Extended Equal Service and Differentiated Service Models for Peer-to-Peer File Sharing

Peer-to-Peer (P2P) systems have proved to be the most effective and popular file sharing applications in recent years. Previous studies mainly focus on the equal service and the differentiated service strategies when peers have no initial data before their download. In an upload-constrained P2P file sharing system, we model both the equal service process and the differentiated service process when peers' initial data distribution satisfies some special conditions, and also show how to minimize the time to get the file to any number of peers. The proposed models can reveal the intrinsic relations among the initial data amount, the size of peer set and the minimum last finish time. By using the models, we can also provide arbitrary degree of differentiated service to a certain number of peers. We believe that our analysis process and achieved theoretical results could provide fundamental insights into studies on bandwidth allocation and data scheduling, and can give helpful reference both for improving system performance and building effective incentive mechanism in P2P file sharing systems

Unpacking culture using Delphi

Following a phenomenological Expert Delphi Study of academics and practitioners, findings suggest that: a by-product of post-industrialism, Globalization, and Web2.0 is the value of investigating culture from an associated, rather than a disassociated state; cultural understanding and its application beyond simply defining and classifying has become the rate-determining step; and that national identity, whilst widely used, is not the most insightful unit. Furthermore, culture cannot be judged on a linear scale – it is dynamic, contextual, and perishable. For these reasons it is argued that when culture is measured, it should be viewed as something which is symbiotic and osmotic. The paper reports findings of field work done in decamping culture and branding with establishing their relationship and interdependence

‘The Pinocchio Effect’ – when managing the brand creation process, across cultures

In global marketing and international management, the fields of Branding and Culture are well discussed as separate disciplines; within both academia and industry. However, there appears to be limited supporting literature, examining brands and culture as a collective discipline. In addition, environmental factors such as ethnicity, nationality and religion are also seen to play a significant role. This in itself adds to the challenges encountered, by those looking to critically apply learning and frameworks, to any information gathered. In the first instance, this paper tries to bring aspects together from Branding and Culture and in doing so, aims to find linkages between the two. The main purpose of this paper is to distil current brand thinking and explore what impact cross-cultural, cross-national, and ethnic interactions have on a brand’s creation. The position of the authors is that without further understanding in this field, a brand will experience what has been termed by them as the ‘Pinocchio Effect’. Pinocchio was a puppet who longed to become a real human being; but sadly encountered difficulties. The conclusion presented is that the critical long-term success of a brand lies in three areas: how it is created; the subsequent associated perceptions; and more specifically in the reality of the relationships that it enjoys. Collectively these processes necessitate an appraisal of connecting strategic management procedures and thinking. Finally, this paper looks into proposing future methods for brand evaluation and strategic management. The aim is to stimulate further thinking in a field; which transcends national, ethnic and cultural boundaries - in the interests of developing new insight, and to provide a platform for marketers to develop more effective communications

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for cooperative sequential decision making under uncertainty and MAs allow temporally extended and asynchronous action execution. To date, most methods assume the underlying Dec-POMDP model is known a priori or a full simulator is available during planning time. Previous methods which aim to address these issues suffer from local optimality and sensitivity to initial conditions. Additionally, few hardware demonstrations involving a large team of heterogeneous robots and with long planning horizons exist. This work addresses these gaps by proposing an iterative sampling based Expectation-Maximization algorithm (iSEM) to learn polices using only trajectory data containing observations, MAs, and rewards. Our experiments show the algorithm is able to achieve better solution quality than the state-of-the-art learning-based methods. We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017

Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from optimal. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called \emph{decentralized stick-breaking policy representation} (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods

Cryo-EM structures of herpes simplex virus type 1 portal vertex and packaged genome.

Herpesviruses are enveloped viruses that are prevalent in the human population and are responsible for diverse pathologies, including cold sores, birth defects and cancers. They are characterized by a highly pressurized pseudo-icosahedral capsid-with triangulation number (T) equal to 16-encapsidating a tightly packed double-stranded DNA (dsDNA) genome1-3. A key process in the herpesvirus life cycle involves the recruitment of an ATP-driven terminase to a unique portal vertex to recognize, package and cleave concatemeric dsDNA, ultimately giving rise to a pressurized, genome-containing virion4,5. Although this process has been studied in dsDNA phages6-9-with which herpesviruses bear some similarities-a lack of high-resolution in situ structures of genome-packaging machinery has prevented the elucidation of how these multi-step reactions, which require close coordination among multiple actors, occur in an integrated environment. To better define the structural basis of genome packaging and organization in herpes simplex virus type 1 (HSV-1), we developed sequential localized classification and symmetry relaxation methods to process cryo-electron microscopy (cryo-EM) images of HSV-1 virions, which enabled us to decouple and reconstruct hetero-symmetric and asymmetric elements within the pseudo-icosahedral capsid. Here we present in situ structures of the unique portal vertex, genomic termini and ordered dsDNA coils in the capsid spooled around a disordered dsDNA core. We identify tentacle-like helices and a globular complex capping the portal vertex that is not observed in phages, indicative of herpesvirus-specific adaptations in the DNA-packaging process. Finally, our atomic models of portal vertex elements reveal how the fivefold-related capsid accommodates symmetry mismatch imparted by the dodecameric portal-a longstanding mystery in icosahedral viruses-and inform possible DNA-sequence recognition and headful-sensing pathways involved in genome packaging. This work showcases how to resolve symmetry-mismatched elements in a large eukaryotic virus and provides insights into the mechanisms of herpesvirus genome packaging