Exploring the Cognitive Foundations of the Shared Attention Mechanism: Evidence for a Relationship Between Self-Categorization and Shared Attention Across the Autism Spectrum.

Published onlineJournal ArticleThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The social difficulties of autism spectrum disorder (ASD) are typically explained as a disruption in the Shared Attention Mechanism (SAM) sub-component of the theory of mind (ToM) system. In the current paper, we explore the hypothesis that SAM's capacity to construct the self-other-object relations necessary for shared-attention arises from a self-categorization process, which is weaker among those with more autistic-like traits. We present participants with self-categorization and shared-attention tasks, and measure their autism-spectrum quotient (AQ). Results reveal a negative relationship between AQ and shared-attention, via self-categorization, suggesting a role for self-categorization in the disruption in SAM seen in ASD. Implications for intervention, and for a ToM model in which weak central coherence plays a role are discussed.This research was supported by the Australian Research Council (FLFL110100199) and the Canadian Institute for Advanced Research (Social Interactions Identity and Well-Being Program)

A guide to chemokines and their receptors

The chemokines (or chemotactic cytokines) are a large family of small, secreted proteins that signal through cell surface G‐protein coupled heptahelical chemokine receptors. They are best known for their ability to stimulate the migration of cells, most notably white blood cells (leukocytes). Consequently, chemokines play a central role in the development and homeostasis of the immune system, and are involved in all protective or destructive immune and inflammatory responses. Classically viewed as inducers of directed chemotactic migration, it is now clear that chemokines can stimulate a variety of other types of directed and undirected migratory behaviour, such as haptotaxis, chemokinesis, and haptokinesis, in addition to inducing cell arrest or adhesion. However, chemokine receptors on leukocytes can do more than just direct migration, and these molecules can also be expressed on, and regulate the biology of, many non‐leukocytic cell types. Chemokines are profoundly affected by post‐translational modification, by interaction with the extracellular matrix (ECM), and by binding to heptahelical ‘atypical’ chemokine receptors that regulate chemokine localisation and abundance. This guide gives a broad overview of the chemokine and chemokine receptor families; summarises the complex physical interactions that occur in the chemokine network; and, using specific examples, discusses general principles of chemokine function, focussing particularly on their ability to direct leukocyte migration

Transmit Power Minimization for MIMO Systems of Exponential Average BER with Fixed Outage Probability

This document is the Accepted Manuscript version of the following article: Dian-Wu Yue, and Yichuang Sun, ‘Transmit Power Minimization for MIMO Systems of Exponential Average BER with Fixed Outage Probability’, Wireless Personal Communications, Vol. 90 (4): 1951-1970, first available online on 20 June 2016. Under embargo. Embargo end date: 20 June 2017. The final publication is available at Springer via https://link.springer.com/article/10.1007%2Fs11277-016-3432-4This paper is concerned with a wireless multiple-antenna system operating in multiple-input multiple-output (MIMO) fading channels with channel state information being known at both transmitter and receiver. By spatiotemporal subchannel selection and power control, it aims to minimize the average transmit power (ATP) of the MIMO system while achieving an exponential type of average bit error rate (BER) for each data stream. Under the constraints on each subchannel that individual outage probability and average BER are given, based on a traditional upper bound and a dynamic upper bound of Q function, two closed-form ATP expressions are derived, respectively, which can result in two different power allocation schemes. Numerical results are provided to validate the theoretical analysis, and show that the power allocation scheme with the dynamic upper bound can achieve more power savings than the one with the traditional upper bound.Peer reviewe

Analyzing Network Level Information

This chapter provides a brief description of the methods employed for collecting initial information about a given suspicious online communication message, including header and network information; and how to forensically analyze the dataset to attain the information that would be necessary to trace back to the source of the crime. The header content and network information are usually the immediate sources for collecting preliminary information about a given collection of suspicious online messages. The header analysis of an e-mail corpus identifying all the senders, the recipients associated with each sender, and the frequency of messages exchanged between users helps an investigator to understand the overall nature of e-mail communication. Electronic messages like e-mails or virtual network data present a potential dataset or a source of evidence containing personal communications, critical business communications, or agreements. When a crime is committed, it is always possible for the perpetrator to manipulate e-mails or any electronic evidence, forging the details to remove relevant evidence or tampering the data to mislead the investigator. Possible manipulation of such evidence may include backdating, executing time-stamp changes, altering the message sender, recipient, or message content, etc. However, such attempts of manipulation and misleading can be detected by examining the message header. By examining e-mail header and analyzing network information through forensic analysis, investigators can gain valuable insight into the source of a message that is otherwise not traceable through the message body. Investigators can utilize a range of existing algorithms and models and build on leveraging typical forensic planning. Such models focus on what type of information should be collected, ensuring the forensically sound collection and preservation of identified Electronically Stored Information (ESI). By applying these models, it is possible to achieve a full analysis and collect all the relevant information pertaining to the crime. The collected finding is then compiled to reconstruct the whole crime scene, deduct more accurate and logical conclusions [1]

Criminal Information Mining

In the previous chapters, the different aspects of the authorship analysis problem were discussed. This chapter will propose a framework for extracting criminal information from the textual content of suspicious online messages. Archives of online messages, including chat logs, e-mails, web forums, and blogs, often contain an enormous amount of forensically relevant information about potential suspects and their illegitimate activities. Such information is usually found in either the header or body of an online document. The IP addresses, hostnames, sender and recipient addresses contained in the e-mail header, the user ID used in chats, and the screen names used in web-based communication help reveal information at the user or application level. For instance, information extracted from a suspicious e-mail corpus helps us to learn who the senders and recipients are, how often they communicate, and how many types of communities/cliques there are in a dataset. Such information also gives us an insight into the inter and intra-community patterns of communication. A clique or a community is a group of users who have an online communication link between them. Header content or user-level information is easy to extract and straightforward to use for the purposes of investigation

On the discovery of continuous truth: a semi-supervised approach with partial ground truths

In many applications, the information regarding to the same object can be collected from multiple sources. However, these multi-source data are not reported consistently. In the light of this challenge, truth discovery is emerged to identify truth for each object from multi-source data. Most existing truth discovery methods assume that ground truths are completely unknown, and they focus on the exploration of unsupervised approaches to jointly estimate object truths and source reliabilities. However, in many real world applications, a set of ground truths could be partially available. In this paper, we propose a semi-supervised truth discovery framework to estimate continuous object truths. With the help of ground truths, even a small amount, the accuracy of truth discovery can be improved. We formulate the semi-supervised truth discovery problem as an optimization task where object truths and source reliabilities are modeled as variables. The ground truths are modeled as a regularization term and its contribution to the source weight estimation can be controlled by a parameter. The experiments show that the proposed method is more accurate and efficient than the existing truth discovery methods

Genetically predicted circulating protein biomarkers and ovarian cancer risk.

OBJECTIVE: Most women with epithelial ovarian cancer (EOC) are diagnosed after the disease has metastasized and survival in this group remains poor. Circulating proteins associated with the risk of developing EOC have the potential to serve as biomarkers for early detection and diagnosis. We integrated large-scale genomic and proteomic data to identify novel plasma proteins associated with EOC risk. METHODS: We used the germline genetic variants most strongly associated (P <1.5 × 10-11) with plasma levels of 1329 proteins in 3301 healthy individuals from the INTERVAL study to predict circulating levels of these proteins in 22,406 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). Association testing was performed by weighting the beta coefficients and standard errors for EOC risk from the OCAC study by the inverse of the beta coefficients from INTERVAL. RESULTS: We identified 26 proteins whose genetically predicted circulating levels were associated with EOC risk at false discovery rate < 0.05. The 26 proteins included MFAP2, SEMG2, DLK1, and NTNG1 and a group of 22 proteins whose plasma levels were predicted by variants at chromosome 9q34.2. All 26 protein association signals identified were driven by association with the high-grade serous histotype that comprised 58% of the EOC cases in OCAC. Regional genomic plots confirmed overlap of the genetic association signal underlying both plasma protein level and EOC risk for the 26 proteins. Pathway analysis identified enrichment of seven biological pathways among the 26 proteins (Padjusted <0.05), highlighting roles for Focal Adhesion-PI3K-Akt-mTOR and Notch signaling. CONCLUSION: The identified proteins further illuminate the etiology of EOC and represent promising new EOC biomarkers for targeted validation by studies involving direct measurement of plasma proteins in EOC patient cohorts

Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts

Inattentive Consumers in Markets for Services

In an experiment on markets for services, we find that consumers are likely to stick to default tariffs and achieve suboptimal outcomes. We find that inattention to the task of choosing a better tariff is likely to be a substantial problem in addition to any task and tariff complexity effect. The institutional setup on which we primarily model our experiment is the UK electricity and gas markets, and our conclusion is that the new measures by the UK regulator Ofgem to improve consumer outcomes are likely to be of limited impact