Scattering theory and the discrete linear optimal control problem

This paper deals with the interpretation of the discrete-time optimal control problem as a scattering process in a discrete medium. We treat the discrete optimal linear regulator, constrained end-point and servo and tracking problems, providing a unified approach to these problems. This approach results in an easy derivation of the desired results as well as several new ones.

A note on Sommerfeld's diffraction problem

A direct transform technique is found to be most suitable for attacking two-dimensional diffraction problems. As a first example of the application of the technique, the well-known Sommerfeld problem is reconsidered and the solution of the problem of diffraction, by a half-plane, of a cylindrical pulse is made use of in deducing the solution of the problem of diffraction of a plane wave by a soft half-plane. Journal of Mathematical Physics is copyrighted by The American Institute of Physics.

Power Minimization for CDMA under Colored Noise

Rate-constrained power minimization (PMIN) over a code division multiple-access (CDMA) channel with correlated noise is studied. PMIN is. shown to be an instance of a separable convex optimization problem subject to linear ascending constraints. PMIN is further reduced to a dual problem of sum-rate maximization (RMAX). The results highlight the underlying unity between PMIN, RMAX, and a problem closely related to PMIN but with linear receiver constraints. Subsequently, conceptually simple sequence design algorithms are proposed to explicitly identify an assignment of sequences and powers that solve PMIN. The algorithms yield an upper bound of 2N - 1 on the number of distinct sequences where N is the processing gain. The sequences generated using the proposed algorithms are in general real-valued. If a rate-splitting and multi-dimensional CDMA approach is allowed, the upper bound reduces to N distinct sequences, in which case the sequences can form an orthogonal set and be binary +/- 1-valued.

The transfer problem and the intertemporal terms of trade

In this paper the effects of a transfer on the intertemporal terms of trade are examined in the context of a simple two-country, two-period model. When intertemporal trade occurs because the two economies have different rates of time preference, a transfer improves the terms of trade of the paying country. Alternatively, when trade occurs owing to international differences in the endowments of goods over the two periods, the effect of a transfer depends on (a) the relationship between the interest rate and the rates of time preference of the two countries and (b) the relationship between their elasticities of intertemporal consumption substitution.

Functional and cellular analysis of autoimmune regulator (AIRE) protein

Autoimmune diseases are a major health problem. Usually autoimmune disorders are multifactorial and their pathogenesis involves a combination of predisposing variations in the genome and other factors such as environmental triggers. APECED (autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy) is a rare, recessively inherited, autoimmune disease caused by mutations in a single gene. Patients with APECED suffer from several organ-specific autoimmune disorders, often affecting the endocrine glands. The defective gene, AIRE, codes for a transcriptional regulator. The AIRE (autoimmune regulator) protein controls the expression of hundreds of genes, representing a substantial subset of tissue-specific antigens which are presented to developing T cells in the thymus and has proven to be a key molecule in the establishment of immunological tolerance. However, the molecular mechanisms by which AIRE mediates its functions are still largely obscure. The aim of this thesis has been to elucidate the functions of AIRE by studying the molecular interactions it is involved in by utilizing different cultured cell models. A potential molecular mechanism for exceptional, dominant, inheritance of APECED in one family, carrying a glycine 228 to tryptophan (G228W) mutation, was described in this thesis. It was shown that the AIRE polypeptide with G228W mutation has a dominant negative effect by binding the wild type AIRE and inhibiting its transactivation capacity in vitro. The data also emphasizes the importance of homomultimerization of AIRE in vivo. Furthermore, two novel protein families interacting with AIRE were identified. The importin alpha molecules regulate the nuclear import of AIRE by binding to the nuclear localization signal of AIRE, delineated as a classical monopartite signal sequence. The interaction of AIRE with PIAS E3 SUMO ligases, indicates a link to the sumoylation pathway, which plays an important role in the regulation of nuclear architecture. It was shown that AIRE is not a target for SUMO modification but enhances the localization of SUMO1 and PIAS1 proteins to nuclear bodies. Additional support for the suggestion that AIRE would preferably up-regulate genes with tissue-specific expression pattern and down-regulate housekeeping genes was obtained from transactivation studies performed with two models: human insulin and cystatin B promoters. Furthermore, AIRE and PIAS activate the insulin promoter concurrently in a transactivation assay, indicating that their interaction is biologically relevant. Identification of novel interaction partners for AIRE provides us information about the molecular pathways involved in the establishment of immunological tolerance and deepens our understanding of the role played by AIRE not only in APECED but possibly also in several other autoimmune diseases.

Popular Mixed Matchings

We study the problem of matching applicants to jobs under one-sided preferences: that is, each applicant ranks a non-empty subset of jobs under an order of preference, possibly involving ties. A matching M is said to be rnore popular than T if the applicants that prefer M to T outnumber those that prefer T to M. A matching is said to be popular if there is no matching more popular than it. Equivalently, a matching M is popular if phi(M,T) >= phi(T, M) for all matchings T, where phi(X, Y) is the number of applicants that prefer X to Y. Previously studied solution concepts based oil the popularity criterion are either not guaranteed to exist for every instance (e.g., popular matchings) or are NP-hard to compute (e.g., least unpopular matchings). This paper addresses this issue by considering mixed matchings. A mixed matching is simply a probability distributions over matchings in the input graph. The function phi that compares two matchings generalizes in a natural manner to mixed matchings by taking expectation. A mixed matching P is popular if phi(P,Q) >= phi(Q,P) for all mixed matchings Q. We show that popular mixed matchings always exist. and we design polynomial time algorithms for finding them. Then we study their efficiency and give tight bounds on the price of anarchy and price of stability of the popular matching problem.

Robust THP Transceiver Designs for Multiuser MIMO Downlink with Imperfect CSIT

We present robust joint nonlinear transceiver designs for multiuser multiple-input multiple-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. The BS employs Tomlinson-Harashima precoding (THP) for interuser interference precancellation at the transmitter. We consider robust transceiver designs that jointly optimize the transmit THP filters and receive filter for two models of CSIT errors. The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed. This model is applicable when the CSIT error is dominated by channel estimation error. In this case, the proposed robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. The other model we consider is a norm-bounded error (NBE) model, where the CSIT error can be specified by an uncertainty set. This model is applicable when the CSIT error is dominated by quantization errors. In this case, we consider a worst-case design. For this model, we consider robust (i) minimum SMSE, (ii) MSE-constrained, and (iii) MSE-balancing transceiver designs. We propose iterative algorithms to solve these problems, wherein each iteration involves a pair of semidefinite programs (SDPs). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints. We evaluate the robustness of the proposed algorithms to imperfections in CSIT through simulation, and show that the proposed robust designs outperform nonrobust designs as well as robust linear transceiver designs reported in the recent literature.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

What the problem may be with family intervention . . . and with dissemination more generally: A commentary

While the two decades since the study by Kavanagh et al. (1993) has given additional insights into effective dissemination of family interventions, the accompanying papers show that progress remains limited. The effectiveness trial that triggered this series of papers offers a cautionary tale. Despite management support, 30–35 hr of workshop training and training of local supervisors who could act as champions, use of the full intervention was limited. In part, this seemed due to the demanding nature of the intervention and its incompatibility with practitioners’ roles, in part, to limitations in the training, among other factors. While the accompanying papers note these and other barriers to dissemination, they miss a more disturbing finding in the original paper: Practitioners said they were using several aspects in routine care, despite being unable to accurately describe what they were. This finding highlights the risks in taking practitioners’ reports of their practice in files or supervision sessions at face value and potentially has implications for reports of other clinical work. The fidelity of disseminated treatments can only be assured by audits of practice, accompanied by affirming but also corrective feedback.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Ballistic Trauma in Finland : An Epidemiologic and Clinical Study of Firearm and Explosion Injuries

The occurrence and nature of civilian firearm- and explosion-injuries in Finland, and the nature of severe gunshot injuries of the extremities were described in seven original articles. The main data sources used were the National Hospital Discharge Register, the Cause-of-Death Register, and the Archive of Death Certificates at Statistics Finland. The present study was population based. Epidemiologic methods were used in six and clinical analyses in five papers. In these clinical studies, every original hospital record and death certificate was critically analyzed. The trend of hospitalized firearm injuries has slightly declined in Finland from the late 1980s to the early 2000s. The occurrence decreased from 5.1 per 100 000 person-years in 1990 to 2.6 in 2003. The decline was found in the unintentional firearm injuries. A high incidence of unintentional injuries by firearms was characteristic of the country, while violence and homicides by firearms represented a minor problem. The incidence of fatal non-suicidal firearm injuries has been stable, 1.8 cases per 100 000 person-years. Suicides using firearms were eight times more common during the period studied. This is contrary to corresponding reports from many other countries. However, the use of alcohol and illegal drugs or substances was detected in as many as one-third of the injuries studied. The median length of hospitalization was three days and it was significantly associated (p<0.001) with the type of injury. The mean length of hospital stay has decreased from the 1980s to the early 2000s. In this study, there was a special interest in gunshot injuries of the extremities. From a clinical point of view, the nature of severe extremital gunshot wounds, as well as the primary operative approach in their management, varied. The patients with severe injuries of this kind were managed at university and central hospital emergency departments, by general surgeons in smaller hospitals and by cardiothoracic or vascular surgeons in larger hospitals. Injuries were rarities and as such challenges for surgeons on call. Some noteworthy aspects of the management were noticed and these should be focused on in the future. On the other hand, the small population density and the relatively large geographic area of Finland do not favor high volume, centralized trauma management systems. However, experimental war surgery has been increasingly taught in the country from the 1990s, and excellent results could be expected during the present decade. Epidemiologically, explosion injuries can be considered a minor problem in Finland at present, but their significance should not be underestimated. Fatal explosion injuries showed up sporadically. An increase occurred from 2002 to 2004 for no obvius reason. However, in view of the historical facts, a possibility for another rare major explosion involving several people might become likely within the next decade. The national control system of firearms is mainly based on the new legislations from 1998 and 2002. However, as shown in this study, there is no reason to assume that the national hospitalization policies, or the political climate, or the legislation might have changed over the study period and influenced the declining development, at least not directly. Indeed, the reason for the decline to appear in the incidence of unintentional injuries only remains unclear. It may derive from many practical steps, e.g. locked firearm cases, or from the stability of the community itself. For effective reduction of firearm-related injuries, preventive measures, such as education and counseling, should be targeted at recreational firearm users. To sum up, this study showed that the often reported increasing trend in firearm as well as explosion-related injuries has not manifested in Finland. Consequently, it can be recognized that, overall, the Finnish legislation together with the various strategies have succeeded in preventing firearm- and explosion-related injuries in the country.

Accurate Parameter Estimation of Contaminant Transport Inverse Problem using Particle Swarm Optimization

Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

A Combinatorial Optimization Problem for High Order PODs with Few Sensors

Experimental characterization of high dimensional dynamic systems sometimes uses the proper orthogonal decomposition (POD). If there are many measurement locations and relatively fewer sensors, then steady-state behavior can still be studied by sequentially taking several sets of simultaneous measurements. The number required of such sets of measurements can be minimized if we solve a combinatorial optimization problem. We aim to bring this problem to the attention of engineering audiences, summarize some known mathematical results about this problem, and present a heuristic (suboptimal) calculation that gives reasonable, if not stellar, results.

Understanding who cares: Creating the evidence to address the long-standing policy problem of staff shortages in early childhood education and care

Evidence-based policy is a means of ensuring that policy is informed by more than ideology or expedience. However, what constitutes robust evidence is highly contested. In this paper, we argue policy must draw on quantitative and qualitative data. We do this in relation to a long entrenched problem in Australian early childhood education and care (ECEC) workforce policy. A critical shortage of qualified staff threatens the attainment of broader child and family policy objectives linked to the provision of ECEC and has not been successfully addressed by initiatives to date. We establish some of the limitations of existing quantitative data sets and consider the potential of qualitative studies to inform ECEC workforce policy. The adoption of both quantitative and qualitative methods is needed to illuminate the complex nature of the work undertaken by early childhood educators, as well as the environmental factors that sustain job satisfaction in a demanding and poorly understood working environment.