New cutting-planes for the time-and/or precedence-constrained ATSP and directed VRP

In this paper, we introduce five classes of new valid cutting planes for the precedence-constrained (PC) and/or time-window-constrained (TW) Asymmetric Travelling Salesman Problems (ATSPs) and directed Vehicle Routing Problems (VRPs). We show that all five classes of new inequalities are facet-defining for the directed VRP-TW, under reasonable conditions and the assumption that vehicles are identical. Similar proofs can be developed for the VRP-PC. As ATSP-TW and PC-ATSP can be formulated as directed identical-vehicle VRP-TW and PC-VRP, respectively, this provides a link to study the polyhedral combinatorics for the ATSP-TW and PC-ATSP. The first four classes of these new cutting planes are cycle-breaking inequalities that are lifted from the well-known D^-_k and D⁺_k inequalities (see Grötschel and Padberg in Polyhedral theory. The traveling salesman problem: a guided tour of combinatorial optimization, Wiley, New York, 1985). The last class of new cutting planes, the TW ₂ inequalities, are infeasible-path elimination inequalities. Separation of these constraints will also be discussed. We also present prelimanry numerical results to demonstrate the strengh of these new cutting planes.

A descriptive interview with 64 patients discharged from an acute-psyciatric-inpatient service

Background Acute-mental-health services receive hundreds of admissions every year. Some of these patients will continue to be case-managed by community mental-health teams on discharge from the acute unit while others will not remain in contact with the mental-health service. This study compares the findings of comprehensive interviews conducted with current and past patients of the community mental-health service 3 or more years following case closure from the community ambulatory service.
Methods Between 1 July 1999 and 30 June 2001, there were 2245 closed cases identified at Barwon Health. Letters of invitation to participate in a research project were sent to people who had suffered from psychotic illnesses, and had been case-closed by community mental-health services between the above dates and had not been in contact with the Community and Mental Health Service for at least 6 months. A second group of participants was recruited from people who had also been case-closed by community mental health teams in Barwon Health during the 1999–2001 2-year-time window but whose cases had been re-opened and who were in case management with Barwon Health at the time of the study. All participants were interviewed using the Diagnostic Interview for Psychosis.
Results Letter responses were received from 17 men and 18 women, aged 40.7 ± 12.0 (mean ± SD), who were interviewed. A second group of 17 men and 12 women, aged 40.9 ± 9.6 (mean ± SD) of currently case-managed patients was interviewed. All interviewees reported a detailed history of mental illness. Persistent social dysfunction and impaired quality of life were reported in both groups.
Conclusion Patients suffering from psychotic disorders who had been case-closed by community mental-health teams and had been discharged to the care of their general practitioners or elsewhere continued to show evidence of significant impairment due to mental illness 3 years after being case-closed.

A predictive framework for modeling healthcare data with evolving clinical interventions

Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.

A new semi-automated method for assessing avian acoustic networks reveals that juvenile and adult zebra finches have separate calling networks

Social networks are often inferred from spatial associations, but other parameters like acoustic communication are likely to play a central role in within group interactions. However, it is currently difficult to determine which individual initiates vocalizations, or who responds to whom. To this aim, we designed a method that allows analyzing group vocal network while controlling for spatial networks, by positioning each group member in equidistant individual cages and analyzing continuous vocal interactions semi-automatically. We applied this method to two types of zebra finch groups, composed of either two adult females and two juveniles, or four young adults (juveniles from the first groups). Young often co-occur in the same social group as adults but are likely to have a different social role, which may be reflected in their vocal interactions. Therefore, we tested the hypothesis that the social structure of the group influences the parameters of the group vocal network. We found that groups including juveniles presented periods with higher level of activity than groups composed of young adults. Using two types of analyses (Markov analysis and cross-correlation), we showed that juveniles as well as adults were more likely to respond to individuals of their own age-class (i.e. to call one after another, in terms of turn-taking, and within a short time-window, in terms of time delay). When juveniles turned into adulthood, they showed adult characteristics of vocal patterns. Together our results suggest that vocal behavior changes during ontogeny, and individuals are more strongly connected with individuals of the same age-class within acoustic networks.

The Great East Window of York Minster

This inquiry into the meaning behind the images of this vast stained glass window has uncovered a complex theology of the Mass; replies to Lollard 'heretics'; and has identified threats to the power of the Church at this critical time in English history at the beginning of the fifteenth century.

Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.

Love, politics, time

Featuring the musical compositional techniques of phase, repetition and pulse, with the sounds of New York recorded from a 16th floor hotel window, this sonic poem is a plea for the intimately spoken word. As cockatoos rise in the white siren sky, two lovers confront love and time in a halting conversation inside a placeless shelter.
This performance work is a poetic and musical experimentation with ideas from the philosopher Alain Badiou. The intersection of political and amorous truth procedures thought to form the subject matter of many novels is extended upon by presenting such an intersection via the crossing of genres of music, sound art, poetry, prose and theatre. This collaborative venture forms a continuing experiment with the idea that music does not simply form a corollary with words and their representation in sound, but rather explores ways in which music can form an antagonistic relationship to the spoken word.
'Conversation in an air raid shelter' was originally presented as a live performance at Double Dialogues Conference: 'The 21st century - The Event, The Subject, The Artwork', Fiji, 2012 and the audio recording appears in Double Dialogues Issue 16, Spring 2013 with an accompanying discursive article 'Love, Politics, Time'. It is available on CD and Youtube. It was also performed at the Torquay Literary Festival in 2013. A discussion of its process by Josephine Scicluna features on a video currently in production by Deakin University for a new unit on creativity in the Bachelor of Arts program.

A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.