Graphicons and Tactics in Satirical Trolling on Tumblr.com

Internet trolling is inherently multimodal, relying on both textual and graphical means of communication (or “graphicons”). We examined how satire and ideological trolls who use graphicons on the microblogging site Tumblr.com, use knowledge of local culture as part of their trolling tactics. Based on a qualitative thematic analysis of 172 trolling posts (that include 284 graphicons), we identified 7 Tumblr satire troll tactics: the lying tactic, the derailment tactic, the parodic exaggeration tactic, the misappropriation of jargon tactic, the straight man (or “comical seriousness”) tactic, the troll reveal tactic, and the politeness tactic. We also found that ideologically extremizing language was the most commonly used outrage tactic and that trolls used graphicons frequently as flame baiting prompts and for tone modification

Effect of wire diameter on ultrasonic enhancement of subcooled pool boiling

Paper presented at the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16-18 July, 2012.New methods for cooling of microelectronic elements have been recently developed, including application of ultrasonic fields. Ultrasonic fields enhance the heat transfer in two-phase cooling. The present work deals with ultrasonic enhancement of heat transfer from wires in sub-cooled pool boiling. The experiments have been carried out using three wires of different diameters: 0.05, 0.09, 0.2mm, submerged into a bath with water. The applied ultrasonic field was of frequency of 40 kHz and intensity of 0.5 W/cm2. The wire wall temperature was measured as a function of wire surface heat flux. When the ultrasonic field was applied, the wall temperature reduced in the range of measured heat fluxes. The temperature difference increased with the heat flux. It also increased with the wire diameter. At the smallest diameter only a small decrease of the wall temperature, about 10-15 degrees, was observed, while at larger diameters the decrease of the wall temperature was about 30 - 35 degrees.dc201

Using Rheo-Small-Angle Neutron Scattering to Understand How Functionalised Dipeptides Form Gels

We explore the use of rheo-small-angle neutron scattering as a method to collect structural information from neutron scattering simultaneously with rheology to understand how low-molecular-weight hydrogels form and behave under shear. We examine three different gelling hydrogel systems to assess what structures are formed and how these influence the rheology. Furthermore, we probe what is happening to the network during syneresis and why the gels do not recover after an applied strain. All this information is vital when considering gels for applications such as 3D-printing and injection

How managers can build trust in strategic alliances: a meta-analysis on the central trust-building mechanisms

Trust is an important driver of superior alliance performance. Alliance managers are influential in this regard because trust requires active involvement, commitment and the dedicated support of the key actors involved in the strategic alliance. Despite the importance of trust for explaining alliance performance, little effort has been made to systematically investigate the mechanisms that managers can use to purposefully create trust in strategic alliances. We use Parkhe’s (1998b) theoretical framework to derive nine hypotheses that distinguish between process-based, characteristic-based and institutional-based trust-building mechanisms. Our meta-analysis of 64 empirical studies shows that trust is strongly related to alliance performance. Process-based mechanisms are more important for building trust than characteristic- and institutional-based mechanisms. The effects of prior ties and asset specificity are not as strong as expected and the impact of safeguards on trust is not well understood. Overall, theoretical trust research has outpaced empirical research by far and promising opportunities for future empirical research exist

Characterization of MKIDs for CMB observation at 220 GHz with the South Pole Telescope

We present an updated design of the 220 GHz microwave kinetic inductance detector (MKID) pixel for SPT-3G+, the next-generation camera for the South Pole Telescope. We show results of the dark testing of a 63-pixel array with mean inductor quality factor $Q_i = 4.8 \times 10^5$, aluminum inductor transition temperature $T_c = 1.19$ K, and kinetic inductance fraction $\alpha_k = 0.32$. We optically characterize both the microstrip-coupled and CPW-coupled resonators, and find both have a spectral response close to prediction with an optical efficiency of $\eta \sim 70\%$. However, we find slightly lower optical response on the lower edge of the band than predicted, with neighboring dark detectors showing more response in this region, though at level consistent with less than 5\% frequency shift relative to the optical detectors. The detectors show polarized response consistent with expectations, with a cross-polar response of $\sim 10\%$ for both detector orientations.Comment: 6 pages, 5 figures, ASC 2022 proceeding

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Dealing with Missing Data and Uncertainty in the Context of Data Mining

Missing data is an issue in many real-world datasets yet robust methods for dealing with missing data appropriately still need development. In this paper we conduct an investigation of how some methods for handling missing data perform when the uncertainty increases. Using benchmark datasets from the UCI Machine Learning repository we generate datasets for our experimentation with increasing amounts of data Missing Completely At Random (MCAR) both at the attribute level and at the record level. We then apply four classification algorithms: C4.5, Random Forest, Naïve Bayes and Support Vector Machines (SVMs). We measure the performance of each classifiers on the basis of complete case analysis, simple imputation and then we study the performance of the algorithms that can handle missing data. We find that complete case analysis has a detrimental effect because it renders many datasets infeasible when missing data increases, particularly for high dimensional data. We find that increasing missing data does have a negative effect on the performance of all the algorithms tested but the different algorithms tested either using preprocessing in the form of simple imputation or handling the missing data do not show a significant difference in performance

Author Correction: A consensus-based transparency checklist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper