On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Effects of transportation accessibility on residential property values: Application of spatial hedonic price model in Seoul, South Korea, metropolitan area

A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space—often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as systemwide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.

Adherence to fish oil intervention in patients with chronic kidney disease

Objective: With growing recognition of the role of inflammation in the development of chronic and acute disease, fish oil is increasingly used as a therapeutic agent, but the nature of the intervention may pose barriers to adherence in clinical populations. Our objective was to investigate the feasibility of using a fish oil supplement in hemodialysis patients. ---------- Design: This was a nonrandomized intervention study.---------- Setting: Eligible patients were recruited at the Hemodialysis Unit of Wesley Hospital, Brisbane, Queensland, Australia. Patients The sample included 28 maintenance hemodialysis patients out of 43 eligible patients in the unit. Exclusion criteria included patients regularly taking a fish oil supplement at baseline, receiving hemodialysis for less than 3 months, or being unable to give informed consent.---------- Intervention: Eicosapentaenoic acid (EPA) was administered at 2000 mg/day (4 capsules) for 12 weeks. Adherence was measured at baseline and weekly throughout the study according to changes in plasma EPA, and was further measured subjectively by self-report.---------- Results: Twenty patients (74%) adhered to the prescription based on changes in plasma EPA, whereas an additional two patients self-reported good adherence. There was a positive relationship between fish oil intake and change in plasma EPA. Most patients did not report problems with taking the fish oil. Using the baseline data, it was not possible to characterize adherent patients.---------- Conclusions: Despite potential barriers, including the need to take a large number of prescribed medications already, 74% of hemodialysis patients adhered to the intervention. This study demonstrated the feasibility of using fish oil in a clinical population.

Application of reinforcement learning in an open railway access market price negotiation

In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.

Solving navigational uncertainty using grid cells on robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.

The probability archive : from essence to uncertainty in the growth of knowledge

From a ‘cultural science’ perspective, this paper traces one aspect of a more general shift, from the realist representational regime of modernity to the productive DIY systems of the internet era. It argues that collecting and archiving is transformed by this change. Modern museums – and also broadcast television – were based on determinist or ‘essence’ theory; while internet archives like YouTube (and the internet as an archive) are based on ‘probability’ theory. The paper goes through the differences between modernist ‘essence’ and postmodern ‘probability’; starting from the obvious difference that in a museum each object is selected by experts for its intrinsic properties, while on the internet you don’t know what you will find. The status of individual objects is uncertain, although the productivity of the overall archive is unlimited. The paper links these differences with changes in contemporary culture – from a Newtonian to a quantum universe, progress to risk, institutional structure to evolutionary change, objectivity to uncertainty, identity to performance. Borrowing some of its methodology from science fiction, the paper uses examples from museums and online archives, ranging from the oldest stone tool in the world to the latest tribute vid on the net.

Environmental aspects and challenges of oilseed produced biodiesel in Southeast Asia

Research on alternative fuel for the vehemently growing number of automotivesis intensified due to environmental reasons rather than turmoil in energy price and supply. From the policy and steps to emphasis the use of biofuel by governments all around the world, this can be comprehended that biofuel have placed itself as a number one substitute for fossil fuels. These phenomena made Southeast Asia a prominent exporter of biodiesel. But thrust in biodiesel production from oilseeds of palm and Jatropha curcas in Malaysia, Indonesia and Thailand is seriously threatening environmental harmony. This paper focuses on this critical issue of biodiesels environmental impacts, policy, standardization of this region as well as on the emission of biodiesel in automotive uses. To draw a bottom line on feasibilities of different feedstock of biodiesel, a critical analysis on oilseed yield rate, land use, engine emissions and oxidation stability is reviewed. Palm oil based biodiesel is clearly ahead in all these aspects of feasibility, except in the case of NOx where it lags from conventional petro diesel.

A comparison of the 27-item and 12-item intolerance of uncertainty scales

The 27-item Intolerance of Uncertainty Scale (IUS) has become one of the most frequently used measure of Intolerance of Uncertainty. More recently, an abridged, 12-item version of the IUS has been developed. The current research used clinical (n = 50) and non-clinical (n = 56) samples to examine and compare the psychometric properties of both versions of the IUS. The two scales showed good internal consistency at both the total and subscale level and had satisfactory test-retest reliability. Both versions were correlated with worry and trait anxiety and had satisfactory concurrent validity. Significant differences between the scores of the clinical and non-clinical sample supported discriminant validity. Predictive validity was also supported for the two scales. Total scores, in the case of the clinical sample, and a subscale, in the case of the non-clinical sample, significantly predicted pathological worry and trait anxiety. Overall, the clinicians and researchers can use either version of the IUS with confidence, due to their sound psychometric properties.

Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Uncertainty and innovation in fashion design

Generally speaking, psychologists have suggested three traditional views of how people cope with uncertainty. They are the certainty maximiser, the intuitive statistician-economist and the knowledge seeker (Smithson, 2008). In times of uncertainty, such as the recent global financial crisis, these coping methods often result in innovation in industry. Richards (2003) identifies innovation as different from creativity in that innovation aims to transform and implement rather than simply explore and invent. An examination of the work of iconic fashion designers, through case study and situational analysis, reveals that coping with uncertainty manifests itself in ways that have resulted in innovations in design, marketing methods, production and consumption. In relation to contemporary fashion, where many garments look the same in style, colour, cut and fit (Finn, 2008), the concept of innovation is an important one. This paper explores the role of uncertainty as a driver of innovation in fashion design. A key aspect of seeking knowledge, as a mechanism to cope with this uncertainty, is a return to basics. This is a problem for contemporary fashion designers who are no longer necessarily makers and therefore do not engage with the basic materials and methods of garment construction. In many cases design in fashion has become digital, communicated to an unseen, unknown production team via scanned image and specification alone. The disconnection between the design and the making of garments, as a result of decades of off-shore manufacturing, has limited the opportunity for this return to basics. The authors argue that the role of the fashion designer has become about the final product and as a result there is a lack of innovation in the process of making: in the form, fit and function of fashion garments. They propose that ‘knowledge seeking’ as a result of uncertainty in the fashion industry, in particular through re-examination of the methods of making, could hold the key to a new era of innovation in fashion design.