Equation of state sensitivities when inferring neutron star and dense matter properties

Understanding the dense matter equation of state at extreme conditions is an important open problem. Astrophysical observations of neutron stars promise to solve this, with NICER poised to make precision measurements of mass and radius for several stars using the waveform modelling technique. What has been less clear, however, is how these mass-radius measurements might translate into equation of state constraints and what are the associated equation of state sensitivities. We use Bayesian inference to explore and contrast the constraints that would result from different choices for the equation of state parametrization; comparing the well-established piecewise polytropic parametrization to one based on physically motivated assumptions for the speed of sound in dense matter. We also compare the constraints resulting from Bayesian inference to those from simple compatibility cuts. We find that the choice of equation of state parametrization and particularly its prior assumptions can have a significant effect on the inferred global mass-radius relation and the equation of state constraints. Our results point to important sensitivities when inferring neutron star and dense matter properties. This applies also to inferences from gravitational wave observations

Artificial Intelligence And Digital Forensics

Artificial intelligence (AI) is a well-established branch of computer science concerned with making machines smart enough to perform computationally large or complex tasks that normally require human intelligence; furthermore, it comprises a combination of technologies that can obtain insights and patterns from a massive amount of data which is a crucial element of forensic analysis. This chapter focuses on AI and its subfields: machine learning and deep learning ”in general ”and also details AI and data mining techniques pertaining to digital forensics. In highlighting the current shortcomings of prevailing approaches, we propose a new approach to offer a clearer insight into potential data, and/or detect variables of interest, as well as assess the future of digital forensics in the concluding section

Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

Effects of self-assessment feedback on self-assessment and task-selection accuracy

Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of performance) as well as accurate task selection (i.e., choosing a subsequent task that fits the current level of performance). Because self-assessment accuracy is often low, task-selection accuracy suffers as well and, consequently, self-regulated learning can lead to suboptimal learning outcomes. Recent studies have shown that a training with video modeling examples enhanced self-assessment accuracy on problem-solving tasks, but the training was not equally effective for every student and, overall, there was room for further improvement in self-assessment accuracy. Therefore, we investigated whether training with video examples followed by feedback focused on selfassessment accuracy would improve subsequent self-assessment and task-selection accuracy in the absence of the feedback. Experiment 1 showed, contrary to our hypothesis, that selfassessment feedback led to less accurate future self-assessments. In Experiment 2, we provided students with feedback focused on self-assessment accuracy plus information on the correct answers, or feedback focused on self-assessment accuracy, plus the correct answers and the opportunity to contrast those with their own answers. Again, however, we found no beneficial effect of feedback on subsequent self-assessment accuracy. In sum, we found no evidence that feedback on self-assessment accuracy improves subsequent accuracy. Therefore, future research should address other ways improving accuracy, for instance by taking into account the cues upon which students base their self-assessments

Resistance breeding of common bean shapes the physiology of the rhizosphere microbiome.

Abstract: The taxonomically diverse rhizosphere microbiome contributes to plant nutrition, growth and health, including protection against soil-borne pathogens. We previously showed that breeding for Fusarium-resistance in common bean changed the rhizosphere microbiome composition and functioning. Here, we assessed the impact of Fusarium-resistance breeding in common bean on microbiome physiology. Combined with metatranscriptome data, community-level physiological profiling by Biolog EcoPlate analyses revealed that the rhizosphere microbiome of the Fusarium-resistant accession was distinctly different from that of the Fusarium-susceptible accession, with higher consumption of amino acids and amines, higher metabolism of xylanase and sialidase, and higher expression of genes associated with nitrogen, phosphorus and iron metabolism. The resistome analysis indicates higher expression of soxR, which is involved in protecting bacteria against oxidative stress induced by a pathogen invasion. These results further support our hypothesis that breeding for resistance has unintentionally shaped the assembly and activity of the rhizobacterial community toward a higher abundance of specific rhizosphere competent bacterial taxa that can provide complementary protection against fungal root infections