926 research outputs found

    Special Delivery: Programming with Mailbox Types (Extended Version)

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    The asynchronous and unidirectional communication model supported by mailboxes is a key reason for the success of actor languages like Erlang and Elixir for implementing reliable and scalable distributed systems. While many actors may send messages to some actor, only the actor may (selectively) receive from its mailbox. Although actors eliminate many of the issues stemming from shared memory concurrency, they remain vulnerable to communication errors such as protocol violations and deadlocks. Mailbox types are a novel behavioural type system for mailboxes first introduced for a process calculus by de'Liguoro and Padovani in 2018, which capture the contents of a mailbox as a commutative regular expression. Due to aliasing and nested evaluation contexts, moving from a process calculus to a programming language is challenging. This paper presents Pat, the first programming language design incorporating mailbox types, and describes an algorithmic type system. We make essential use of quasi-linear typing to tame some of the complexity introduced by aliasing. Our algorithmic type system is necessarily co-contextual, achieved through a novel use of backwards bidirectional typing, and we prove it sound and complete with respect to our declarative type system. We implement a prototype type checker, and use it to demonstrate the expressiveness of Pat on a factory automation case study and a series of examples from the Savina actor benchmark suite.Comment: Extended version of paper accepted to ICFP'2

    Irritability in youth: A critical integrative review

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    Irritability, defined as proneness to anger that may reach an impairing extent, is common in youth. There has been a recent upsurge in relevant research. We combine systematic and narrative review approaches to integrate the latest clinical and translational findings and provide suggestions to address research gaps. Clinicians and researchers should assess irritability routinely; specific assessment tools are now available. Informant effects are prominent, stable, and vary by age and gender. The prevalence of irritability is particularly high in attention deficit hyperactivity disorder, autism spectrum disorder, and mood and anxiety disorders. Irritability is associated with impairment and suicidality risk independent of co-occurring diagnoses. Irritability trajectories have been identified that are differentially associated with clinical outcomes; some begin early in life. Youth irritability is associated with increased risk later in life for anxiety, depression, behavioral problems, and suicidality. Irritability is moderately heritable and genetic associations differ based on age and comorbid illnesses. Parent management training is effective for constructs related to irritability, but its efficacy in irritability should be tested rigorously, as should novel mechanism-informed interventions (e.g., those targeted to frustration exposure). Associations between irritability and suicidality and the impact of cultural context are important, under-researched topics. Large, diverse, longitudinal samples that extend into adulthood are needed. Data from both animal and human research indicate that aberrant responses to frustration and threat are central to the pathophysiology of irritability, thus affording important translational opportunities

    Emotional and Behavioural Profiles, Parenting Factors and Empathy in Young Children with Callous- Unemotional Traits and/or Autism Spectrum Disorder

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    Callous-Unemotional (CU) traits are associated with more severe behavioural challenges and poorer long-term outcomes. In recent years, research into CU traits has extended into earlier developmental periods. The literature has also taken a neurodevelopmental and transdiagnostic shift into considering CU traits profiles within not only disruptive behaviour disorders, but also Autism Spectrum Disorder. This thesis aims to determine whether CU traits can be observed as a distinct cooccurring feature in young autistic children, and whether high CU traits present with similar profiles of behaviour, regulation, parent-related factors and empathy development across non-autistic and likely-autistic young children. The thesis comprises three empirical studies that involved intensive data collection with families of clinic-referred children. CU traits were measured using the Inventory of Callous-Unemotional Traits, while likely-autistic children were identified using a combination of clinical interview, Social Communication Questionnaire, and ADOS-2 administration. Profiles of child behaviour, regulation, parent-related factors, and empathy were measured using both parent-report questionnaires and in-vivo observational tasks. Results supported the view that CU traits can be observed and measured within early childhood and can co-occur but with a distinct profile within likely-autistic children. Findings also demonstrated that CU traits appear to be characterised by distinct correlates when occurring across non-autistic and likely-autistic young children with conduct problems. Further, CU traits may be more strongly associated than ASD features with various empathy deficits. This research highlights the importance of considering dysregulation within the context of empathy behaviours in young children. We also highlight a lack of convergence between parent-report measures and observational-coded measures of empathy and discuss the implications of this for future research

    AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

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    BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

    Letting the Narrative Unfold: Black Female Storytellers of the 21st Century

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    The important aspects of film and television are the stories that are portrayed. Everyone has a story to tell. However, who tells the story is equally important as who portrays the story. This thesis analyzes three Black female auteurs and the work they have created with Black women at the center of those narratives. Shonda Rhimes, Ava DuVernay, and Issa Rae are Black female auteurs because they are Black female storytellers each with their own story to tell. They each have created and produced content that portrays Black women in a three-dimensional light

    Novel digital biomarkers for frontotemporal dementia

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    Frontotemporal dementia (FTD) is a heterogenous neurodegenerative disease and is caused by an autosomal dominant mutation in around one third of cases. This pattern of inheritance enables FTD to be studied in the presymptomatic phase, where individuals carry the genetic mutation but have yet to develop symptoms. There are currently no approved treatments for FTD, although clinical trials aiming to target interventions at the earliest disease stage, are underway. There is an urgent need for biomarkers that can reliably detect and monitor the progression of disease in the presymptomatic period, though there are a distinct lack of sensitive cognitive measures. This thesis aims to establish the validity and sensitivity of a set of digital biomarkers that can be used to measure cognitive function in FTD. I begin this thesis by describing the Ignite computerised cognitive assessment, developing normative properties for the tests through a remote data collection study in over 2,000 healthy controls. I build upon this validation by establishing the concurrent validity of Ignite with gold-standard pen and paper tasks, the test-retest reliability upon repeated administration, and demonstrate the tests are sensitive to presymptomatic impairment across several cognitive domains. I also describe a novel portable eye tracking experiment that can be completed outside of the lab, first highlighting the validity of the tests as measures of cognitive function and demonstrating their sensitivity in detecting early changes in social cognition in the presymptomatic period. Finally, I investigate a smartphone app that passively monitors human-device interactions to generate digital biomarkers of cognitive function. I establish the acceptability of the app in the general population before demonstrating the measures produced can detect differences in keyboard interactions in presymptomatic FTD mutation carriers. This work provides evidence that biomarkers generated from different digital devices are valid and sensitive measures of cognitive impairment in FTD. Therefore, digital biomarkers could replace outdated pen and paper tasks and be used as outcome measures in clinical trials

    Event structure semantics for multiparty sessions

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    We propose an interpretation of multiparty sessions as "Flow Event Structures", which allows concurrency within sessions to be explicitly represented. We show that this interpretation is equivalent, when the multiparty sessions can be described by global types, to an interpretation of such global types as "Prime Event Structures"
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