Quality Trade-off Research Articles

Generalized Prediction Framework for Reconstructed Image Properties using Neural Networks.

Model-based reconstruction (MBR) algorithms in CT have demonstrated superior dose-image quality tradeoffs compared to traditional analytical methods. However, the nonlinear and data-dependent nature of these algorithms pose significant challenges for performance evaluation and parameter optimization. To address these challenges, this work presents an analysis framework for quantitative and predictive modeling of image properties in general nonlinear MBR algorithms. We propose to characterize the reconstructed appearance of arbitrary stimuli by the generalized system response function that accounts for dependence on the imaging conditions, reconstruction parameters, object, and the stimulus itself (size, contrast, location). We estimate this nonlinear function using a multilayer perceptron neural network by providing input and output pairs that samples the range of imaging parameters of interest. The feasibility of this approach was demonstrated for predicting the appearance of a spiculated lesion reconstructed by a penalized-likelihood objective with a Huber penalty in a physical phantom as a function of its location and reconstruction parameters β and δ. The generalized system response functions predicted from the trained neural network show good agreement with those computed from mean reconstructions, proving the ability of the framework in mapping out the nonlinear function for combinations of imaging parameters not present in the training data. We demonstrated utility of the framework to achieve desirable (e.g., non-blocky) lesion appearance in arbitrary locations in the phantom without the need for performing actual reconstructions. The proposed prediction framework permits efficient and quantifiable performance evaluations to provide robust control and understanding of image properties for general classes of nonlinear MBR algorithms.

A NEW ANALYTICAL METHODOLOGY TO HANDLE TIME-COST TRADE-OFF PROBLEM WITH CONSIDERING QUALITY LOSS COST UNDER INTERVAL-VALUED FUZZY UNCERTAINTY

This paper proposes a new framework in addressing time-cost trade-off problem (TCTP) under uncertainty. First critical path analysis is carried out based on developing a new interval-valued fuzzy (IVF)-program evaluation and review technique (PERT) approach. Then, non-conformance risks that influence on execution quality of activities are identified and evaluated based on a new approach that considers probability of risk along with impacts on time, cost, and performance. Then, a new mathematical model under IVF uncertainty is presented to decrease project total time while considering time, cost and quality loss cost that is determined in form of rework or modification cost. Finally, the approach categorizes the activities in three groups based on their level of criticality. Outcome of this methodology is a scheduling that addresses time, cost and quality trade-offs in addition to categorizing activities in different groups based on being on the critical path. Therefore, the project manager not only gets a scheduling based on the TCTP with considering quality loss cost but also has a knowledge of activities that require extra attentions. To show the steps of this methodology, an existing application from the literature is adopted and solved.

Architectural Tactics for Big Data Cybersecurity Analytics Systems: A Review

ContextBig Data Cybersecurity Analytics (BDCA) systems leverage big data technologies for analyzing security events data to protect organizational networks, computers, and data from cyber attacks. ObjectiveWe aimed at identifying the most frequently reported quality attributes and architectural tactics for BDCA systems. MethodWe used Systematic Literature Review (SLR) method for reviewing 74 papers. ResultOur findings are twofold: (i) identification of 12 most frequently reported quality attributes for BDCA systems; and (ii) identification and codification of 17 architectural tactics for addressing the identified quality attributes. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each. ConclusionOur study reveals that in the context of BDCA (a) performance, accuracy and scalability are the most important quality concerns (b) data analytics is the most critical architectural component (c) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support (d) empirical investigation is required to evaluate the impact of the codified tactics and explore the quality trade-offs and dependencies among the tactics and (e) the reported tactics need to be modelled using a standardized modelling language such as UML.

Exploiting Algorithmic Noise Tolerance for Scalable On-Chip Voltage Regulation

With the advent of on-chip digital low-dropout (DLDO) regulators, distributed on-chip voltage regulation has become increasingly promising. Environmental and operating conditions have been demonstrated to degrade DLDO performance, which directly affects execution accuracy. The area overhead (OH) needed to compensate aging-induced voltage noise degradation can be significant. Accordingly, in this paper, the algorithmic noise tolerance of certain processor components is exploited as an area–quality control knob to trade the program output quality for area OH. Furthermore, efficient and lightweight techniques utilizing a unidirectional shift register and reduced clock pulsewidth triggering are proposed to realize a novel aging-aware (AA) DLDO to achieve a better area and quality tradeoff. Owing to the large number and distributed nature of voltage regulators, with the proposed design, both the number of regulators utilized in the system and the size of each local regulator are scalable to satisfy the needs of different applications and processor components with varying algorithmic noise tolerance. It is demonstrated through simulation of an IBM POWER8 like processor that the proposed AA design can achieve up to, respectively, 43.2% and $3\times $ transient and steady-state performance improvement. Additionally, more than 10% area OH saving can be achieved over a 5-year period.

Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study

Datasets collected using light detection and ranging (LiDAR) technology often consist of dense point clouds. However, the density of the point cloud could vary depending on several different factors including the capabilities of the data collection equipment, the conditions in which data are collected, and other features such as range and angle of incidence. Although variation in point density is expected to influence the quality of the information extracted from LiDAR, the extent to which changes in density could affect the extraction is unknown. Understanding such impacts is essential for agencies looking to adopt LiDAR technology and researchers looking to develop algorithms to extract information from LiDAR. This paper focuses specifically on understanding the impacts of point density on extracting traffic signs from LiDAR datasets. The densities of point clouds are first reduced using stratified random sampling; traffic signs are then extracted from those datasets at different levels of point density. The precision and accuracy of the detection process was assessed at the different levels of point cloud density and on four different highway segments. In general, it was found that for signs with large panels along the approach on which LiDAR data were collected, reducing the point cloud density by up to 70% of the original point cloud had minimal impacts on the sign detection rates. Results of this study provide practical guidance to transportation agencies interested in understanding the tradeoff in price, quality, and coverage, when acquiring LiDAR equipment for the inventory of traffic signs on their transportation networks.

Using the Conservation Practice Effectiveness (CoPE) Database to assess adoption tradeoffs

The newly published Conservation Practice Effectiveness (CoPE) Database compiles information on the effectiveness of a suite of conservation practices developed to treat contaminants in surface runoff and tile drainage water from agricultural landscapes. Traditional conservation practices such as no-tillage and conservation crop rotation are included in the CoPE Database, as well as novel practices such as drainage water management, blind inlets, and denitrification bioreactors. This will be particularly useful to conservation planners seeking new approaches to water quality problems associated with dissolved constituents, such as nitrate (NO₃) or soluble phosphorus (SP), and for researchers seeking to understand the circumstances in which such practices are most effective. Another novel feature of the database is the presentation of information on how individual conservation practices impact multiple water quality concerns. This information will be critical to enabling conservationists and policy makers to avoid (or at least be aware of) undesirable tradeoffs, whereby great efforts are made to improve water quality related to one resource concern (e.g., sediment) but exacerbate problems related to other concerns (e.g., NO₃ or SP). Finally, we note that the CoPE Database can serve as a source of the soft data needed to calibrate simulation models assessing the potential water quality tradeoffs of conservation practices, including those that are still being developed.

The Effect of Trade Liberalization on Marriage and Fertility: Evidence from Indian Districts

Using a district-level panel data constructed from four waves of decennial Indian Census covering 1981-2011, we examine the medium run (1991-2001) and long run (1991-2011) impacts of the 1991 trade liberalization in India on marriage and fertility rates among young women aged 15-34 years. We exploit the fact that the countrywide tariff reductions that varied across industries created exogenous local labor market shocks based on the initial industrial composition of the district. We find that districts that were more exposed to tariff cuts witnessed a larger increase in marriage rate, especially in urban areas, compared to districts that were less exposed to tariff cuts. Fertility rate declined in districts that were relatively more exposed to tariff reductions, with the effect mostly driven by the rural areas. On average, the trade reform had no negative impact on the employment of young men and women. While we can explain our findings on marriage rates with the help of aggregate labor market indicators which indicate no adverse effect on the workforce participation rate of young men and women, thus increasing their prospects of marriage, the decline in fertility points towards a quantity quality trade-off where families invest in the quality of children when economic prospects improve.

Clinical Evaluation of Shot-Within-Shot Optimization for Gamma Knife Radiosurgery Planning and Delivery

Shot-within-shot (SWS) optimization is a new planning technique that relies on various combinations of shot weighting and prescription isodose line (IDL) to reduce beam-on time. The method differs from other planning techniques that incorporate mixed collimation, multiple stereotactic coordinates, and traditionally low prescription IDLs (<60%). In this work, we evaluate the percentage of brain metastasis for which the method can be applied, the magnitude of the resultant time savings, and the possible tradeoffs in plan quality. A retrospective analysis was performed on 75 patients treated for 241 metastatic lesions in the brain. For each lesion, the original planning metrics related to target coverage, conformity, gradient, and beam-on time were recorded. A subset of lesions were selected for replanning using the SWS technique based on size, shape, and proximity to critical structures. Two replans were done, a reference plan was prescribed at the 50% IDL, and an optimized plan was prescribed at an IDL typically >50%. Planning metrics were then compared among the original plan and the 2 replans. More than a third (39%) of the brain metastases were eligible for the SWS technique. For these lesions, the differences between the original plan and reference SWS plan were as follows: ΔV12Gy < 0.5 cc in 93% of cases, ΔV12Gy < 0.5 cc in 100% of cases, Δselectivity < 0.1 in 79% of cases. Negligible differences were seen between the 2 replans in terms of Δselectivity and ΔV12Gy; ΔGI < 5% in 99% of cases. After optimization, beam-on time was reduced by 25%-30% in approximately 40%-50% of eligible lesions when compared with the reference SWS plan (ΔTmax = 42%). In comparison with the original plan, beam-on time was reduced even further, ΔT > 50% in 20% of cases (ΔTmax = 70%). This work demonstrates clinically that optimization using the shot-within-shot technique can reduce beam-on time without degrading treatment plan quality.

Effective Content-Aware Chroma Reconstruction Method for Screen Content Images.

In this paper, we propose an effective novel content-aware chroma reconstruction (CACR) method for screen content images (SCIs). After receiving the decoded downsampled YUV image on the client side, our fast chroma-copy approach reconstructs the missing chroma pixels in the flat regions of SCI. Then, for non-flat regions, a non-flat region-based winner-first voting (NRWV) strategy is proposed to identify the chroma subsampling scheme used on the server side prior to compression. Further, an effective adaptive hybrid approach is proposed to reconstruct each missing chroma pixel in the non-flat region by fusing the two reconstructed results, one from our modified NRWV-based chroma subsampling-binding and luma-guided chroma reconstruction scheme, which favors the sharp edges in SCI, as well as the other from the bicubic interpolation scheme, which favors blurred and continuous-tone textures. Further, based on the identified chroma subsampling scheme, a geometry alignment-based error compensation approach is proposed to enhance the reconstructed chroma image. Based on typical test SCIs and JCT-VC screen content videos, comprehensive experiments are carried out in HEVC-16.17 to demonstrate that in terms of quality, visual effect, and quality-bitrate tradeoff of the reconstructed SCIs, our CACR method significantly outperforms the existing state-of-the-art methods.

Relative Labour Market Performance of Former International Students: Evidence from the National Graduates Survey

Canada is increasingly looking to international students as a source of post-secondary tuition revenues and new immigrants. We compare the labour market performance of former international students (FISs) through the first decade of the 2000s with that of their Canadian-born and -educated (CBE) and foreign-born and -educated (FBE) counterparts. We find that FISs outperform FBE immigrants by a substantial margin but underperform CBE graduates from similar post-secondary programs. We also find evidence of a deterioration in FIS outcomes relative to both comparison groups. We argue that this deterioration is most consistent with a quality trade-off because the supply of international students has not kept pace with the growth in demand.

Local algorithms for hierarchical dense subgraph discovery

Finding the dense regions of a graph and relations among them is a fundamental problem in network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical relations. The incremental nature of algorithms for computing these decompositions and the need for global information at each step of the algorithm hinders scalable parallelization and approximations since the densest regions are not revealed until the end. In a previous work, Lu et al. proposed to iteratively compute the h -indices of neighbor vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. This work generalizes the iterative h -index computation for truss decomposition as well as nucleus decomposition which leverages higher-order structures to generalize core and truss decompositions. In addition, we prove convergence bounds on the number of iterations. We present a framework of local algorithms to obtain the core, truss, and nucleus decompositions. Our algorithms are local, parallel, offer high scalability, and enable approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our local algorithms on real-world networks.

A Quality-Configurable Approximate Serial Bus for Energy-Efficient Sensory Data Transfer

Mobile, wearable, and implantable devices integrate an increasing number and variety of sensors, such as microphones, image sensors, and accelerometers. These devices spend substantial amounts of time reading the sensors within them, incurring significant data transfer from the sensor to the processor. The high capacitance of sensor-to-processor interconnects, high data rates of modern sensors and frequent usage of applications that use sensors result in significant energy dissipation for sensory data transfer. The contribution of sensing to power consumption is only expected to increase as more and more high-performance sensors are embedded in a single device. To address this challenge, we propose AxSerBus, a quality-configurable approximate serial bus that exploits the locality of sensory data and the error resiliency of sensing applications to reduce energy dissipation. AxSerBus significantly reduces signal transitions by encoding the differences of sensory data in three encoding modes, depending on the magnitude of the differences: very small differences are zeroed out, incurring no energy dissipation; intermediate differences are encoded using special low-transition count patterns; and for high differences, the absolute value (not the difference) of the data is transmitted. Compared with previous schemes, the proposed multi-level encoding results in more data being encoded using low-energy patterns. In addition, in the intermediate difference encoding mode, the differences are encoded in an approximate manner, and the approximation bounds are proportional to the magnitude of the differences. Since small differences are more frequent than large differences in sensory data, the proposed encoding scheme also minimizes quality degradation. We demonstrate that AxSerBus achieves superior energy vs. quality trade-offs compared with previous schemes. We also present a low-overhead heuristic scheme for dynamic quality configuration. In an image processing application, AxSerBus reduces the signal transitions up to 90% with minor image quality degradation. In the context of an optical character recognition (OCR) application, AxSerBus achieves 79.4% reduction in dynamic power dissipation, while maintaining accuracy above 95%.

Energy and Reliability Improvement of Voltage-Based, Clustered, Coarse-Grain Reconfigurable Architectures by Employing Quality-Aware Mapping

An energy–quality scalable coarse grain reconfigurable architecture (CGRA) based on the voltage overscaling (VOS) technique is presented. The approximation level of each processing element (PE) in the CGRA is determined by the applied VOS-determined voltage level. By employing the technique, the architecture may be configured for accurate or approximate modes of computation depending on a user-specified output quality-of-service target for a given application. More precisely, operating voltages used for performing various operations in the application dataflow graph are minimized subject to the output quality constraint by using an energy–quality tradeoff algorithm. To make the hardware implementation of the scheme more efficient, PEs are clustered into groups of (e.g., $3\times 1$ and $2\times 1$ ) voltage islands. To assess the efficacy of the proposed method in improving the power (energy) consumption and reliability of CGRAs, different combinations of minimum output quality constraints, voltage levels, and cluster sizes for several benchmarks are studied. Simulation results indicate considerable reductions in energy consumption (up to 43%) and aging rate (up to 73%) when compared with the conventional CGRA with perfect output quality (i.e., with no approximate computations).

Shaping Pharmaceutical Tenders for Effectiveness and Sustainability in Countries with Expanding Healthcare Coverage.

Policy makers in countries, aiming to build and expand their healthcare systems and coverage, need effective procedures to support the most efficient use of limited financial resources. Tendering is commonly deployed to minimize and fix the purchasing price for the contract duration, especially for off-patent pharmaceuticals. While tenders can reduce acquisition costs, they may also expose the healthcare systems to risks including drug shortages, quality trade-offs, and ultimately, compromised patient health outcomes. Careful planning is therefore required. The effectiveness and impact of tendering were examined in different healthcare settings to establish good tender practices and to develop guidance for tender stakeholders in countries with expanding healthcare coverage for the effective conduct. The literature was reviewed for tender practices and outcomes in all countries, and tender experts from one multi-national pharmaceutical company in 17 countries with expanding healthcare coverage were surveyed on current tender practices. Tendering is a common practice for multisource pharmaceuticals in most countries worldwide. However, countries with expanding healthcare coverage specifically are vulnerable to the risks of defective tendering practices. Risk factors include non-transparent tender practices, a lack of consistency, unclear tender award criteria, a focus on lowest price only, single-winner tendering, and generally, a lack of impact monitoring. If well planned, managed, and conducted, tenders can be advantageous. Countries with expanding healthcare coverage should approach tenders strategically to achieve the desired improvements in healthcare. The good tender practices derived from this study may guide policy makers and purchasers in countries with expanding healthcare coverage on how to expand access to healthcare at an affordable cost. These include the use of multiple selection criteria and performance monitoring. Plain Language Summary Decision makers in countries aiming to expand their healthcare systems must best use the limited money available for healthcare. Tendering is commonly deployed for pharmaceuticals produced by multiple manufacturers (so-called multisource pharmaceuticals), to choose the product with the lowest price. Through tenders, purchasers request offers from suppliers for the needed products.The ultimate purpose of our research was to develop a guidance on robust tender processes. Therefore, we reviewed the literature to examine the effectiveness and impact of current tendering practices. In addition, we conducted a survey among tender experts from one pharmaceutical company in 17 countries with expanding healthcare coverage.In both the survey and the literature review, we confirmed that worldwide, tendering is a common practice for multisource pharmaceuticals. However, defective tendering practices may increase the vulnerability for some risks including abuse due to intransparent processes, lack of consistency, unclear tender award criteria, a focus on lowest price only, single winner tendering, and generally, a lack of impact monitoring after the end of the tender process.Hence, tenders must be well planned, managed, and conducted to be advantageous. Countries with defined and transparent tender frameworks and processes will be better equipped to achieve the desired improvements in the healthcare systems. ‘Good tender practices’ include the clear definition of requirements to be used as selection criteria in addition to acquisition costs, and for monitoring of the tender success. ‘Good tender practices’ may help to manage cost and improve healthcare at the same time.Electronic supplementary materialThe online version of this article (10.1007/s40258-018-0405-7) contains supplementary material, which is available to authorized users.

Platform Architecture and Quality Trade-offs of Multihoming Complements

Multihoming, the decision to design a complement to operate on multiple platforms, is becoming increasingly common in many platform markets. Perceived wisdom suggests that multihoming is beneficial for complement providers as they expand their market reach, but it reduces differentiation among competing platforms as the same complements become available on different platforms. We argue that complement providers face trade-offs when designing their products for multiple platform architectures—they must decide how far to specialize the complement to each platform’s technological specifications. Because of these trade-offs, multihoming complements can have different quality performance across platforms. In a study of the U.S. video game industry, we find that multihoming games have lower-quality performance on a technologically more complex console than on a less complex one. Also, games designed for and released on a focal platform have lower-quality performance on platforms they are subsequently multihomed to. However, games that are released on the complex platform with a delay suffer a smaller drop in quality on complex platforms. This has important implications for platform competition, and for managers considering expanding their reach through multihoming.The online appendix is available at https://doi.org/10.1287/isre.2018.0779 .

Beyond the Iron Triangle in Public Construction Projects

AbstractTraditionally, construction professionals have focused on time, cost, and quality tradeoffs for successful completion of public construction projects. In addition to this, no-dispute and sa...

A Genetic Algorithm-based approach for Project Management and developed design of construction

Construction Project Management processes aim at the optimization of three main KPIs: time, cost and quality. As the time / cost trade-off approach is well known, quality optimization is still pursued with a practical approach, often based upon Project Manager’s experience. A case study of construction of a new university building is presented, focusing on external walls detailed design. Wall performances concerning time, costs and quality depend on the performances of each of the product layer that composes the wall. For each layer three commercial options has been considered. Therefore, a time – cost – quality trade-off procedure has been implemented using Genetic Algorithms, with the aim of finding a set of optimal solutions for the construction of the building envelope.

Beyond the Quantity–Quality tradeoff: Population control policy and human capital investment

The quantity–quality tradeoff theory implies that a reduction in fertility would induce more human capital investment per child, and it is widely believed that population control policy could promote human capital levels in developing countries. China's one-child policy (OCP) has significantly reduced the fertility in the country, and popular belief holds that this policy has contributed to the enhancement in China's human capital since 1979. However, the quantity–quality tradeoff dose not present the complete truth—another crucial factor relates to which segment of the population is reduced, a critical issue that has been ignored in the literature. China's OCP is significantly more strictly implemented in urban areas than in rural areas, where human capital investment in children is significantly lower. This two-tier OCP could have significantly increased the rural–urban fertility ratio and brought about important consequences. We first emphasize the importance of such a population compositional effect, which may offset the potentially positive quantity–quality tradeoff effect on human capital. We present a simple analytic framework and quantify the relative sizes of the quantity–quality tradeoff and compositional effects. We find that the former is dominated by the latter, and that the OCP has reduced the average human capital level of China's next generation by approximately 1–2%. This novel evidence indicates that the conventional wisdom on population control may be incomplete and that its policy implications could be misleading.

Solving the task variant allocation problem in distributed robotics

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively.

Optimal Cost–Quality Trade-Off Model for Differentiating Presale Housing Quality Strategies

Housing quality (HQ) has been a long-standing concern for both developers and homebuyers. Currently, HQ depends on the expected profit and subjectivity of the developers, and homebuyers only have a passive choice of whether to accept housing with such quality. Asian housing supply markets have largely adopted the presale housing system. Under this system, developers are able to verify future occupants before commencing construction, enabling them to provide customized designs and differentiated quality items in order to meet user demands and value. Consequently, HQ can be enhanced. A cost–quality trade-off model was developed using a genetic algorithm to help decision-makers identify the optimal HQ differentiation strategy that simultaneously satisfies homebuyers’ expectations of quality and developers’ expectations of profits. The findings showed that the presale housing system effectively improves HQ. A 6% increase in homebuyers’ budgets can achieve the optimal quality improvement effect, while an 8% or more increase in developers’ construction costs in order to improve HQ can generate an additional premium for the developers.