Trade-off Curve Research Articles

An algorithmic framework for the generalized birthday problem

The generalized birthday problem (GBP) was introduced by Wagner in 2002 and has shown to have many applications in cryptanalysis. In its typical variant, we are given access to a function $$H:\{0,1\}^{\ell } \rightarrow \{0,1\}^n$$ (whose specification depends on the underlying problem) and an integer $$K>0$$ . The goal is to find K distinct inputs to H (denoted by $$\{x_i\}_{i=1}^{K}$$ ) such that $$\sum _{i=1}^{K}H(x_i) = 0$$ . Wagner’s K-tree algorithm solves the problem in time and memory complexities of about $$N^{1/(\lfloor \log K \rfloor + 1)}$$ (where $$N= 2^n$$ ). In this paper, we improve the best known GBP time-memory tradeoff curve (published independently by Nikolic and Sasaki and also by Biryukov and Khovratovich) for all $$K \ge 8$$ from $$T^2M^{\lfloor \log K \rfloor -1} = N$$ to $$T^{\lceil (\log K)/2 \rceil + 1 }M^{\lfloor (\log K)/2 \rfloor } = N$$ , applicable for a large range of parameters. We further consider values of K which are not powers of 2 and show that in many cases even more efficient time-memory tradeoff curves can be obtained. Finally, we optimize our techniques for several concrete GBP instances and show how to solve some of them with improved time and memory complexities compared to the state-of-the-art. Our results are obtained using a framework that combines several algorithmic techniques such as variants of the Schroeppel–Shamir algorithm for solving knapsack problems (devised in works by Howgrave-Graham and Joux and by Becker, Coron and Joux) and dissection algorithms (published by Dinur, Dunkelman, Keller and Shamir).

Engineering large end-to-end correlations in finite fermionic chains

We explore deformations of finite chains of independent fermions which give rise to large correlations between their extremes. After a detailed study of the Su-Schrieffer-Heeger (SSH) model, the trade-off curve between end-to-end correlations and the energy gap of the chains is obtained using machine-learning techniques, paying special attention to the scaling behavior with the chain length. We find that edge-dimerized chains, where the second and penultimate hoppings are reinforced, are very often close to the optimal configurations. Our results allow us to conjecture that, given a fixed gap, the maximal attainable correlation falls exponentially with the system size. Study of the entanglement entropy and contour of the optimal configurations suggest that the bulk entanglement pattern is minimally modified from the clean case.

Optimization of modular raft WEC-type attachment to VLFS and module connections for maximum reduction in hydroelastic response and wave energy production

This paper presents optimization of modular raft WEC-type attachment to VLFS and connections between floating modules of the VLFS for regular and irregular waves. The modular raft WEC-type attachment consists of multiple independent auxiliary pontoons connected to the fore edge of the VLFS with hinges and linear Power Take-Off (PTO) systems. The two objectives of the optimization are to maximize the reduction in hydroelastic response of VLFS and the power capture factor. The optimization process is proposed by using differential evolution (DE) algorithm. A numerical analysis framework based on finite element-boundary element (FE-BE) method is presented for determining the power capture factor as well as the hydroelastic response reduction for regular and irregular waves. The proposed optimization process is used to investigate the effects of the design variables, including PTO damping coefficient, pontoon length, the number of connections and connection stiffness, on the trade-off curve (i.e. Pareto front). In addition, the maximum power capture factor and hydroelastic response reduction are examined for various regular and irregular waves.

Multi-objective planning of energy storage technologies for a fully renewable system: Implications for the main stakeholders in Chile

Energy storage systems can cost-effectively balance fluctuations from renewable generation. Also, hydropower dams can provide flexibility, but often cause massive fluctuations in flow releases (hydropeaking), deteriorating the ecology of the downstream rivers. Expanding transmission infrastructure is another flexibility source but is frequently plagued by social opposition and delays. As the decision-making process transcends costs, we developed a multi-objective framework to design a fully renewable power system, such that the tradeoffs between total costs, hydropeaking, and new transmission projects can be assessed from a multi-stakeholder perspective. We planned the Chilean power system for the year 2050 and, based on the obtained trade-off curves (Pareto), we identified the following implications for the different stakeholders. Avoiding new transmission generates little costs (avoiding 30%/100% of transmission costs < 1%/ > 3%), which is positive for planners but negative for transmission companies. Severe hydropeaking can be mitigated for about 1% of additional costs if transmission is deployed. Avoiding both hydropeaking and transmission is the most extreme scenario, costing 11%. The less the transmission and hydropeaking, the more solar and storage technologies are installed. Cheap solar and storage systems enable policymakers to cost-effectively limit hydropeaking and new transmission, which makes the system greener and more socially acceptable.

Best Trade-Off Point Method for Efficient Resource Provisioning in Spark

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.

Process robustness and strength analysis of multi-layered dissimilar joints using ultrasonic metal welding

This paper investigates the effects of process parameters on the joint strength and process robustness when multi-layered joints of dissimilar metals are produced by ultrasonic metal welding (UMW). Three layers of 0.3-mm aluminium sheet are welded with a single 1.0-mm copper sheet which is representative of electric vehicle battery interconnects. A process robustness study in which welding pressure, amplitude of vibration and welding time are varied to produce satisfactory welds is reported. The weld quality is evaluated by performing lap shear and T-peel tests where maximum loads are considered as the quality indicator. Response surfaces are developed to identify the relationship and sensitivity between the input process parameters and output quality indicators. A feasible weldability zone is defined for the first time by identifying the under-weld, good-weld and over-weld conditions based on load-displacement curves and corresponding failure modes. Relying on the weldability zone and response surfaces, multi-objective optimisation is performed to obtain maximum lap shear and T-peel strength which resulted in Pareto frontier or trade-off curve between both objectives. An optimal joint is selected from the Pareto front which is verified and validated by performing confirmation experiments, and further, used for T-peel strength analysis of different interfaces of the multi-layered joint. To conclude, this paper determines both the optimal weld parameters and the robust operating range.

Time\u2013space complexity of quantum search algorithms in symmetric cryptanalysis: applying to AES and SHA-2

Performance of cryptanalytic quantum search algorithms is mainly inferred from query complexity which hides overhead induced by an implementation. To shed light on quantitative complexity analysis removing hidden factors, we provide a framework for estimating time–space complexity, with carefully accounting for characteristics of target cryptographic functions. Processor and circuit parallelization methods are taken into account, resulting in the time–space trade-off curves in terms of depth and qubit. The method guides how to rank different circuit designs in order of their efficiency. The framework is applied to representative cryptosystems NIST referred to as a guideline for security parameters, reassessing the security strengths of AES and SHA-2.

Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota.

The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.

Quantifying stakeholder understanding of an ecosystem service trade-off

Sustainable management of global natural resources is challenged by social and environmental drivers, adding pressure to ecosystem service provision in many regions of the world where there are competing demands on environmental resources. Understanding trade-offs between ecosystem services and how they are valued by different stakeholder groups is therefore critical to maximise benefits and avoid conflict between competing uses. In this study we developed a novel participatory trade-off experiment to elicit the perception of 43 participants, from across four key stakeholder groups, working in land and water management (Environmental Regulators, Farming Advisors, Water Industry Staff and Catchment Scientists). Using the Production Possibility Frontier (PPF) concept, we quantified stakeholder assessment of both the shape and the uncertainty around the PPF in a trade-off between agricultural intensity and the ecological health of freshwater systems. The majority of stakeholder groups selected threshold and logistic decay trade-off curves to describe the relationship of the trade-off, and estimated the uncertainty around the curves to be intermediate or large. The views of the four stakeholder groups differed significantly regarding how they estimated stakeholder trade-off prioritisation; the largest difference in perspectives was identified between Environmental Regulators and Farm Advisors. The methodology considered the cultural, socio-economic and institutional specificities of an ecosystem service interaction and identified potential sources of conflict but also possible solutions for win-win opportunities to explore and share understanding between stakeholders. Valuing stakeholder knowledge as a form of expert data and integrating this into participatory decision-making processes for land and water management thus contributes considerable value beyond traditional approaches to ecosystem service assessments.

Synthesis of Highly Gas-Permeable Polyimides of Intrinsic Microporosity Derived from 1,3,6,8-Tetramethyl-2,7-diaminotriptycene.

A simple synthetic route to a novel sterically hindered triptycene-based diamine, 1,3,6,8-tetramethyl-2,7-diaminotriptycene (TMDAT), and its use in the preparation of high molecular weight polyimides of intrinsic microporosity (PIM-PIs) are reported. The organosoluble TMDAT-derived polyimides displayed high Brunauer–Emmett–Teller surface areas ranging between 610 and 850 m2 g–1 and demonstrated excellent thermal stability of up to 510 °C. Introduction of the rigid three-dimensional paddlewheel triptycene framework and the tetramethyl-induced restriction of the imide bond rotation resulted in highly permeable polyimides with moderate gas-pair selectivity. The best performing polyimide made from TMDAT and a triptycene-based dianhydride showed gas transport properties located between the 2008 and 2015 polymer permeability/selectivity trade-off curves with H2 and O2 permeabilities of 2858 and 575 barrer combined with H2/N2 and O2/N2 selectivities of 24 and 4.8, respectively, after 200 days of physical aging.

Single-vendor multi-buyer supply chain coordination with stochastic demand

This paper proposes a model of single-vendor multi-buyer supply chain coordination with stochastic demand in which the vendor's production cycle length and the buyers' ordering cycle lengths are synchronized. A mathematical model for the coordination policy considering possible stock-out and/or excessive stock is formulated, and a solution algorithm for the model is developed. The numerical results show that, under stochastic situation, our coordination model performs better than two other policies, the decentralized decision making model and the common cycle coordination model. Considering the difficulty of estimating shortage cost, trade-off curves are plotted along a range of service levels to further compare the models and reinforce our results.

Control Performance Assessment for ILC-Controlled Batch Processes in a 2-D System Framework

In this paper, control performance assessment (CPA) is studied for batch processes controlled by iterative learning control (ILC). A 2-D linear quadratic Gaussian (LQG) benchmark is proposed to assess the performance of ILC in a 2-D framework. Based on the 2-D theory, an ILC-controlled batch process is first converted into a 2-D Roesser model. Subsequently, in order to assess the control performance of the converted 2-D system, the conventional LQG tradeoff curve is upgraded to the LQG performance assessment tradeoff surface. However, the complete knowledge of the system model is required to obtain the LQG tradeoff surface. For system without accurate model knowledge, a novel data-driven CPA method is further proposed. In this case, a novel 2-D closed-loop subspace identification method is proposed to identify the converted 2-D Roesser system. Based on the identified model, the LQG tradeoff surface can be obtained and utilized to assess the control performance. Overall, several simulation examples verified the feasibility and effectiveness of the proposed method.

VeriIntel2C: Abstracting RTL to C to maximize High-Level Synthesis Design Space Exploration

The design of integrated circuits (ICs) is typically done using low level Hardware Description Languages (HDLs) like Verilog or VHDL (Register Transfer Level). These enable the full controllability of the generated hardware design as they allow to specify the detailed behaviour and structure of the architecture, at every single clock cycle. The main drawback of using these low level HDLs is that takes very long time to create and verify large ICs with them. Moreover, it is hard to re-use HDL code for future projects that require changes in the micro-architecture. Thus, the industry is moving the level of abstraction to C-based VLSI design where designers only have to specify the functionality of the program and High-Level Synthesis (HLS) tools generate the HDL automatically. One additional benefit of C-based VLSI design is that it enables to explore the search space of possible micro-architectures from a single behavioral description. The result of a Design Space Exploration (DSE) is a trade-off curve of Pareto-optimal designs with unique area vs. performance metrics. Most VLSI design companies have large amounts of legacy HDL code. Thus, it makes sense to have an automatic flow to convert HDL designs into behavioral descriptions (e.g. C, C++ or SystemC) optimized for HLS DSE. This implies that the generation of explorable constructs, e.g. loops and arrays, which upon exploration, lead to very different micro-architectures (e.g. loops can be unrolled or folded, arrays can be mapped to RAMs or registers).In this paper, we propose a robust RTL to C translation method called VeriIntel2C to abstract RTL descriptions (written in Verilog) into ANSI-C descriptions optimized for HLS DSE by generating a large number of loops and arrays. Our method is able to generate these explorable constructs with the use of extended Hardware Petri Nets to extract the behaviour of the Verilog designs and to generate a Control Data Flow Graph (CDFG) that allows the easy identification of these constructs. From the experimental results, we are able to demonstrate that VeriIntel2C expands the design space considerably and also improves the quality of design space by 55% on average compared to previous work, on a wide range of designs.

MOS Amplifier Design Methodology for Optimum Performance

While trying to represent the performance metrics of a single-stage amplifier as a function of designable parameters, it is observed that the corresponding metrics form a polytope-type feasible region. Indeed, the polytope so formed is made up of performance metrics which can either be objectives or constraints. Initially, the simplex method is used to obtain an objective value that satisfies a set of constraints. Once the objective is available, the interior point-based method is used to check whether any objective lies inside the feasible region or not. The proposed design flow examines both the periphery and the interior of the polytope so generated. Henceforth, the design of an amplifier can be pointed out to be a distinctive type of optimization concern, called Nonlinear Programming (NLP). Furthermore, efficient global optimization methods have been established to yield an automated synthesis of amplifiers derived from the requirements. In this work, the formulation of the design problem for a cascode amplifier as NLP is described and analyzed. Thereafter, the optimal trade-off curves related to the performance metrics such as small signal gain (Av), unity gain frequency (UGF) or gain bandwidth product (GBWP), and power are derived in order to observe the corresponding dependencies.

Classification assessment methods

Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly for evaluating different learning algorithms. Most of these measures are scalar metrics and some of them are graphical methods. This paper introduces a detailed overview of the classification assessment measures with the aim of providing the basics of these measures and to show how it works to serve as a comprehensive source for researchers who are interested in this field. This overview starts by highlighting the definition of the confusion matrix in binary and multi-class classification problems. Many classification measures are also explained in details, and the influence of balanced and imbalanced data on each metric is presented. An illustrative example is introduced to show (1) how to calculate these measures in binary and multi-class classification problems, and (2) the robustness of some measures against balanced and imbalanced data. Moreover, some graphical measures such as Receiver operating characteristics (ROC), Precision-Recall, and Detection error trade-off (DET) curves are presented with details. Additionally, in a step-by-step approach, different numerical examples are demonstrated to explain the preprocessing steps of plotting ROC, PR, and DET curves.

Storage and repair bandwidth tradeoff for distributed storage systems with clusters and separate nodes

The optimal tradeoff between node storage and repair bandwidth is an important issue for distributed storage systems (DSSs). As for realistic DSSs with clusters, when repairing a failed node, it is more efficient to download more data from intra-cluster nodes than from cross-cluster nodes. Therefore, it is meaningful to differentiate the repair bandwidth from intra-cluster and cross-cluster. For cluster DSSs the tradeoff has been considered with special repair assumptions where all the alive nodes are utilized to repair a failed node. In this paper, we investigate the optimal tradeoff for cluster DSSs under more general storage/repair parameters. Furthermore, a regenerating code construction strategy achieving the points in the optimal tradeoff curve is proposed for cluster DSSs with specific parameters as a numerical example. Moreover, the influence of separate nodes for the tradeoff is also considered for DSSs with clusters and separated nodes.

A Multi-objective Simulation–Optimization Approach for Design of Cutoff Walls and Apron of Diversion Dams

Various seepage problems such as uplift pressure and high exit gradient lead to less efficient hydraulic structures since aims of the hydraulic design are substantial to be achieved. Cutoff walls and aprons are effective implements to control the problematic consequences of seepage flow. This paper presents a novel methodology for optimal design of cutoff walls and apron beneath Yusufkand diversion dam in Iran based on Non-dominated Sorting Genetic Algorithms-ΙΙ (NSGA-ΙΙ) multi-objective optimization model, Multi-Layer Perceptron (MLP) neural network model and Young Conflict Resolution Theory (YCRT). First, input–output data of a simulation model of seepage flow through the foundation of an existing diversion dam using Geostudio software are utilized to train and validate the MLP neural network, which can model the hydraulic performance of cutoff walls and the upstream apron. Then, the validated MLP neural network models are linked to NSGA-ΙΙ multi-objective optimization model and a trade-off curve is acquired among the hydraulic and cost criteria. Finally, YCRT is used to find the compromise optimal solution on the Pareto-front. Results show that by increasing length of the apron, when cutoff walls are at their minimum depth, the reduction of 18, 14 and 17% have been resulted in vertical exit gradient, uplift force and seepage flow discharge, respectively. Conversely, the same increment in length of the apron caused 2, 0 and 2% reduction in the mentioned parameters when other two cutoff walls are at their maximum depth. Moreover, the construction cost pertained to 1 meter of cutoff wall is nearly twice as much as for 1 meter of apron.

A regularized approach for estimation of a composite focal mechanism from a set of microearthquakes

We have developed a novel regularized approach to estimate a composite focal mechanism for microseismic events that share a similar source mechanism. The method operates by minimizing the weighted misfits of the SH/P amplitude ratios (in absolute sense and logarithmic scale) and P-wave polarities, using a regularization parameter determined from the trade-off curve for these values. This approach overcomes the low signal-to-noise ratio (S/N) and single-event azimuthal gaps that may otherwise limit the effectiveness of sparse surface arrays. Compared with focal mechanisms derived from P-wave polarity or amplitude-based methods, our regularized approach reduces the multiplicity of solutions and avoids the use of signed amplitude ratios, which may be ambiguous for data with low S/N. We apply our method to a set of 13 microseismic events recorded during hydraulic-fracture stimulation of the Marcellus Shale in West Virginia and Pennsylvania, USA, yielding a strike-slip focal mechanism accompanied by a minor normal component. Our solution is similar to previously reported focal mechanisms in this area. Jackknife analysis, which tests stability of the inversion based on random sampling of the observation, indicates 95% confidence intervals of 1° and 2°, respectively, for the plunge and azimuth of the P and T axes. By analyzing the event subsets, outliers are identified and the assumption of a single dominant focal mechanism is validated. Numerical modeling demonstrates that our approach is robust in the presence of variations of up to 0°–10° and 0°–35°, respectively, for the plunge and azimuth of P and T axes of the focal mechanisms of these events. Sensitivity analysis using synthetic data also indicates that the algorithm is tolerant to mispicks as well as errors in polarity and amplitude ratio. In the presence of some dissimilar focal mechanisms, the dominant focal mechanism can be reliably estimated if at least 70% of the events have similar source mechanisms.

Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization.

Proactive content delivery for vehicles over cellular networks: The fundamental benefits of computing and caching

The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content demand brings significant challenges for the fifth generation (5G) cellular communication network. To cope with the challenge of massive content delivery, previous studies suggested that the 5G mobile edge network should be designed to integrate communication, computing, and cache (3C) resources to enable advanced functionalities such as proactive content delivery and in-network caching. However, the fundamental benefits achievable by computing and caching in mobile communications networks are not yet properly understood. This paper proposes a novel theoretical framework to characterize the tradeoff among computing, cache, and communication resources required by the mobile edge network to fulfill the task of content delivery. Analytical and numerical results are obtained to characterize the 3C resource tradeoff curve. These results reveal key insights into the fundamental benefits of computing and caching in vehicular mobile content delivery networks.