Data trusts have been proposed as a mechanism through which data can be more readily exploited for a variety of aims, including economic development and social-benefit goals such as medical research or policy-making. Data trusts, and similar data governance mechanisms such as data co-ops, aim to facilitate the use and re-use of datasets across organizational boundaries and, in the process, to protect the interests of stakeholders such as data subjects. However, the current discourse on data trusts does not acknowledge another common stakeholder in the data value chain—the crowd workers who are employed to collect, validate, curate, and transform data. In this paper, we report on a preliminary qualitative investigation into how crowd data workers themselves feel datasets should be used and governed. We find that while overall remuneration is important to those workers, they also value public-benefit data use but have reservations about delayed remuneration and the trustworthiness of both administrative processes and the crowd itself. We discuss the implications of our findings for how data trusts could be designed, and how data trusts could be used to give crowd workers a more enduring stake in the product of their work.

We explore the problems that confront any attempt to explain or explicate exactly what a primitive logical rule of inference is, or consists in. We arrive at a proposed solution that places a surprisingly heavy load on the prospect of being able to understand and deal with specifications of rules that are essentially self-referring. That is, any rule $\rho $ is to be understood via a specification that involves, embedded within it, reference to rule $\rho $ itself. Just how we arrive at this position is explained by reference to familiar rules as well as less familiar ones with unusual features. An inquiry of this kind is surprisingly absent from the foundations of inferentialism—the view that meanings of expressions (especially logical ones) are to be characterized by the rules of inference that govern them.

I show that the logic $\textsf {TJK}^{d+}$, one of the strongest logics currently known to support the naive theory of truth, is obtained from the Kripke semantics for constant domain intuitionistic logic by (i) dropping the requirement that the accessibility relation is reflexive and (ii) only allowing reflexive worlds to serve as counterexamples to logical consequence. In addition, I provide a simplified natural deduction system for $\textsf {TJK}^{d+}$, in which a restricted form of conditional proof is used to establish conditionals.

Cloud storage faces many problems in the storage process which badly affect the system's efficiency. One of the most problems is insufficient buffer space in cloud storage. This means that the packets of data wait to have storage service which may lead to weakness in performance evaluation of the system. The storage process is considered a stochastic process in which we can determine the probability distribution of the buffer occupancy and the buffer content and predict the performance behavior of the system at any time. This paper modulates a cloud storage facility as a fluid queue controlled by Markovian queue. This queue has infinite buffer capacity which determined by the M/M/1/N queue with constant arrival and service rates. We obtain the analytical solution of the distribution of the buffer occupancy. Moreover, several performance measures and numerical results are given which illustrate the effectiveness of the proposed model.

Dynamic movement primitives (DMP) are motion building blocks suitable for real-world tasks. We suggest a methodology for learning the manifold of task and DMP parameters, which facilitates runtime adaptation to changes in task requirements while ensuring predictable and robust performance. For efficient learning, the parameter space is analyzed using principal component analysis and locally linear embedding. Two manifold learning methods: kernel estimation and deep neural networks, are investigated for a ball throwing task in simulation and in a physical environment. Low runtime estimation errors are obtained for both learning methods, with an advantage to kernel estimation when data sets are small.

Magnant and Martin conjectured that the vertex set of any d-regular graph G on n vertices can be partitioned into $n / (d+1)$ paths (there exists a simple construction showing that this bound would be best possible). We prove this conjecture when $d = \Omega(n)$, improving a result of Han, who showed that in this range almost all vertices of G can be covered by $n / (d+1) + 1$ vertex-disjoint paths. In fact our proof gives a partition of V(G) into cycles. We also show that, if $d = \Omega(n)$ and G is bipartite, then V(G) can be partitioned into n/(2d) paths (this bound is tight for bipartite graphs).

Modules are requisite for the realization of modular reconfigurable manipulators. The design of modules in literature mainly revolves around geometric aspects and features such as lengths, connectivity and adaptivity. Optimizing and designing the modules based on dynamic performance is considered as a challenge here. The present paper introduces an Architecture-Prominent-Sectioning (APS) strategy for the planning of architecture of modules such that a reconfigurable manipulator possesses minimal joint torques during its operations. Proposed here is the transferring of complete structure into an equivalent system, perform optimization and map the resulting arrangement into possible architecture. The strategy has been applied on a set of modular configurations considering three-primitive-paths. The possibility of getting advanced/complex shapes is also discussed to incorporate the idea of a modular library.

The present work achieves a mathematical, in particular syntax-independent, formulation of dynamics and intensionality of computation in terms of games and strategies. Specifically, we give game semantics of a higher-order programming language that distinguishes programmes with the same value yet different algorithms (or intensionality) and the hiding operation on strategies that precisely corresponds to the (small-step) operational semantics (or dynamics) of the language. Categorically, our games and strategies give rise to a cartesian closed bicategory, and our game semantics forms an instance of a bicategorical generalisation of the standard interpretation of functional programming languages in cartesian closed categories. This work is intended to be a step towards a mathematical foundation of intensional and dynamic aspects of logic and computation; it should be applicable to a wide range of logics and computations.

Blood-side resistance to oxygen transport in extracorporeal membrane blood oxygenators (MBO) depends on fluid mechanics governing the laminar flow in very narrow channels, particularly the hemodynamics controlling the cell free layer (CFL) built-up at solid/blood interfaces. The CFL thickness constitutes a barrier to oxygen transport from the membrane towards the erythrocytes. Interposing hemicylindrical CFL disruptors in animal blood flows inside rectangular microchannels, surrogate systems of MBO mimicking their hemodynamics, proved to be effective in reducing (ca. 20%) such thickness (desirable for MBO to increase oxygen transport rates to the erythrocytes). The blockage ratio (non-dimensional measure of the disruptor penetration into the flow) increase is also effective in reducing CFL thickness (ca. 10–20%), but at the cost of risking clot formation (undesirable for MBO) for disruptors with penetration lengths larger than their radius, due to large residence times of erythrocytes inside a low-velocity CFL formed at the disruptor/wall edge.