolution and effectiveness of the community. The preliminary results suggest design considerations for online governance communities, and highlight their role to not only provide deliberative space, but to mediate social network connections

C⋅\cdotASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters. Our physically simulated character can learn a diverse repertoire of skills while providing controllability in the form of direct manipulation of the skills to be performed. C$\cdot$ASE divides the heterogeneous skill motions into distinct subsets containing homogeneous samples for training a low-level conditional model to learn conditional behavior distribution. The skill-conditioned imitation learning naturally offers explicit control over the character's skills after training. The training course incorporates the focal skill sampling, skeletal residual forces, and element-wise feature masking to balance diverse skills of varying complexities, mitigate dynamics mismatch to master agile motions and capture more general behavior characteristics, respectively. Once trained, the conditional model can produce highly diverse and realistic skills, outperforming state-of-the-art models, and can be repurposed in various downstream tasks. In particular, the explicit skill control handle allows a high-level policy or user to direct the character with desired skill specifications, which we demonstrate is advantageous for interactive character animation.Comment: SIGGRAPH Asia 202

Development of an Intelligent Monitoring and Control System for a Heterogeneous Numerical Propulsion System Simulation

The NASA Numerical Propulsion System Simulation (NPSS) project is exploring the use of computer simulation to facilitate the design of new jet engines. Several key issues raised in this research are being examined in an NPSS-related research project: zooming, monitoring and control, and support for heterogeneity. The design of a simulation executive that addresses each of these issues is described. In this work, the strategy of zooming, which allows codes that model at different levels of fidelity to be integrated within a single simulation, is applied to the fan component of a turbofan propulsion system. A prototype monitoring and control system has been designed for this simulation to support experimentation with expert system techniques for active control of the simulation. An interconnection system provides a transparent means of connecting the heterogeneous systems that comprise the prototype

Explaining autonomous driving with visual attention and end-to-end trainable region proposals

Autonomous driving is advancing at a fast pace, with driving algorithms becoming more and more accurate and reliable. Despite this, it is of utter importance to develop models that can ofer a certain degree of explainability in order to be trusted, understood and accepted by researchers and, especially, society. In this work we present a conditional imitation learning agent based on a visual attention mechanism in order to provide visually explainable decisions by design. We propose different variations of the method, relying on end-to-end trainable regions proposal functions, generating regions of interest to be weighed by an attention module. We show that visual attention can improve driving capabilities and provide at the same time explainable decisions

Comparing User Space and In-Kernel Packet Processing for Edge Data Centers

Telecommunication operators are massively moving their network functions in small data centers at the edge of the network, which are becoming increasingly common. However, the high performance provided by commonly used technologies for data plane processing such as DPDK, based on kernel-bypass primitives, comes at the cost of rigid resource partitioning. This is unsuitable for edge data centers, in which efficiency demands both general-purpose applications and data-plane telco workloads to be executed on the same (shared) physical machines. In this respect, eBPF/XDP looks a more appealing solution, thanks to its capability to process packets in the kernel, achieving a higher level of integration with non-data plane applications albeit with lower performance than DPDK. In this paper we leverage the recent introduction of AF_XDP, an XDP-based technology that allows to efficiently steer packets in user space, to provide a thorough comparison of user space vs in-kernel packet processing in typical scenarios of a data center at the edge of the network. Our results provide useful insights on how to select and combine these technologies in order to improve overall throughput and optimize resource usage