Bogas - Boops boops (Linnaeus, 1758) - from the Biscay to the North Sea in 2500 BC and 18980 AD

Se presentan los hallazgos del pez espárido boga (Boops boops) en los concheros neolíticos de Suecia. La distribucion de los tamaños de los peces hallados indica que existía en el Mar del Norte en este periodo una población reproductora. Las temperaturas del mar eran más altas que hoy. La pesca de la boga indica el uso de redes u otras artes de pesca. Las excavaciones recientes en yacimientos tanto antiguos como nuevos indican que los concheros son intencionales, es decir, estructuras rituales más que amontonamientos comunes. Se subraya la necesidad de nuevas colecciones comparativas y la integración de arqueozoólogos en las excavacione

Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models

Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler.Comment: Accepted for publication in Journal of Computational and Graphical Statistic

The More the Merrier: Leveraging on the Bug Inflow to Guide Software Maintenance

Issue management, a central part of software maintenance, requires much effort for complex software systems. The continuous inflow of issue reports makes it hard for developers to stay on top of the situation, and the threatening information overload makes activities such as duplicate management, Issue Assignment (IA), and Change Impact Analysis (CIA) tedious and error-prone. Still, most practitioners work with tools that act as little more than issue containers. Machine Learning encompasses approaches that identify patterns or make predictions based on empirical data. While humans have limited ability to work with big data, ML instead tends to improve the more training data that is available. Consequently, we argue that the challenge of information overload in issue management appears to be particularly suitable for ML-based tool support. While others have initially explored the area, we develop two ML-based tools, and evaluate them in proprietary software engineering contexts. We replicated [1] for five projects in two companies, and our automated IA obtains an accuracy matching the current manual processes. Thus, as our solution delivers instantaneous IA, an organization can potentially save considerable analysis effort. Moreover, for the most comprehensive of the five projects, we implemented automated CIA in the tool ImpRec [3]. We evaluated the tool in a longitudinal in situ study, i.e., deployment in two development teams in industry. Based on log analysis and complementary interviews using the QUPER model [2] for utility assessment, we conclude that ImpRec offered helpful support in the CIA task

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models

To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language - an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days

Agency by Analogy: A Comment on Odious Debt

Part of a special issue on odious debts and state corruption. A study was conducted to examine the phenomenon of odious debt from the perspective of common-law agency. Data were obtained from a review of the relevant literature and from the 19th century novel, The Prisoner of Zenda, by Anthony Hope. Findings revealed that the application of the agency doctrine to the problem of sovereign debt poses a number of problems, including identifying the principal within the sovereign-debt context, and the consensual relationship assumed by common-law agency between the principal and the agent. Findings suggested, therefore, that agency doctrine\u27s direct applicability to the odious debt problem is limited. Findings indicated, however, that agency can be useful as a source of analogy. Findings are discussed in detail

Effect of Trans-Nasal Evaporative Intra-arrest Cooling on Functional Neurologic Outcome in Out-of-Hospital Cardiac Arrest : The PRINCESS Randomized Clinical Trial

© 2019 American Medical Association. All rights reserved.Importance: Therapeutic hypothermia may increase survival with good neurologic outcome after cardiac arrest. Trans-nasal evaporative cooling is a method used to induce cooling, primarily of the brain, during cardiopulmonary resuscitation (ie, intra-arrest). Objective: To determine whether prehospital trans-nasal evaporative intra-arrest cooling improves survival with good neurologic outcome compared with cooling initiated after hospital arrival. Design, Setting, and Participants: The PRINCESS trial was an investigator-initiated, randomized, clinical, international multicenter study with blinded assessment of the outcome, performed by emergency medical services in 7 European countries from July 2010 to January 2018, with final follow-up on April 29, 2018. In total, 677 patients with bystander-witnessed out-of-hospital cardiac arrest were enrolled. Interventions: Patients were randomly assigned to receive trans-nasal evaporative intra-arrest cooling (n = 343) or standard care (n = 334). Patients admitted to the hospital in both groups received systemic therapeutic hypothermia at 32°C to 34°C for 24 hours. Main Outcomes and Measures: The primary outcome was survival with good neurologic outcome, defined as Cerebral Performance Category (CPC) 1-2, at 90 days. Secondary outcomes were survival at 90 days and time to reach core body temperature less than 34°C. Results: Among the 677 randomized patients (median age, 65 years; 172 [25%] women), 671 completed the trial. Median time to core temperature less than 34°C was 105 minutes in the intervention group vs 182 minutes in the control group (P < .001). The number of patients with CPC 1-2 at 90 days was 56 of 337 (16.6%) in the intervention cooling group vs 45 of 334 (13.5%) in the control group (difference, 3.1% [95% CI, -2.3% to 8.5%]; relative risk [RR], 1.23 [95% CI, 0.86-1.72]; P = .25). In the intervention group, 60 of 337 patients (17.8%) were alive at 90 days vs 52 of 334 (15.6%) in the control group (difference, 2.2% [95% CI, -3.4% to 7.9%]; RR, 1.14 [95% CI, 0.81-1.57]; P = .44). Minor nosebleed was the most common device-related adverse event, reported in 45 of 337 patients (13%) in the intervention group. The adverse event rate within 7 days was similar between groups. Conclusions and Relevance: Among patients with out-of-hospital cardiac arrest, trans-nasal evaporative intra-arrest cooling compared with usual care did not result in a statistically significant improvement in survival with good neurologic outcome at 90 days. Trial Registration: ClinicalTrials.gov Identifier: NCT01400373.Peer reviewedFinal Accepted Versio

Adopting Automated Bug Assignment in Practice: A Longitudinal Case Study at Ericsson

The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR's first bug assignment without human intervention happened in April 2019. Our study evaluates the adoption of TRR within its industrial context at Ericsson. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. We conduct an industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.Comment: Under revie

Geofysiker, drönare och geologer ger tillsammans en bättre bild av berget

Teknisk geologi vid Lunds tekniska högskola har i ett nyligen slutfört projekt, finansierat av Stiftelsen Bergteknisk Forskning (BeFo), undersökt om geofysiska mätningar med DCIP (kombinerade resistivitets- och IP-mätningar) kan ge bättre information om bergmassan i samband med planeringen av tunnlar och andra bergarbeten samt berguttag i bergtäkter. Resultaten bekräftar att DCIP i den undersökta miljön kan användas för att indikera lervittrade zoner, svaghetsstrukturer och uppkrossade zoner. Detta visar på att det går att underlätta och förbättra prognosarbetet, genom att ännu en möjlighet ges att bedöma bergmassans kvalitet innan byggfasen inleds