Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification

We propose a novel, succinct, and effective approach to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$ in regression tasks. For predicting this conditional distribution, its quantiles of probability levels spreading the interval $(0,1)$ are boosted by additive models which are designed by us with intuitions and interpretability. We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$, while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e.g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization. This ensemble multi-quantiles approach called EMQ proposed by us is totally data-driven, and can gradually depart from Gaussian and discover the optimal conditional distribution in the boosting. On extensive regression tasks from UCI datasets, we show that EMQ achieves state-of-the-art performance comparing to many recent uncertainty quantification methods. Visualization results further illustrate the necessity and the merits of such an ensemble model

Dependency Grammar Based English Subject-Verb Agreement Evaluation

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Waiting Cost based Long-Run Network Investment Decision-making under Uncertainty

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and trigger a high asset risk. System operators have the incentive to postpone reinforcement, and &amp;#x2018;wait and see&amp;#x2019; whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties. The waiting cost is quantified by the options&amp;#x0027; cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty.</p

MiR-206-mediated dynamic mechanism of the mammalian circadian clock

<p>Abstract</p> <p>Background</p> <p>As a group of highly conserved small non-coding RNAs with a length of 21~23 nucleotides, microRNAs (miRNAs) regulate the gene expression post-transcriptionally by base pairing with the partial or full complementary sequences in target mRNAs, thus resulting in the repression of mRNA translation and the acceleration of mRNA degradation. Recent work has revealed that miRNAs are essential for the development and functioning of the skeletal muscles where they are. In particular, miR-206 has not only been identified as the only miRNA expressed in skeletal muscles, but also exhibited crucial roles in regulation of the muscle development. Although miRNAs are known to regulate various biological processes ranging from development to cancer, much less is known about their role in the dynamic regulation of the mammalian circadian clock.</p> <p>Results</p> <p>A detailed dynamic model of miR-206-mediated mammalian circadian clock system was developed presently by using Hill-type terms, Michaelis-Menten type and mass action kinetics. Based on a system-theoretic approach, the model accurately predicts both the periodicity and the entrainment of the circadian clock. It also explores the dynamics properties of the oscillations mediated by miR-206 by means of sensitivity analysis and alterations of parameters. Our results show that miR-206 is an important regulator of the circadian clock in skeletal muscle, and thus by study of miR-206 the main features of its mediation on the clock may be captured. Simulations of these processes display that the amplitude and frequency of the oscillation can be significantly altered through the miR-206-mediated control.</p> <p>Conclusions</p> <p>MiR-206 has a profound effect on the dynamic mechanism of the mammalian circadian clock, both by control of the amplitude and control or alteration of the frequency to affect the level of the gene expression and to interfere with the temporal sequence of the gene production or delivery. This undoubtedly uncovers a new mechanism for regulation of the circadian clock at a post-transcriptional level and provides important insights into the normal development as well as the pathological conditions of skeletal muscles, such as the aging, chronic disease and cancer.</p

Waiting Cost based Long-Run Network Investment Decision-making under Uncertainty

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and trigger a high asset risk. System operators have the incentive to postpone reinforcement, and &amp;#x2018;wait and see&amp;#x2019; whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties. The waiting cost is quantified by the options&amp;#x0027; cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty.</p

Influence of framing on medical decision making

Numerous studies have demonstrated the robustness of the framing effect in a variety of contexts, especially in medical decision making. Unfortunately, research is still inconsistent as to how so many variables impact framing effects in medical decision making. Additionally, much attention should be paid to the framing effect not only in hypothetical scenarios but also in clinical experience

Network Pricing with Investment Waiting Cost based on Real Options under Uncertainties

Existing capacity-based network pricing uses discounted cash flows to calculate costs, unable to reflect the uncertainties and flexibilities in distribution networks. Such shortcoming could distort the cost-reflectivity of pricing signals, particularly those for renewables and flexible technologies, causing more constraints and curtailment issues in networks. This paper proposes a new pricing method, Incremental Cost Network Pricing based on Real Options (ICOC), which can reflect network user uncertainties on network investment by using real options. Under this concept, network operators can delay investment for a certain period by paying waiting cost based on options value until more information is available, thus avoiding non-reversible investment due to uncertainties. The options cost will be levied on network users as i) rewards if they can provide flexibilities to the system; or ii) waiting costs if they present uncertainties to the system. The reward or cost is determined by a binomial tree pricing under a risk-neutral condition, which is added onto asset present value as the total cost to be recovered. Such cost is allocated to network users based on their nodal incremental costs. The proposed method is demonstrated on a practical network with different users, i) uncertain, ii) flexible; iii) certain and nonflexible.</p