Trade-off Curve Research Articles

Artificial water channels enable fast and selective water permeation through water-wire networks.

Artificial water channels are synthetic molecules that aim to mimic the structural and functional features of biological water channels (aquaporins). Here we report on a cluster-forming organic nanoarchitecture, peptide-appended hybrid[4]arene (PAH[4]), as a new class of artificial water channels. Fluorescence experiments and simulations demonstrated that PAH[4]s can form, through lateral diffusion, clusters in lipid membranes that provide synergistic membrane-spanning paths for a rapid and selective water permeation through water-wire networks. Quantitative transport studies revealed that PAH[4]s can transport >109 water molecules per second per molecule, which is comparable to aquaporin water channels. The performance of these channels exceeds the upper bound limit of current desalination membranes by a factor of ~104, as illustrated by the water/NaCl permeability-selectivity trade-off curve. PAH[4]'s unique properties of a high water/solute permselectivity via cooperative water-wire formation could usher in an alternative design paradigm for permeable membrane materials in separations, energy production and barrier applications.

Adaptive battery aware power management of a computer with self power-managed components

Dynamic power management strategies are generally used to achieve efficient power consumption of battery operated computer systems. Such computer systems usually integrate a number of built-in power-management policies. These policies are generally integrated into device drivers and cannot be changed. This paper addresses the problem of adaptive dynamic power management of a battery operated computer with self-power managed components. The power management task is split into Component Power Manager (CPM) and Global Power Manager (GPM). The CPM is the local-level policy that is pre-defined and can't change. The GPM cannot overwrite the CPM policy. A Service Flow Controller (SFC) is incorporated to control the service request generation for a specific component. The GPM uses model free reinforcement learning to adequately guide SFC actions. Moreover, the GPM implements Reinforcement learning based battery power management aiming at optimizing the battery's State of Charge (SoC) and improving its lifetime. This is performed by letting the GPM adapt the system quality of services to the actual battery SoC. Experiments on measured data traces confirmed the effectiveness of the proposed approach. Up to 57.2% of maximum SoC savings are obtained while good performance levels are maintained. Compared to prior reference studies, the proposed approach is model free, event driven, adapts to non-stationary workloads, considers multiple types of user applications, models the battery nonlinear characteristics, can handle SoC degradation and performance at the same time, and is capable to achieve deep and wide SoC degradation/performance tradeoff curves.

A Deep Study on Layered Multi-Relay Non-Orthogonal Amplify-Forward Networks

The paper introduces a cross-layer design for transmission of Successive Refinement (SR) source code interplayed with non-orthogonal layered code, deployed over a half-duplex multi-relay Non-orthogonal Amplify-Forward (NAF) network. Assuming the Channel State Information (CSI) is not available at the source node, firstly the achievable layered Diversity-Multiplexing Tradeoff (DMT) curve is derived. Then, by taking Distortion Exponent (DE) as the figure of merit, several achievable lower bounds are proved and the optimal expected distortion performance under high Signal to Noise Ratio (SNR) approximation is explicitly obtained. It is shown that the proposed coding can achieve the Multi-Input Single-Output (MISO) upper bound under certain regions of bandwidth ratios, by which the optimal performance in these regions can be explicitly characterized. Further the non-orthogonal layered coding scheme is extended to a multi-hop MIMO Decode-Forward (DF) relay network and set of DE lower bounds is derived. Simulation results confirm the effectiveness of the proposed scheme under general SNR values and compared with its counterparts.

Performance enhancement of the machine‐fault diagnosis system using feature mapping, normalisation and decision fusion

A unified fault modelling approach for machines with varying operating speeds is of interest in automating production facilities. Building a unified fault model is challenging due to the presence of speed-specific attributes in the features derived. In this work, it is shown how feature selection, mapping, normalisation and fusion of heterogeneous systems can help enhance the performance of speed-independent (SI) machine-fault diagnosis systems. Statistical features, obtained after applying feature selection, are used as input to a support vector machine (SVM) back-end classifier as the baseline system. Entropy-based feature selection algorithm is proposed to improve the performance of the fault diagnosis system. Furthermore, to make the fault diagnosis system independent of speed, locality constrained linear coding (LLC), Fisher vector encoding (FVE) and mean and variance normalisation (MVN) are used. The LLC-MVN system and FVE-MVN system map the input features in terms of SI basis vectors to make the features robust to speed-specific variations. Finally, the decision scores of the time-domain LLC-MVN-SVM, frequency-domain LLC-MVN-SVM systems and variational mode decomposition-based FVE-MVN-SVM system were fused with appropriate weighting factors. The detection error trade-off curve is also used as a performance measure for intelligent fault diagnosis systems.

Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication.

Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.

Standardizing pharmaceutical delivery to reduce pharmacy costs while simultaneously reducing missing doses

During a patient’s hospital visit, the hospital pharmacy strives to provide service with minimal missing doses or medication errors. In order to decrease the number of missing doses and medication errors that can occur, pharmacies can use either a robot within the central pharmacy or an automated dispensing cabinet (ADC) on the unit that dispenses individual doses for patients. While the robot and the ADC are more effective than a pharmacy technician, they can be costly to implement. We create a linear model which captures the key aspects of the medication delivery system, both from the pharmacy perspective and the nursing unit perspective. The linear program determines the delivery method for each medication, dose type, and unit in order to minimize the cost to deliver the medication to the patient for a given missing dose rate. Solving this linear program for different missing dose rates produces the tradeoff curve between the minimum cost of delivery and the average number of missing doses. We then create a heuristic to determine the delivery method based on a ratio comparing the delivery cost increase to the missing dose reduction.

Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation.

As artificial intelligence (AI)- or deep-learning-based technologies become more popular, the main research interest in the field is not only on their accuracy, but also their efficiency, e.g., the ability to give immediate results on the users’ inputs. To achieve this, there have been many attempts to embed deep learning technology on intelligent sensors. However, there are still many obstacles in embedding a deep network in sensors with limited resources. Most importantly, there is an apparent trade-off between the complexity of a network and its processing time, and finding a structure with a better trade-off curve is vital for successful applications in intelligent sensors. In this paper, we propose two strategies for designing a compact deep network that maintains the required level of performance even after minimizing the computations. The first strategy is to automatically determine the number of parameters of a network by utilizing group sparsity and knowledge distillation (KD) in the training process. By doing so, KD can compensate for the possible losses in accuracy caused by enforcing sparsity. Nevertheless, a problem in applying the first strategy is the unclarity in determining the balance between the accuracy improvement due to KD and the parameter reduction by sparse regularization. To handle this balancing problem, we propose a second strategy: a feedback control mechanism based on the proportional control theory. The feedback control logic determines the amount of emphasis to be put on network sparsity during training and is controlled based on the comparative accuracy losses of the teacher and student models in the training. A surprising fact here is that this control scheme not only determines an appropriate trade-off point, but also improves the trade-off curve itself. The results of experiments on CIFAR-10, CIFAR-100, and ImageNet32 × 32 datasets show that the proposed method is effective in building a compact network while preventing performance degradation due to sparsity regularization much better than other baselines.

Exponential Separations in the Energy Complexity of Leader Election

Energy is often the most constrained resource for battery-powered wireless devices, and most of the energy is often spent on transceiver usage (i.e., transmitting and receiving packets) rather than computation. In this article, we study the energy complexity of fundamental problems in several models of wireless radio networks. It turns out that energy complexity is very sensitive to whether the devices can generate random bits and their ability to detect collisions . We consider four collision detection models: Strong-CD (in which transmitters and listeners detect collisions), Sender-CD (in which only transmitters detect collisions), Receiver-CD (in which only listeners detect collisions), and No-CD (in which no one detects collisions). The take-away message of our results is quite surprising. For randomized algorithms, there is an exponential gap between the energy complexity of Sender-CD and Receiver-CD: Randomized: No-CD = Sender-CD > Receiver-CD = Strong-CD and for deterministic algorithms, there is another exponential gap in energy complexity, but in the reverse direction : Deterministic: No-CD = Receiver-CD > Sender-CD = Strong-CD Precisely, the randomized energy complexity of Leader Election is Θ(log * n ) in Sender-CD but Θ(log(log * n )) in Receiver-CD, where n is the number of devices, which is unknown to the devices at the beginning; the deterministic complexity of Leader Election is Θ(log N ) in Receiver-CD but Θ(log log N ) in Sender-CD, where N is the size of the ID space. There is a tradeoff between time and energy. We provide a new upper bound on the time-energy tradeoff curve for randomized algorithms. A critical component of this algorithm is a new deterministic Leader Election algorithm for dense instances, when n = Θ( N ), with inverse Ackermann energy complexity.

On Secure Exact-Repair Regenerating Codes With a Single Pareto Optimal Point

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of $\ell $ nodes. An $(n,k,d,\ell )$ secure exact-repair regenerating code is an $(n,k,d)$ exact-repair regenerating code that is secure under this eavesdropping model. It has been shown that for some parameters $(n,k,d,\ell )$ , the associated optimal storage-bandwidth tradeoff curve, which has one corner point, can be determined. The focus of this paper is on characterizing such parameters. We establish a lower bound $\hat {\ell }$ on the number of wiretap nodes, and show that this bound is tight for the case $k = d = n-1$ .

A game theoretical low impact development optimization model for urban storm water management

Abstract This study presents a novel framework to optimize Low Impact Development (LID) practices for urban storm water management. First, the Storm Water Management Model (SWMM) model was executed for different possible scenarios of input parameters and various LIDs to simulate runoff volume, Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) loads. Next, a neural network (MLP-ANN) was trained and validated, as a surrogate model, against the set of inputs and output variables from the SWMM model simulations. The inherent uncertainties in the rainfall-runoff modeling were accounted for using a Nonlinear Interval Number Programming (NINP) model, and stakeholders' interactions are considered using a leader-follower game model. Velenjak urban watershed in Tehran, Iran, with four key stakeholders was considered as the study area. Tehran Municipality is the financial service provider that makes the first decision the leader-follower structure, and is considered as the leader with the priority of minimizing LIDs’ construction and maintenance costs. Tehran Department of Environmental Protection, Tehran Regional Water company, and Tehran Province Water and Wastewater company are the main followers in the decision making structure in the study area. This game theoretical framework yields several Pareto optimal solutions given the conflicting utilities of various players, and a Multi Criteria Decision Making procedure – i.e. PROMETHEE model – selects the most preferred compromised option. Fourteen weighting scenarios were considered in the PROMETHEE model to determine the compromise solution among the 52 solutions on the trade-off curve. The novelty of this study lies in using a nonlinear interval conflict resolution multi-objective optimization model for urban storm water management based on a leader-follower game. The proposed methodology warrants BOD and TSS loads, as well as storm water volume, are all reduced, with the highest reductions of 93, 86 and 90 percent, respectively. Results testify to the efficacy of the proposed model for urban storm water management.

The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory.

Queuing theory can and has been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper, we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and 'treatment' rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision-making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool, reducing variation should be considered.

Rack-Aware Regenerating Codes for Data Centers

Erasure coding is widely used for massive storage in data centers to achieve high fault tolerance and low storage redundancy. Since the cross-rack communication cost is often high, it is critical to design erasure codes that minimize the cross-rack repair bandwidth during failure repair. In this paper, we analyze the optimal trade-off between storage redundancy and cross-rack repair bandwidth specifically for data centers, subject to the condition that the original data can be reconstructed from a sufficient number of any non-failed nodes. We characterize the optimal trade-off curve under functional repair, and propose a general family of erasure codes called rack-aware regenerating codes (RRC), which achieve the optimal trade-off. We further propose exact repair constructions of RRC that have minimum storage redundancy and minimum cross-rack repair bandwidth, respectively. We show that (i) the minimum storage redundancy constructions support a wide range of parameters and have cross-rack repair bandwidth that is strictly less than that of the classical minimum storage regenerating codes in most cases, and (ii) the minimum cross-rack repair bandwidth constructions support all the parameters and have less cross-rack repair bandwidth than that of the minimum bandwidth regenerating codes for almost all of the parameters.

Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation

A challenging issue in optimal allocating water resources is uncertainty in parameters of a model. In this paper, a fuzzy multi-objective model was proposed to maximize the economic benefits of consumers and to optimize the allocation of surface and groundwater resources used for optimal cropping pattern. In the proposed model, three objective functions were optimizing farmer’s maximum net profit, groundwater stability and maximizing the reliability of water supply considering uncertainties in water resources and economic parameters in a basin. The optimal Pareto trade-off curves extracted using Non-dominated Sorting Genetic Algorithm- II. The best point on the Pareto trade-off curves was determined by using five decision-making approaches which combined by Breda aggregation method. Then, analyzing the credibility level of the optimization parameters and nonlinearity condition of objective functions revealed that by non-linearization of objective functions and increasing the fuzziness of the water demand and economic parameters, the model achieves more desirable values. Having been applied under uncertain conditions of objective functions and the input parameters, the results indicate an average increase of 17% and 54% in the allocation of agriculture and urban sectors, respectively. According to the annually optimal allocation results, the groundwater resources show higher sensitivity rather than surface water resources to the uncertainties in the parameters. Moreover, the optimal operation policies are more efficient than the deterministic model Consequently, the suggested model can facilitate optimizing water resources allocation policies, providing the optimal cropping pattern under uncertainty conditions, and can be used for the similar uncertain condition in other basins.

Reconciling modern machine-learning practice and the classical bias-variance trade-off.

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias-variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to avoid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double-descent" curve subsumes the textbook U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning.

Bi-Level Participatory Forest Management Planning Supported by Pareto Frontier Visualization

Abstract This research addresses the problem of forested landscape management planning in contexts characterized by multiple ecosystem services and multiple stakeholders. A new methodology for participatory landscape-level forest management is proposed. Specifically, a bilevel representation is used, whereas models of subsystems are used for constructing an integrated model of the master problem. Participatory workshops and interactive visualization of the Pareto frontier are used to support the solution of the multi-objective optimization upper- and lower-level problems. The visualization is implemented by a technique—Interactive Decision Maps—that displays interactively the Pareto frontier in the form of decision maps, that is, collections of the objectives’ tradeoff curves. Since the upper-level problem may be characterized by a large number of decision variables, we compare the Pareto frontier generated by the Interactive Decision Maps technique with the Pareto frontier generated by a decomposition approach that builds from the Pareto frontiers of the lower-level subproblems. The approach supports further the negotiation between upper- and lower-level goals. Results are discussed for a large-scale application in a forested landscape in northwest Portugal.

A fuzzy multi-objective optimization approach for treated wastewater allocation.

In face of the new climate and socio-environmental conditions, conventional sources of water are no longer reliable to supply all water demands. Different alternatives are proposed to augment the conventional sources, including treated wastewater. Optimal and objective allocation of treated wastewater to different stakeholders through an optimization process that takes into account multiple objectives of the system, unlike the conventional ground and surface water resources, has been widely unexplored. This paper proposes a methodology to allocate treated wastewater, while observing the physical constraints of the system. A multi-objective optimization model (MOM) is utilized herein to identify the optimal solutions on the pareto front curve satisfying different objective functions. Fuzzy transformation method (FTM) is utilized to develop different fuzzy scenarios that account for potential uncertainties of the system. Non-dominated sorting genetic algorithm II (NSGA-II) is then expanded to include the confidence level of fuzzy parameters, and thereby several trade-off curves between objective functions are generated. Subsequently, the best solution on each trade-off curve is specified with preference ranking organization method for enrichment evaluation (PROMETHEE). Sensitivity analysis of criteria's weights in the PROMETHEE method indicates thatthe results arehighly dependent on the weighting scenario, and hence weights should be carefully selected. We apply this framework to allocate projected treated wastewater in the planning horizon of 2031, which is expected to be produced by wastewater treatment plants in the eastern regions of Tehran province, Iran. Results revealed the efficiency of this methodology to obtain the most confident allocation strategy in the presence of uncertainties.

Optimising healthy and safe fish intake recommendations: a trade-off between personal preference and cost.

Individuals may perceive personalised dietary advice as more relevant and motivational than national guidelines. Personal preference and food cost are factors that can affect consumer decisions. The objective of this study was to present a method for modelling and analysing the trade-off between deviation from preference and food cost for optimised personalised dietary recommendations. Quadratic programming was applied to minimise deviation from fish preference and cost simultaneously with different weights on the cost for 3016 Danish adults (whose dietary intake and body weight were recorded in a national dietary survey). Model constraints included recommendations for EPA, DHA and vitamin D and tolerable levels for methyl mercury, dioxins and dioxin-like polychlorinated biphenyls. When only minimising deviation from preference, 50 % of the study population should be recommended to increase fish intake, 48 % should be suggested to maintain current consumption and 2 % should be suggested to decrease fish consumption. When only minimising cost, the vast majority (99 %) should be recommended to only consume herring, which is the least-expensive fish species. By minimising deviation from preference and cost simultaneously with different weights on the cost, personalised optimal trade-off curves between deviation from fish intake preference and fish cost could be generated for each individual in our study population, except for twenty-two individuals (0·7 %) whose contaminant background exposure was too high. In the future, the method of this paper could be applied in the personal communication of healthy and safe food recommendations that fit the preferences of individual consumers.

Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model.

High-SNR Channel Capacity for Communication Over Radar Waveforms

We apply recent theory in the high-SNR estimation of channel capacity to study how much information may be encoded in radar pulse waveforms. Waveforms are constrained to those which admit a high-gain, low-sidelobe pulse compression filter. Using simple numerical methods, we compute the high-SNR capacity of a Gaussian channel using such a waveform alphabet. The resulting tradeoff curves indicate a notably favorable theoretical tradeoff space between signal processing performance and high-SNR channel capacity.

An Example of Multi-objective Optimization for Dynamic Processes

Recent research activity has shown an interest in multi-objective optimization of dynamic processes, both for design and for real-time control; see Liu et al. (2018), Valderrama & Ruiz (2018), and de Sousa Santos et al. (2018) for some recent examples. Many of the methods proposed for these problems are based on converting the multi-objective problem into a single objective through the use of weights. Some works, for instance, Liu et al. (2018), however, consider the multi-objective problem directly but even in these cases, the problem is treated as a single objective problem with other objectives incorporated as constraints. All of these methods may not necessarily generate a satisfactory trade-off or Pareto optimal curve when this trade-off curve is non-convex. In this paper, we illustrate the use of a plant propagation nature inspired algorithm for the solution of design and control problems for dynamic processes. Being population based, it is able to identify an approximation to the Pareto optimal frontier simultaneously. A simple example from the literature is used to demonstrate how this problem can be formulated. The open source, freely available, Fresa application (http://www.ucl.ac.uk/~ucecesf/fresa.html),written in Julia, is used and the discussion concentrates on the problem formulation. The result shows how a multi-objective approach may lead to better insight into the design or control issues. The illustrated problem was originally formulated as a single objective problem. In doing so, information about alternative designs and how these could affect the performance of the system may be hidden. A multi-objective formulation allows the engineer to make better informed design decisions.