We ask whether interaction is necessary for order-optimal 1-bit mean estimation over nonparametric finite-moment classes. Adaptive threshold-query protocols achieve the order-optimal 1-bit minimax rate, and the same rate is attainable with general 1-bit queries using only one adaptive transition (i.e., two stages of querying). In the non-adaptive setting, threshold and interval queries are known to be highly suboptimal, but the case of arbitrary non-adaptive quantizers remains unresolved. Can such quantizers match the adaptive rate, yielding an optimal one-shot protocol? Or is the known two-stage estimator stage-optimal, with a single adaptive transition being necessary and sufficient?

In this paper, we study a variant of best-arm identification involving elements of risk sensitivity and communication constraints. Specifically, the goal of the learner is to identify the arm with the highest quantile reward, while the communication from an agent (who observes rewards) and the learner (who chooses actions) is restricted to only one bit of feedback per arm pull. We propose an algorithm that utilizes noisy binary search as a subroutine, allowing the learner to estimate quantile rewards through 1-bit feedback. We derive an instance-dependent upper bound on the sample complexity of our algorithm and provide an algorithm-independent lower bound for specific instances, with the two matching to within logarithmic factors under mild conditions, or even to within constant factors in certain low error probability scaling regimes. The lower bound is applicable even in the absence of communication constraints, and thus we conclude that restricting to 1-bit feedback has a minimal impact on the scaling of the sample complexity.

In this paper, we consider a bandit problem in which there are a number of groups each consisting of infinitely many arms. Whenever a new arm is requested from a given group, its mean reward is drawn from an unknown reservoir distribution (different for each group), and the uncertainty in the arm’s mean reward can only be reduced via subsequent pulls of the arm. The goal is to identify the infinite-arm group whose reservoir distribution has the highest $(1-\alpha)$-quantile (e.g., median if $\alpha = \frac{1}{2}$), using as few total arm pulls as possible. We introduce a two-step algorithm that first requests a fixed number of arms from each group and then runs a finite-arm grouped max-quantile bandit algorithm. We characterize both the instance-dependent and worst-case regret, and provide a matching lower bound for the latter, while discussing various strengths, weaknesses, algorithmic improvements, and potential lower bounds associated with our instance-dependent upper bounds.

An efficient compressed sensing scheme requires a small number of measurements, a fast recovery algorithm, a small approximation error, and little or no randomness. In 2014, Iwen presented two compressed sensing schemes with near-optimal runtime, based on binary matrices. We combine ideas from these two schemes with a classical construction, used by Porat and Rothschild for near-optimal group testing, to produce a new compressed sensing scheme requiring significantly less randomness without compromising runtime. We give two variants of this compressed sensing scheme: the first is measurement-optimal, and the second is deterministic.

Global software development (GSD) holds various challenges and problems for team members. When confronted with a contextual change in their working environment, individuals have to adapt to the new situation. This includes the adaptation of working styles, behaviors, and methods. Additionally, new challenges, especially those based on the virtual work and cultural background of team members, have to be addressed. By conducting explorative expert interviews, we identified challenges and potential solutions for individuals when encountering contextual change with a focus on competences. We identified that the lack of competences was seen as a major influence factor for a variety of common challenges to GSD. The identification of underlying factors of challenges could allow for focused development of interventions to overcome these challenges. Furthermore, we identified factors influencing the adaptation of competences to the given context and provided insight into the process of competences adaptation. This is the basis for the future development of a set of internationalized GSD competences. Copyright © 2014 John Wiley & Sons, Ltd.

Mobile agent technologies have been widely used in distributed computing to take care of the task execution for the user. However, pervasive computing presents new challenges to existing mobile agent systems, especially the need for the context-aware self-configuring collaboration with the services provided by the physical objects. In order to address the problem, this paper presents a self-configuring personal agent platform to enable a mobile agent to adapt to the on-demand collaboration with the services. The platform consists of a Ubiquitous Intelligent Object (UIO) model the pervasive computing environment modeling, a code repository to provide executable codes which can be downloaded and instantiated as a mobile agent's capability at runtime, a service registry server for UIOs to publish and subscribe services, and personal agents, one for each individual user. A prototype of platform has been implemented as proof-of-concept and a preliminary performance study has also been carried out on it using a case study.