Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa

Joint System Prognostics For Increased Efficiency And Risk Mitigation In Advanced Nuclear Reactor Instrumentation and Control

The science of prognostics is analogous to a doctor who, based on a set of symptoms and patient tests, assesses a probable cause, the risk to the patient, and a course of action for recovery. While traditional prognostics research has focused on the aspect of hydraulic and mechanical systems and associated failures, this project will take a joint view in focusing not only on the digital I&C aspect of reliability and risk, but also on the risks associated with the human element. Model development will not only include an approximation of the control system physical degradation but also on human performance degradation. Thus the goal of the prognostic system is to evaluate control room operation; to identify and potentially take action when performance degradation reduces plant efficiency, reliability or safety

A Study Of UV-curable Offset Ink Emulsified With An Alternative Isopropyl Alcohol-free Fountain Solution

In the present research, fountain solution without isopropyl alcohol (IPA) for Ultraviolet offset curing ink (UV ink) was prepared by using Ethylene Glycol Mono-butyl Ether (EGME) as a substitute for IPA. The effect of EGME concentration on the water pick-up characteristics, tack value, rheological behaviors, and curing time of UV offset inks was investigated. Water pick-up characteristics, tack value and rheological behaviors were measured by Duke Ink water emulsification tester, Tack-o-scope and cone-plate rheometer, respectively. The curing time of the UV ink was evaluated by the rub test of printed sheet samples proofed on the polymer film at the standard solid ink density and the same ink thickness. The results revealed that an increase in EGME concentration increased the water pick-up characteristics of the UV ink. There was no significant influence of EGME on the tack value of UV inks. However, the tack value of UV ink was significantly affected by fountain concentration in UV inks and UV ink color. The addition of EGME reduced the dynamic viscosity and thixotropic property of UV inks but did not change the flow behavior of UV ink as shear thinning. This study indicates that UV ink emulsified with a higher EGME concentration fountain solution needs a longer curing time. The cyan UV ink has the longest curing time. Finally, the fountain solution of 10% EGME exhibited good performance in water pick-up characteristics, tack value, rheological behaviors, and curing time of UV inks

\mbox{SU}(3)_L \otimes \mbox{U}(1)_N and \mbox{SU}(4)_L \otimes \mbox{U}(1)_N gauge models with right-handed neutrinos

Pisano and Pleitez have introduced an interesting \mbox{SU}(3)_C \otimes \mbox{SU}(3)_L \otimes \mbox{U}(1)_N gauge model which has the property that gauge anomaly cancellation requires the number of generations to be a multiple of 3. We consider generalizing that model to incorporate right-handed neutrinos. We find that there exists a non-trivial generalization of the Pisano-Pleitez model with right-handed neutrinos which is actually simpler than the original model in that symmetry breaking can be achieved with just three \mbox{SU}(3)_L triplets (rather than 3 \mbox{SU}(3)_L triplets and a sextet). We also consider a gauge model based on \mbox{SU}(3)_C\otimes \mbox{SU}(4)_L \otimes \mbox{U}(1)_N symmetry. Both of these new models also have the feature that the anomalies cancel only when the number of generations is divisible by 3.Comment: 8, McGill/94-1

EXPLORING THE POTENTIAL OF SHORT-TIME FOURIER TRANSFORMS FOR ANALYZING SKIN CONDUCTANCE AND PUPILLOMETRY IN REAL-TIME APPLICATIONS

The development of real-time predictors of mental workload is critical for the practical application of augmented cognition to human-machine systems. This paper explores a novel method based on a short-time Fourier transform (STFT) for analyzing galvanic skin conductance (SC) and pupillometry time-series data to extract estimates of mental workload with temporal bandwidth high-enough to be useful for augmented cognition applications. We tested the method in the context of a process control task based on the DURESS simulation developed by Vincente and Pawlak (1994; ported to Java by Cosentino,& Ross, 1999). SC, pupil dilation, blink rate, and visual scanning patterns were measured for four participants actively engaged in controlling the simulation. Fault events were introduced that required participants to diagnose errors and make control adjustments to keep the simulator operating within a target range. We were interested in whether the STFT of these measures would produce visible effects of the increase in mental workload and stress associated with these events. Graphical exploratory data analysis of the STFT showed visible increases in the power spectrum across a range of frequencies directly following fault events. We believe this approach shows potential as a relatively unobtrusive, low-cost, high bandwidth measure of mental workload that could be particularly useful for the application of augmented cognition to human-machine systems

Human Factors Issues For Multi-Modular Reactor Units

Smaller and multi-modular reactor (MMR) will be highly technologically-advanced systems allowing more system flexibility to reactors configurations (e.g., addition/deletion of reactor units). While the technical and financial advantages of systems may be numerous, MMR presents many human factors challenges that may pose vulnerability to plant safety. An important human factors challenge in MMR operation and performance is the monitoring of data from multiple plants from centralized control rooms where human operators are responsible for interpreting, assessing, and responding to different system’s states and failures (e.g., simultaneously monitoring refueling at one plant while keeping an eye on another plant’s normal operating state). Furthermore, the operational, safety, and performance requirements for MMR can seriously change current staffing models and roles, the mode in which information is displayed, procedures and training to support and guide operators, and risk analysis. For these reasons, addressing human factors concerns in MMR are essential in reducing plant risk