Uncertain Targets Research Articles

Challenges in the Development of Therapy for Dry Age-Related Macular Degeneration.

Dry age-related macular degeneration (AMD), a multifactorial progressive degenerative disease of the retinal photoreceptors, pigmented epithelium and Bruch's membrane/choroid in central retina, causes visual impairment in millions of elderly people worldwide. The only available therapy for this disease is the over-the-counter (OTC) multi-vitamins plus macular xanthophyll (lutein/zeaxanthin) which attempts to block the damages of oxidative stress and ionizing blue light. Therefore development of dry AMD prescribed treatment is a pressing unmet medical need. However, this effort is currently hindered by many challenges, including an incomplete understanding of the mechanism of pathogenesis that leads to uncertain targets, confounded by not yet validated preclinical models and the difficulty to deliver the drugs to the posterior segment of the eye. Additionally, with slow disease progression and a less than ideal endpoint measurement method, clinical trials are necessarily large, lengthy and expensive. Increased commitment to research and development is an essential foundation for dealing with these problems. Innovations in clinical trials with novel endpoints, nontraditional study designs and the use of surrogate diseases might shorten the study time, reduce the patient sample size and consequently lower the budget for the development of the new therapies for the dry AMD.

Dark matter annihilation and decay in dwarf spheroidal galaxies: the classical and ultrafaint dSphs

Dwarf spheroidal (dSph) galaxies are prime targets for present and future gamma-ray telescopes hunting for indirect signals of particle dark matter. The interpretation of the data requires careful assessment of their dark matter content in order to derive robust constraints on candidate relic particles. Here, we use an optimised spherical Jeans analysis to reconstruct the `astrophysical factor' for both annihilating and decaying dark matter in 21 known dSphs. Improvements with respect to previous works are: (i) the use of more flexible luminosity and anisotropy profiles to minimise biases, (ii) the use of weak priors tailored on extensive sets of contamination-free mock data to improve the confidence intervals, (iii) systematic cross-checks of binned and unbinned analyses on mock and real data, and (iv) the use of mock data including stellar contamination to test the impact on reconstructed signals. Our analysis provides updated values for the dark matter content of 8 `classical' and 13 `ultrafaint' dSphs, with the quoted uncertainties directly linked to the sample size; the more flexible parametrisation we use results in changes compared to previous calculations. This translates into our ranking of potentially-brightest and most robust targets---viz., Ursa Minor, Draco, Sculptor---, and of the more promising, but uncertain targets---viz., Ursa Major 2, Coma---for annihilating dark matter. Our analysis of Segue 1 is extremely sensitive to whether we include or exclude a few marginal member stars, making this target one of the most uncertain. Our analysis illustrates challenges that will need to be addressed when inferring the dark matter content of new `ultrafaint' satellites that are beginning to be discovered in southern sky surveys.

Optimal Motion Planning for Searching for Uncertain Targets

This paper explores the potential for applying newly available numerical methods in optimal control to solve motion planning problems created by the search for targets with motion uncertainty characterized by constant but unknown parameters. These recent developments enable the efficient computation of numerical solutions for search problems with multiple searchers, nonlinear dynamics, and a broad class of objectives. We demonstrate the efficacy of these methods through implementing a multi-agent optimal search problem. We then derive an expansion of the optimal search modeling framework which facilitates the consideration of multi-agent searching problems with more general strategic objectives and utilize this expanded framework to implement an example combat defense scenario.

Project Management Decisions with Uncertain Targets

Project management decision rules presume that fixed and inflexible targets have been defined for the project. If a project’s slack is defined as the difference between actual project performance and these targets, then these decision rules can be characterized as maximizing the probability that slack is nonnegative (i.e., maximizing the probability of meeting the targets). These rules rely on z-scores to compare uncertain performance to target levels. Following these decision rules will not always suffice for the project manager to act consistently with customer preferences. In particular, actual requirements may be uncertain or subject to change, and customers may have some flexibility. A decision-analytic approach accounting for these factors can allow the project manager to maximize the customer’s expected utility. We redefine project slack to reflect the difference between performance and a random target that reflects both the customer’s risk tolerance and uncertainty about the actual requirement. The z-score associated with this slack is shown to be proportional to the certainty equivalent for a project. Thus utility-maximizing decision rules in the language of decision analysis can be readily translated into z-score-maximizing decision rules in the language of project management. From this, we discuss how related decision-analytic concepts such as value of information might be applied to families of problems in project management.

Challenges in the development of glaucoma neuroprotection therapy

Glaucoma, a disease of the optic nerve and retina, causes blindness in millions of people worldwide. Currently available therapies for this disease only attempt to reduce intraocular pressure, the major risk factor, without addressing the associated optic neuropathy and retinopathy. Development of glaucoma neuroprotective treatment is therefore a pressing unmet medical need. Unfortunately, many challenges hinder this effort, including an incomplete understanding of the mechanism of pathogenesis, leading to uncertain therapeutic targets and confounded by not yet validated preclinical models. Most importantly, with slow disease progression and a less than ideal endpoint measurement method, clinical trials are necessarily large, lengthy, expensive and, to many, prohibitive. No easy solution is available to overcome these challenges. Increased commitment to basic mechanistic research is an essential foundation for dealing with this problem. Innovations in clinical trials with novel surrogate endpoints, nontraditional study designs and the use of surrogate diseases might shorten the study time, reduce the patient sample size and consequently lower the budgetary hurdle for the development of new therapies.

The Consequences of Uncertain Debt Targets

Several proposals to reduce U.S. debt reveal large differences in their targets. We examine how an unknown debt target affects economic activity using a real business cycle model in which Bayesian households learn about a state-dependent debt target in an endogenous tax rule. Recent papers use stochastic volatility shocks to study fiscal uncertainty. In our setup, the fiscal rule is time-varying due to unknown changes in the debt target. Households infer the current debt target from a noisy tax rule and jointly estimate the transition probabilities. Three key findings emerge from our analysis: (1) Limited information about the debt target amplifies the effect of tax shocks through changes in expected tax rates; (2) The welfare losses are an order of magnitude larger when both the debt target state and transition matrix are unknown than when only the debt target state is unknown to households; (3) An unknown debt target likely reduced the stimulative effect of the ARRA and uncertainty about the sunset provision in the Bush tax cuts may have slowed the recovery and led to welfare losses.

Cancer prognosis using support vector regression in imaging modality

The proposed techniques investigate the strength of support vector regression (SVR) in cancer prognosis using imaging features. Cancer image features were extracted from patients and recorded into censored data. To employ censored data for prognosis, SVR methods are needed to be adapted to uncertain targets. The effectiveness of two principle breast features, tumor size and lymph node status, was demonstrated by the combination of sampling and feature selection methods. In sampling, breast data were stratified according to tumor size and lymph node status. Three types of feature selection methods comprised of no selection, individual feature selection, and feature subset forward selection, were employed. The prognosis results were evaluated by comparative study using the following performance metrics: concordance index (CI) and Brier score (BS). Cox regression was employed to compare the results. The support vector regression method (SVCR) performs similarly to Cox regression in three feature selection methods and better than Cox regression in non-feature selection methods measured by CI and BS. Feature selection methods can improve the performance of Cox regression measured by CI. Among all cross validation results, stratified sampling of tumor size achieves the best regression results for both feature selection and non-feature selection methods. The SVCR regression results, perform better than Cox regression when the techniques are used with either CI or BS. The best CI value in the validation results is 0.6845. The best CI value corresponds to the best BS value 0.2065, which were obtained in the combination of SVCR, individual feature selection, and stratified sampling of the number of positive lymph nodes. In addition, we also observe that SVCR performs more consistently than Cox regression in all prognosis studies. The feature selection method does not have a significant impact on the metric values, especially on CI. We conclude that the combinational methods of SVCR, feature selection, and sampling can improve cancer prognosis, but more significant features may further enhance cancer prognosis accuracy.

Delayed action and uncertain stabilisation targets. How much will the delay cost?

Despite the growing concern about actual on-going climate change, there is little consensus on the scale and timing of actions needed to stabilise the concentrations of greenhouse gases. Many countries are unwilling to implement mitigation strategies, at least in the short term, and no agreement on an ambitious global stabilisation target has yet been reached. It is thus likely that international climate policies will be characterized by a high degree of uncertainty over the stringency of the climate objective, and that some countries might delay their participation to global action. What additional economic costs will this delay in the adoption of mitigation measures imply? What would the optimal short-term strategy be given the uncertainty surrounding the climate policy to come? Is there a hedging strategy that decision makers can adopt to cope with delayed action and uncertain targets? This paper addresses these questions by quantifying the economic implications of delaying mitigation action, and by computing the optimal abatement strategy in the presence of uncertainty about a global stabilisation target (which will be agreed upon in future climate negotiations). Results point to short-term inaction as the key determinant for the economic costs of ambitious climate policies. They also indicate that there is an effective hedging strategy that could minimise the cost of climate policy uncertainty over the global stabilisation target: a short-term moderate climate policy would be a good strategy to reduce the costs of delayed action and to cope with uncertainty about the outcome of future climate negotiations. By contrast, failing to curb emissions in the short term imposes rapidly increasing additional costs of compliance.

Does hand dominance affect the use of motor abundance when reaching to uncertain targets?

This study investigated hemispheric differences in utilizing motor abundance to achieve flexible patterns of joint coordination when reaching to uncertain target locations. Right-handed participants reached with each arm to the same central target when its final location was certain or when there was a 66% probability that its location could change after movement initiation. Use of greater motor abundance was observed when participants reached to the central target under target location uncertainty regardless of the arm used to reach. Joint variance associated with variability of movement direction was larger when reaching with the left, non-dominant arm. This arm also exhibited higher hand path variability compared to the dominant arm. These arm differences were not found when the final (central) target location was known in advance. The results provide preliminary evidence for a greater ability of the dominant (right) arm/left hemisphere to decouple directions in joint space. That is, to increase the use of motor abundance without simultaneously inducing unwanted hand path variability requires that joint variations be restricted to a limited subspace of joint space. Hemispheric differences in motor planning did not appear to account for arm differences related to the use of motor abundance.

Delayed Action and Uncertain Targets - How Much Will Climate Policy Cost?

Despite the growing concern about actual on-going climate change, there is little consensus about the scale and timing of actions needed to stabilise the concentrations of greenhouse gases. Many countries are unwilling to implement effective mitigation strategies, at least in the short-term, and no agreement on an ambitious global stabilisation target has yet been reached. It is thus likely that some, if not all countries, will delay the adoption of effective climate policies. This delay will affect the cost of future policy measures that will be required to abate an even larger amount of emissions. What additional economic cost of mitigation measures will this delay imply? At the same time, the uncertainty surrounding the global stabilisation target to be achieved crucially affects short-term investment and policy decisions. What will this uncertainty cost? Is there a hedging strategy that decision makers can adopt to cope with delayed action and uncertain targets? This paper addresses these questions by quantifying the economic implications of delayed mitigation action, and by computing the optimal abatement strategy in the presence of uncertainty about a global stabilisation target (which will be agreed upon in future climate negotiations). Results point to short-term inaction as the key determinant for the economic costs of ambitious climate policies. They also indicate that there is an effective hedging strategy that could minimise the cost of climate policy under uncertainty, and that a short-term moderate climate policy would be a good strategy to reduce the costs of delayed action and to cope with uncertainty about the outcome of future climate negotiations. By contrast, an insufficient short-term effort significantly increases the costs of compliance in the long-term.

Delayed Action and Uncertain Targets: How Much Will Climate Policy Cost?

Delayed Action and Uncertain Targets: How Much Will Climate Policy Cost?

Prekybos apyvartiniais taršos leidimais sistemos ekonominio efektyvumo didinimas: paskirstymo metodų įtaka

Prekybos apyvartiniais taršos leidimais tikslas yra mažiausiomis sąnaudomis sumažinti išmetamų šiltnamio dujų kiekį. Nors teorija teigia, kad apyvartinių taršos leidimų (ATL) paskirstymo metodai esant tobulai konkurencijai neturi įtakos prekybos apyvartiniais taršos leidimais sistemos efektyvumui, Europos Sąjungos (ES) prekybos ATL sistemos įgyvendinimas parodė paskirstymo metodų ir prekybos ATL sistemos efektyvumo ryšį. Straipsnyje aptariama istorinio ir aukciono metodų įtaka prekybos ATL sistemos efektyvumui bei analizuojami santykinių taršos rodiklių taikymo būdai. Be to, lyginami ES nacionaliniuose ATL paskirstymo planuose naudoti metodai bei analizuojama galima ATL paklausa rinkoje. Pagrindinis straipsnio tikslas - išanalizuoti ATL paskirstymo metodus, įvardyti diskutuotinus klausimus ir rekomenduoti, kaip padidinti ES prekybos ATL sistemos efektyvumą.

Stochastic saliency-based search model for search asymmetry with uncertain targets

Whereas some models with mechanism coding task-related information, e.g. task-related saliency map, can explain why search asymmetry occurs in some visual search tasks where the target is preliminarily defined, existing models cannot clearly explain human behavior in singleton search tasks showing search asymmetry. This study shows that the saliency-based search model can explain search asymmetry in singleton search without assuming task-related saliency map if we introduce two new essential ideas. The first improvement is about a competitive mechanism between feature maps, and the other is the introduction of a stochastic winner takes all (WTA) network. According to the modified model introducing these two modifications, search asymmetry in singleton search tasks results from an asymmetry in probability of the attentional focus being directed to the target. When the saliency difference is large, then attentional focus is almost always directed to the target first. On the other hand, when the saliency difference is relatively small, then the probability of the target receiving attentional focus first becomes relatively smaller. The stochastic saliency-based search model could reproduce human behavior in a range of visual search tasks showing search asymmetry, without assuming task-related saliency map.

Visual search asymmetry with uncertain targets.

The underlying mechanism of search asymmetry is still unknown. Many computational models postulate top-down selection of target-defining features as a crucial factor. This feature selection account implies, and other theories implicitly assume, that predefined target identity is necessary for search asymmetry. The authors tested the validity of the feature selection account using a singleton search task without a predefined target. Participants conducted a target-defined and a singleton search task with a circle (O) and a circle with a vertical bar (Q). Search asymmetry was observed in both tasks with almost identical magnitude. The results were not due to trial-by-trial feature selection, because search asymmetry persisted even when the target was completely unpredictable. Asymmetry in the singleton search was also observed with more complex stimuli, Kanji characters. These results suggest that feature selection is not necessary for search asymmetry, and they impose important constraints on current visual search theories.

Detection of noisy visual targets: models for the effects of spatial uncertainty and signal-to-noise ratio.

Brigham and Women’s Hospital, Boston, Massachusetts 02115 An “extreme-detector” model for detecting spatially uncertain targets in noisy backgrounds predicts how both detection and localization abilities are degraded by increasing the number of possible target locations. Experiments 1 and 2 show that the model accurately predicts detection and localization performance in tasks with two, four, and eight locations from d’ estimates of the observer’s ability to detect the target in a known spatial location. These predictions can be linked to the physical stimuli by combining the extreme-detector model with a “psychophysical” model that specifies how stimulus measures determine the target’s detectability in a given location. Single-parameter fits of four such combined models were compared with estimates of detection and localization performance in Experiment 3, which manipulated the target’s physical signal-to-noise ratio across various conditions of an eightlocation task.