Fractionating autism based on neuroanatomical normative modeling

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6–31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case–control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

Troubles on the Border

This paper aims to determine the extent and effects of the phenomenon of inconsistent case-particle boundary marking in the grammatical annotation of Japanese. It is focused on establishing what represents ‘inconsistent boundary marking’, how it is dealt with in informational terms, what effect it has on communication, and why it should be avoided. To this purpose, I will first build a typology of the tokenization strategies in the grammatical annotation of Japanese. I will then individuate several forms of inconsistent boundary marking and, more in general, of poor grammatical annotation, and discuss them according to the types of inconsistency and their different epistemic effects.

The Most Preferred and Effective Reviewer of L2 Writing among Automated Grading System, Peer Reviewer and Teacher

Who is the most preferred and deemed the most helpful reviewer in improving student writing? This study exerciseda blended teaching method which consists of three currently prevailing reviewers: the automated grading system(AGS, a web-based method), the peer review (a process-oriented approach), and the teacher grading technique (theproduct-oriented approach) in a Writing (IV) class involving 22 technological sophomore students of ModernLanguages Department. The questionnaire results indicated the participants preferred the teacher as the reviewer totheir peers followed by the automated grading system and considered the teacher the most effective in helping theirwriting. Three L2 teachers including one native speaker of English reviewed an essay which was the only and themost inconsistent case between a human rater and a machine rater in the study (2.3 vs. 3.6). This case surfaced anessential problem that the automated grading system couldn’t detect and correct expressions transferred from L1.Data also revealed that teachers without training, their grammatical error identification rates are respectively 82.9%,31.4% and 74.3%. After training, student reviewers could detect and correct from 70.2 to 79.3 percent of grammarerrors on average.

The Application of Fuzzy Data Processing Technology in the Network Failure Mining

In order to solve the coordinate problem of high redundancy and lack of stability in traditional failure mining knowledge base, this paper presents a fuzzy failure mining algorithm to realize regular acquisition in inconsistent case and purification of learning samples by the comprehensive application of fuzzy data processing technology. With characters of simplified samples, strong adaptability, high failure tolerance and not easy to fall into local minimum point, the algorithm can effectively process the diagnosis and incompatible information in network failure mining. The experiments show that the system implemented by this method improves accuracy and speed of diagnosis and has a certain application value comparing to other similar methods.

Iterative algorithms with seminorm-induced oblique projections

A definition of oblique projections onto closed convex sets that use seminorms induced by diagonal matrices which may have zeros on the diagonal is introduced. Existence and uniqueness of such projections are secured via directional affinity of the sets with respect to the diagonal matrices involved. A block-iterative algorithmic scheme for solving the convex feasibility problem, employing seminorm-induced oblique projections, is constructed and its convergence for the consistent case is established. The fully simultaneous algorithm converges also in the inconsistent case to the minimum of a certain proximity function.