The loss of trust in the European Union during the great recession since 2007: The role of heuristics from the national political system

How can we explain the decline in support for the European Union (EU) and the idea of European integration after the onset of the great recession in the fall of 2007? Did the economic crisis and the austerity policies that the EU imposed—in tandem with the IMF—on several member countries help cause this drop? While there is some evidence for this direct effect of EU policies, we find that the most significant determinant of trust and support for the EU remains the level of trust in national governments. Based on cue theory and using concepts of diffuse and specific support, we find that support for the EU is derived from evaluations of national politics and policy, which Europeans know far better than the remote political system of the EU. This effect, however, is somewhat muted for those sophisticated Europeans that are more knowledgeable about the EU and are able to form opinions about it independently of the national contexts in which they live. We also find that the recent economic crisis has led to a discernible increase in the number of those who are disillusioned with politics both at the national and the supranational level. We analyze 133 national surveys from 27 EU countries by estimating a series of cross-classified multilevel logistic regression models.

TA-RE: An Exchange Language for Mining Software Repositories

Software repositories have been getting a lot of attention from researchers in recent years. In order to analyze software repositories, it is necessary to first extract raw data from the version control and problem tracking systems. This poses two challenges: (1) extraction requires a non-trivial effort, and (2) the results depend on the heuristics used during extraction. These challenges burden researchers that are new to the community and make it difficult to benchmark software repository mining since it is almost impossible to reproduce experiments done by another team. In this paper we present the TA-RE corpus. TA-RE collects extracted data from software repositories in order to build a collection of projects that will simplify extraction process. Additionally the collection can be used for benchmarking. As the first step we propose an exchange language capable of making sharing and reusing data as simple as possible.

Practical Object-Oriented Back-in-Time Debugging

Back-in-time debuggers are extremely useful tools for identifying the causes of bugs, as they allow us to inspect the past states of objects no longer present in the current execution stack. Unfortunately the "omniscient" approaches that try to remember all previous states are impractical because they either consume too much space or they are far too slow. Several approaches rely on heuristics to limit these penalties, but they ultimately end up throwing out too much relevant information. In this paper we propose a practical approach to back-in-time debugging that attempts to keep track of only the relevant past data. In contrast to other approaches, we keep object history information together with the regular objects in the application memory. Although seemingly counter-intuitive, this approach has the effect that past data that is not reachable from current application objects (and hence, no longer relevant) is automatically garbage collected. In this paper we describe the technical details of our approach, and we present benchmarks that demonstrate that memory consumption stays within practical bounds. Furthermore since our approach works at the virtual machine level, the performance penalty is significantly better than with other approaches.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

Parsing for agile modeling

In order to analyze software systems, it is necessary to model them. Static software models are commonly imported by parsing source code and related data. Unfortunately, building custom parsers for most programming languages is a non-trivial endeavour. This poses a major bottleneck for analyzing software systems programmed in languages for which importers do not already exist. Luckily, initial software models do not require detailed parsers, so it is possible to start analysis with a coarse-grained importer, which is then gradually refined. In this paper we propose an approach to "agile modeling" that exploits island grammars to extract initial coarse-grained models, parser combinators to enable gradual refinement of model importers, and various heuristics to recognize language structure, keywords and other language artifacts.

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Diagnosing synaesthesia with online colour pickers: Maximising sensitivity and specificity

The most commonly used method for formally assessing grapheme-colour synaesthesia (i.e., experiencing colours in response to letter and/or number stimuli) involves selecting colours from a large colour palette on several occasions and measuring consistency of the colours selected. However, the ability to diagnose synaesthesia using this method depends on several factors that have not been directly contrasted. These include the type of colour space used (e.g., RGB, HSV, CIELUV, CIELAB) and different measures of consistency (e.g., city block and Euclidean distance in colour space). This study aims to find the most reliable way of diagnosing grapheme-colour synaesthesia based on maximising sensitivity (i.e., ability of a test to identify true synaesthetes) and specificity (i.e., ability of a test to identify true non-synaesthetes). We show, applying ROC (Receiver Operating Characteristics) to binary classification of a large sample of self-declared synaesthetes and non-synaesthetes, that the consistency criterion (i.e., cut-off value) for diagnosing synaesthesia is considerably higher than the current standard in the field. We also show that methods based on perceptual CIELUV and CIELAB colour models (rather than RGB and HSV colour representations) and Euclidean distances offer an even greater sensitivity and specificity than most currently used measures. Together, these findings offer improved heuristics for the behavioural assessment of grapheme-colour synaesthesia.

The Process-Outcome Mindfulness Effects in Trainees (PrOMET) study: protocol of a pragmatic randomized controlled trial

Background Mindfulness has its origins in an Eastern Buddhist tradition that is over 2500 years old and can be defined as a specific form of attention that is non-judgmental, purposeful, and focused on the present moment. It has been well established in cognitive-behavior therapy in the last decades, while it has been investigated in manualized group settings such as mindfulness-based stress reduction and mindfulness-based cognitive therapy. However, there is scarce research evidence on the effects of mindfulness as a treatment element in individual therapy. Consequently, the demand to investigate mindfulness under effectiveness conditions in trainee therapists has been highlighted. Methods/Design To fill in this research gap, we designed the PrOMET Study. In our study, we will investigate the effects of brief, audiotape-presented, session-introducing interventions with mindfulness elements conducted by trainee therapists and their patients at the beginning of individual therapy sessions in a prospective, randomized, controlled design under naturalistic conditions with a total of 30 trainee therapists and 150 patients with depression and anxiety disorders in a large outpatient training center. We hypothesize that the primary outcomes of the session-introducing intervention with mindfulness elements will be positive effects on therapeutic alliance (Working Alliance Inventory) and general clinical symptomatology (Brief Symptom Checklist) in contrast to the session-introducing progressive muscle relaxation and treatment-as-usual control conditions. Treatment duration is 25 therapy sessions. Therapeutic alliance will be assessed on a session-to-session basis. Clinical symptomatology will be assessed at baseline, session 5, 15 and 25. We will conduct multilevel modeling to address the nested data structure. The secondary outcome measures include depression, anxiety, interpersonal functioning, mindful awareness, and mindfulness during the sessions. Discussion The study results could provide important practical implications because they could inform ideas on how to improve the clinical training of psychotherapists that could be implemented very easily; this is because there is no need for complex infrastructures or additional time concerning these brief session-introducing interventions with mindfulness elements that are directly implemented in the treatment sessions.

Internet-based interpretation bias modification for social anxiety: A pilot study

BACKGROUND AND OBJECTIVES: The biased interpretation of ambiguous social situations is considered a maintaining factor of Social Anxiety Disorder (SAD). Studies on the modification of interpretation bias have shown promising results in laboratory settings. The present study aims at pilot-testing an Internet-based training that targets interpretation and judgmental bias. METHOD: Thirty-nine individuals meeting diagnostic criteria for SAD participated in an 8-week, unguided program. Participants were presented with ambiguous social situations, were asked to choose between neutral, positive, and negative interpretations, and were required to evaluate costs of potential negative outcomes. Participants received elaborate automated feedback on their interpretations and judgments. RESULTS: There was a pre-to-post-reduction of the targeted cognitive processing biases (d = 0.57-0.77) and of social anxiety symptoms (d = 0.87). Furthermore, results showed changes in depression and general psychopathology (d = 0.47-0.75). Decreases in cognitive biases and symptom changes did not correlate. The results held stable accounting for drop-outs (26%) and over a 6-week follow-up period. Forty-five percent of the completer sample showed clinical significant change and almost half of the participants (48%) no longer met diagnostic criteria for SAD. LIMITATIONS: As the study lacks a control group, results lend only preliminary support to the efficacy of the intervention. Furthermore, the mechanism of change remained unclear. CONCLUSION: First results promise a beneficial effect of the program for SAD patients. The treatment proved to be feasible and acceptable. Future research should evaluate the intervention in a randomized-controlled setting.