The Rumsfeld Effect: The unknown unknown

A set of studies tested whether people can use awareness of ignorance to provide enhanced test consistency over time if they are allowed to place uncertain items into a “don’t know” category. For factual knowledge this did occur, but for a range of other forms of knowledge relating to conceptual knowledge and personal identity, no such effect was seen. Known unknowns would appear to be largely restricted to factual kinds of knowledge

Farmer Knowledge of Cassava Mosaic Disease and Management Practices in Ogun State

This study assessed CMD knowledge and management practices of farmers in Ogun state Nigeria during a farmers' training exercise. A total of 101 farmers (80 male and 21 females) participated in this study. Only a few farmers (35.22%) however, were aware that whiteflies are vectors of cassava begomoviruses. Farmers generally obtained their planting material from neighbours’farms (42.71%) and previous planting season (41.67%). This study has shown poor knowledge of CMD amongst farmers in Ogun state and underpins the need for interventions towards farmer education in the study region

Resonances of the Unknown

Purpose – The purpose of this paper is to discuss the relevance of second-order cybernetics for a theory of architectural design and related discourse. Design/methodology/approach – First, the relation of architectural design to the concept of “poiesis” is clarified. Subsequently, selected findings of Gotthard Günther are revisited and related to an architectural poetics. The last part of the paper consists of revisiting ideas mentioned previously, however, on the level of a discourse that has incorporated the ideas and offers a poetic way of understanding them

Metrology with Unknown Detectors

The best possible precision is one of the key figures in metrology, but this is established by the exact response of the detection apparatus, which is often unknown. There exist techniques for detector characterisation, that have been introduced in the context of quantum technologies, but apply as well for ordinary classical coherence; these techniques, though, rely on intense data processing. Here we show that one can make use of the simpler approach of data fitting patterns in order to obtain an estimate of the Cram\'er-Rao bound allowed by an unknown detector, and present applications in polarimetry. Further, we show how this formalism provide a useful calculation tool in an estimation problem involving a continuous-variable quantum state, i.e. a quantum harmonic oscillator

Assessing unknown network traffic

Recent measurements have shown that a growing fraction of all Internet traffic is unknown: it is unclear which applications are causing the traffic. Therefore we have developed and applied a novel methodology to find out what applications are running on the network. This methodology is based on the notion of ¿induced traffic¿: traffic cannot (wide-scale) be on unknown ports, thus, \ud the hypothesis is that such traffic on unknown ports should be preceeded by traffic on known ports between the same peers. We have developed and implemented an algorithm to test this hypothesis. After applying the algorithm in two case studies we, unfortunately, have to conclude that although some improvement is made, there is still a significant fraction of traffic unidentifiable

Quantum computation with unknown parameters

We show how it is possible to realize quantum computations on a system in which most of the parameters are practically unknown. We illustrate our results with a novel implementation of a quantum computer by means of bosonic atoms in an optical lattice. In particular we show how a universal set of gates can be carried out even if the number of atoms per site is uncertain.Comment: 3 figure

Efficient tomography with unknown detectors

We compare the two main techniques used for estimating the state of a physical system from unknown measurements: standard detector tomography and data-pattern tomography. Adopting linear inversion as a fair benchmark, we show that the difference between these two protocols can be traced back to the nonexistence of the reverse-order law for pseudoinverses. We capitalize on this fact to identify regimes where the data-pattern approach outperforms the standard one and vice versa. We corroborate these conclusions with numerical simulations of relevant examples of quantum state tomography.Comment: 13 pages, 6 figures. Submitted for publication. Comments most welcome

Bayesian Optimization with Unknown Constraints

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics.Comment: 14 pages, 3 figure