Carnegie Mellon University Australia

The purpose of this systematic review was to estimate worldwide the number of people with moderate and severe visual impairment (MSVI; presenting visual acuity <6/18, ≥3/60) or blindness (presenting visual acuity <3/60) due to uncorrected refractive error (URE), to estimate trends in prevalence from 1990 to 2010, and to analyze regional differences. The review focuses on uncorrected refractive error which is now the most common cause of avoidable visual impairment globally. : The systematic review of 14,908 relevant manuscripts from 1990 to 2010 using Medline, Embase, and WHOLIS yielded 243 high-quality, population-based cross-sectional studies which informed a meta-analysis of trends by region. The results showed that in 2010, 6.8 million (95% confidence interval [CI]: 4.7-8.8 million) people were blind (7.9% increase from 1990) and 101.2 million (95% CI: 87.88-125.5 million) vision impaired due to URE (15% increase since 1990), while the global population increased by 30% (1990-2010). The all-age age-standardized prevalence of URE blindness decreased 33% from 0.2% (95% CI: 0.1-0.2%) in 1990 to 0.1% (95% CI: 0.1-0.1%) in 2010, whereas the prevalence of URE MSVI decreased 25% from 2.1% (95% CI: 1.6-2.4%) in 1990 to 1.5% (95% CI: 1.3-1.9%) in 2010. In 2010, URE contributed 20.9% (95% CI: 15.2-25.9%) of all blindness and 52.9% (95% CI: 47.2-57.3%) of all MSVI worldwide. The contribution of URE to all MSVI ranged from 44.2 to 48.1% in all regions except in South Asia which was at 65.4% (95% CI: 62-72%). : We conclude that in 2010, uncorrected refractive error continues as the leading cause of vision impairment and the second leading cause of blindness worldwide, affecting a total of 108 million people or 1 in 90 persons.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Oliver Adams, Adam Makarucha, Graham Neubig, Steven Bird, Trevor Cohn. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

Array multipliers are well suited for VLSI implementation because of the regularity in their iterative structure. However, most VLSI circuits are difficult to test. This correspondence shows that, with appropriate cell design, array multipliers can be designed to be very easily testable. An array multiplier is called C-testable if all its adder cells can be exhaustively tested while requiring only a constant number of test patterns. The testability of two well-known array multiplier structures is studied in detail. The conventional design of the carry–save array multiplier is modified. The modified design is shown to be C-testable and requires only 16 test patterns. Similar results are obtained for the Baugh–Wooley two's complement array multiplier. A modified design of the Baugh–Wooley array multiplier is shown to be C-testable and requires 55 test patterns. The C-testability of two other array multipliers, namely the carry–propagate and the TRW designs, is also presented.

Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those encountered during planetary or ocean exploration, a further limit is imposed by communication bandwidth and delay. Market based planning can give humans a higher-level interface to multi-robot systems in these scenarios. Operators provide high level tasks and attach a reward to the achievement of each task. The robots then trade these tasks through a market based mechanism. The challenge for the system designer is to create bidding algorithms for the robots that yield high overall system performance. Opportunity cost provides a nice basis for such bidding algorithms since it encapsulates all the costs and benefits we are interested in. Unfortunately, computing it can be difficult. We propose a method of learning opportunity costs in market based planners. We provide analytic results in simplified scenarios and empirical results on our FIRE simulator, which focuses on exploration of Mars by multiple, heterogeneous rovers.

We consider a common indivisible good allocation problem whose popular applications include on‐campus housing, kidney exchange, and school choice. We show that the so‐called New House 4 (NH4) mechanism, which has been in use at MIT since the 1980s, is equivalent to a natural adaptation of the well‐known Gale–Shapley (GS) mechanism. We run two experiments comparing NH4 with the prominently advocated Top Trading Cycles (TTC) mechanism and NH4 with GS. We find that under NH4, the participation rate is significantly higher than under TTC. Based on a new ordinal test of efficiency, NH4 is more likely to Pareto dominate TTC.

Enterprise architecture (EA) is useful for effectively structuring digital platforms with digital transformation in information societies. Moreover, digital platforms in the healthcare industry accelerate and increase the efficiency of drug discovery and development processes. However, there is the lack of knowledge concerning relationships between EA and digital platforms, in spite of the needs of it. In this paper, we investigated and analyzed the process of drug design and development within the healthcare industry, together with related work in using an enterprise architecture framework for the digital era named the Adaptive Integrated Digital Architecture Framework (AIDAF), specifically supporting the design of digital platforms there. Based on this analysis, we evaluate a method and propose a new reference architecture for promoting digital platforms in the healthcare industry, with future specific aspects of them making effective use of Artificial Intelligence (AI). The practical and theoretical contributions include: (1) Streamlined processes through digital platforms in organizations. (2) Informal knowledge supply and sharing among organizational members through digital platforms. (3) Efficiency and effectiveness in planning production and business for drug development. The findings indicate that EA with digital platforms using the AIDAF contribute to digital transformation with effectiveness for new drugs in the healthcare industry.

Gene regulatory networks (GRNs) describe the progression of transcriptional states that take a single-celled zygote to a multicellular organism. It is well documented that GRNs can evolve extensively through mutations to cis-regulatory modules (CRMs). Transcription factor proteins that bind these CRMs may also evolve to produce novelty. Coding changes are considered to be rarer, however, because transcription factors are multifunctional and hence are more constrained to evolve in ways that will not produce widespread detrimental effects. Recent technological advances have unearthed a surprising variation in DNA-binding abilities, such that individual transcription factors may recognize both a preferred primary motif and an additional secondary motif. This provides a source of modularity in function. Here, we demonstrate that orthologous transcription factors can also evolve a changed preference for a secondary binding motif, thereby offering an unexplored mechanism for GRN evolution. Using protein-binding microarray, surface plasmon resonance, and in vivo reporter assays, we demonstrate an important difference in DNA-binding preference between Tbrain protein orthologs in two species of echinoderms, the sea star, Patiria miniata, and the sea urchin, Strongylocentrotus purpuratus. Although both orthologs recognize the same primary motif, only the sea star Tbr also has a secondary binding motif. Our in vivo assays demonstrate that this difference may allow for greater evolutionary change in timing of regulatory control. This uncovers a layer of transcription factor binding divergence that could exist for many pairs of orthologs. We hypothesize that this divergence provides modularity that allows orthologous transcription factors to evolve novel roles in GRNs through modification of binding to secondary sites.

Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies (GDDx), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined GDDxs. Finally, we evaluate our synthetic KG generator and compare with several state-of-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph's schema.

Medical science has a long history characterised by incidents of extraordinary insights that have resulted in a paradigm shift in the methodologies and approaches used and have moved the discipline forward. While knowledge discovery has much to offer medicine, it cannot be done in ignorance of either this history or the norms of modern medical investigation. This paper explores the lineage of medical knowledge acquisition and discusses the adverse perceptions that data mining techniques will have to surmount to gain acceptance.

This article employs the currency invariant index due to Hovanov et al. (2004 Hovanov, N. K., Kolari, J. and Sokolov, M. 2006. Synthetic money. International Review of Economics and Finance, 16: 161–8. [Crossref] , [Google Scholar]) to construct an optimal or stable common G-3 currency basket across different groups of countries in East Asia. Calculated optimal weights show a larger weight for the US dollar but a nonnegligible role for the Japanese yen. The volatility of the optimal common G-3 currency basket is several times smaller than that of a similarly proposed common G-3 currency basket in East Asia.

Reducing the operating energy costs of commercial buildings in the presence of complex tariff structures is an important problem facing several facility managers. In this paper, we explore the use of a data driven pre-cooling methodology for achieving this outcome. Our contributions are twofold. First, we propose a "gray box" approach to model the building thermal dynamics that imposes minimal data requirements from a building management system (BMS). Second, we illustrate how the model can be used to evaluate various "what-if" pre-cooling strategies to reduce peak demand by applying it to data obtained from a large commercial building located in Australia. The proposed approach enables facility managers to take informed decisions for improving the energy and cost footprints of their buildings. This paper sets the ground for a deeper study into using pre-cooling, driven by our gray box model, for energy cost optimization in commercial buildings.

The key objective of this study is to show that two potential shortcomings of the Determinant of Change in Covariance (DCC) matrix procedure of Rigobon (2003 Rigobon, R. 2003. On the measurement of the international propagation of shocks: is the transmission stable?. Journal of International Economics, 61: 261–83. [Crossref], [Web of Science ®] , [Google Scholar]), namely with the arbitrary determination of the windows, i.e. tranquil and crisis periods and the violation of its heteroscedasticity assumption under the null, can be simultaneously addressed via a simple incorporation of a Markov-switching vector autoregressive approach into the overall DCC procedure. To demonstrate this, we revisit the period around the time of the East Asian crises using daily stock exchange of Indonesia, Malaysia, Philippines, Thailand, Singapore, Korea, Hong Kong and Taiwan, and test whether there is a significant break or discontinuity in the stock exchange returns of the eight East Asian markets during crisis periods, especially around the time of the 1997 financial crises. In contrast to that of Rigobon (2003 Rigobon, R. 2003. On the measurement of the international propagation of shocks: is the transmission stable?. Journal of International Economics, 61: 261–83. [Crossref], [Web of Science ®] , [Google Scholar]), our results show that the propagation of shocks shifted significantly starting with the onset of the sharp decline in the Hong Kong stock market.

Emotion recognition based on a speech signal is one of the intensively studied research topics in the domains of human-computer interaction and affective computing. The presented paper is concerned with emotional-speech recognition based on the neuro-fuzzy network with a weighted fuzzy membership function (NEWFM). NEWFM has a feature selection method and makes fuzzy classifiers. In this paper, NEWFM was utilized for classifying four kinds of emotional-speech signals. This NEWFM classification method achieves as high as 86% overall classification accuracy. Significantly, the NEWFM classifier efficiently detects sadness, with a 97.5% recognition rate.

Abstract Governments using data analytics will be increasingly drawn into creating social licence for these applications. The need will be greatest where two conditions are present. First, where data analytics are used to predict rather than describe or prescribe. Second, where such prediction is used by governments when exercising their coercive powers. Two examples of using predictive risk modelling are identifying children at risk of neglect and abuse; and assessing recidivism risk in the criminal justice system. Each has drawn criticism, precipitating discussion around the requirements for social licence. Much of this discussion focusses on transparency as both an intrinsic virtue and an instrumental virtue for achieving social licence. The paper contends that transparency is unachievable as an intrinsic virtue for such purposes and that its conceptions as an instrumental virtue fall short of that required for users and subjects as well as the public to have “sufficient to approve or disapprove of the algorithm's performance”. The conclusion is that unconventional democratic forms, including deliberative and direct democracy, are likely to prove more successful than representative democracy in establishing that licence and thereby realising more fully the potential contribution of data analytics to better government.

Most satellite communications systems are designed to operate with a line-of-sight between the transmitter and receiver. Multiple users access the resources of a satellite though multiple access protocols. Resource allocation, or channel multiplexing, may be done in a number of ways, but it generally follows four domains: frequency, time, space and code. This chapter discusses the frequency division multiple access (FDMA) and time division multiple access (TDMA). Usage of geostationary Earth orbit (GEO) satellites for international voice and TV broadcast service requires large capacity links and therefore high-gain antennas which can be directed to the satellite. Two classes of satellites including LEO satellites and MEO satellites relevant to telecommunications exist beside GEO. Since the altitude of these two classes of satellite systems is lower than for the GEO satellite, the path loss experienced by their signals is smaller and therefore smaller antennas can be used for achieving their link budget.

Mapping structured data to a shared domain ontology is a key step in publishing semantic content on the Web. This problem is known as Relational-To-Ontology Mapping Problem (Rel2Onto). Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Most of the related work focuses on semantic annotation of source attributes. However, besides semantically annotating source attributes, it is challenging to explicitly infer the relationships between attributes. In this paper we improve previous work by Taheriyan et al. [4] using Subgraph Matching to take into account frequencies of candidate semantic models occurring in the domain knowledge graph used as background knowledge. Preliminary experiments demonstrate that our method achieves higher precision and recall than the state-of-the-art solutions in the difficult scenarios where only few historical mappings between domain ontology and data sources are available.

Proxy Mobile IPv6 (PMIPv6) is a network-based localized mobility support in an IP network. The main advantage of using PMIPv6 is the freeing up of the mobile host when undertaking any mobility-related activity, thereby saving its resources. The mobile access gateway (MAG), however, incurs a high signaling cost for updating the location of a mobile node to the remote local mobility anchor (LMA) if it moves frequently. As it may also cause excessive signaling traffic and high traffic load on LMA, in this paper, a novel LMA selection scheme for load control in PMIPv6 networks is proposed. In the proposed scheme, the AAA server selects the most suitable LMA based on the load information received from the LMAs. Moreover, the LMA performs admission control based on the number of current mobile nodes registered to the LMA. Therefore, the proposed scheme achieves actual load balancing among the LMAs. The cost analysis using the Markov chain presented in this paper shows that the proposed scheme can achieve a performance superior to that of the PMIPv6 scheme

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants.

Recent research indicates a significant association that the severity of fatigue and the autonomic nervous system (ANS) by analyzing the heart rate variability (HRV). In order to detect fatigue, an experiment that provides the subjects with some affective contents that can induce the variety of emotions and ANS was designed in this study. Each subject underwent an affective-content test while wearing a wireless Holter monitor. By analyzing the 20 subjects' HRV episode in the experiment, a new fatigue detection algorithm was established based on six features of the time and frequency domain (TFD) HRV and a neuro-fuzzy network. The six TFD features were used for the 20 subjects, with a reliable accuracy rate of 95%. The proposed algorithm can realize service for affective healthcare applications, such as the monitoring of the fatigability of humans in a ubiquitous environment.