Continuous Tradeoff Research Articles

Clinical decision making for VA ECMO weaning in patients with cardiogenic shock A formative qualitative study.

Weaning and liberation from VA ECMO in cardiogenic shock patients comprises a complex process requiring a continuous trade off between multiple clinical parameters. In the absence of dedicated international guidelines, we hypothesized a great heterogeneity in weaning practices among ECMO centers due to a variety in local preferences, logistics, case load and individual professional experience. This qualitative study focused on the appraisal of clinicians' preferences in decision processes towards liberation from VA ECMO after cardiogenic shock while using focus group interviews in 4 large hospitals. The goal was to provide novel and unique insights in daily clinical weaning practices. As expected, we found we a great heterogeneity of weaning strategies among centers and professionals, although participants appeared to find common ground in a clinically straightforward approach to assess the feasibility of ECMO liberation at the bedside. This was shown in a preference for robust, easily accessible parameters such as arterial pulse pressure, stable cardiac index ≥2.1L/min, VTI LVOT and 'eyeballing' LVEF.

A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems

In urban infrastructure systems, resilience is crucial for maintaining functionality, minimizing losses, and expediting recovery during disruptive incidents. Effective allocating resources across various phases of emerging disturbances can generally enhance the system's capacity to cope with disasters. However, it is imperative to recognize that distinct resource allocation strategies may lead to divergent outcomes in terms of resilience performance. Therefore, this study develops a framework for optimizing resource allocation based on multiple resilience objectives by understanding the interplay between resilience performance and dynamic decision-making. The resilience processes are first formalized into distinct stages, considering the technical and organizational resilience of the infrastructure system in the event of disruption. Building upon this foundation, five decision scenarios are proposed, contingent on the allocation or non-allocation of resources to each resilience stage. A multi-resilience-objective mixed-integer linear programming (MROMILP) model is formulated to optimize the resource allocation scheme for each resilience stage within the constraints of internal resources. Finally, the model and framework are tested using a power system as a tangible example. The integrated multi-stage quantitative resilience assessment and optimization method proposed in this study can assist decision-makers in making dynamic and continuous trade-offs between resources and resilience targets.

Graph Federated Learning for CIoT Devices in Smart Home Applications

This article deals with the problem of statistical and system heterogeneity in a cross-silo federated learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel graph signal processing (GSP)-inspired aggregation rule based on graph filtering dubbed “G-Fedfilt.” The proposed aggregator enables a structured flow of information based on the graph’s topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous tradeoff between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional federated averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models’ gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to 3.99% better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local data sets, while it is capable of yielding up to 2.41% higher accuracy than FedAvg in the case of testing the generalization of the models. Furthermore, the proposed communication optimization scheme can boost the framework’s efficiency by reducing the computation, communication desynchronization, and latency up to 70.21%, 99.65%, and 44.61%, respectively, at the cost of 0.36% accuracy and under the system heterogeneity.

Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition

Social interactions evolve continuously. Sometimes we cooperate, sometimes we compete, while at other times we strategically position ourselves somewhere in between to account for the ever-changing social contexts around us. Research on social interactions often focuses on a binary dichotomy between competition and cooperation, ignoring people’s evolving shifts along a continuum. Here, we develop an economic game – the Space Dilemma – where two players change their degree of cooperativeness over time in cooperative and competitive contexts. Using computational modelling we show how social contexts bias choices and characterise how inferences about others’ intentions modulate cooperativeness. Consistent with the modelling predictions, brain regions previously linked to social cognition, including the temporo-parietal junction, dorso-medial prefrontal cortex and the anterior cingulate gyrus, encode social prediction errors and context-dependent signals, correlating with shifts along a cooperation-competition continuum. These results provide a comprehensive account of the computational and neural mechanisms underlying the continuous trade-off between cooperation and competition.

Methodology for an HBIM workflow focused on the representation of construction systems of built heritage

• A HBIM workflow addressing the representation of building construction systems is presented. • It is structured into 5 recursive and flexible phases to adapt to available information/model uses. • Each phase is presented with a methodological insight to help operators in developing the process. • The results of the methodology are applied on The National Archaeological Museum of Naples – MANN. • the test proved its the efficiency while guiding experts the most appropriate course of action. Conservation and valorisation practices of built heritage can greatly benefit from Heritage Building Information Modelling (HBIM) workflows. The use of HBIM as an information management process is not fully established yet, also due to the focus on industrialised architecture typical of the most common BIM tools. Capitalising on the benefits of the BIM process in the field of built heritage requires pursuing a continuous trade-off between geometric accuracy, semantic richness and parametric behaviour. This research aims to present an HBIM workflow that, as compared to other, more specific pipelines, is general in scope to support the planning and implementation of maintenance and conservation activities of built heritage, emphasising the representation of the building construction systems. The workflow is structured in five phases (model planning, data collection, geometric survey, breakdown structure and HBIM modelling), recursive and flexible to influence one another and to adjust to the information available and the development of the work over time. Each phase of the workflow is presented with a general outline and a methodological insight to help operators in developing the HBIM process most suitable for specific cases. The results of the application of this methodology on a complex and massive historical building (The National Archaeological Museum of Naples – MANN) show that the workflow is both versatile and sufficiently flexible to accommodate a heterogeneous range of objectives while guiding experts to select and document the most appropriate course of action.

Estimating Usable-Security Through Hesitant Fuzzy Linguistic Term Sets Based Technique

The apparent contradiction between usability and security has been discussed in the literature for several years. This continuous trade-off requires be acknowledging and handling whenever security solutions are introduced. However, some progressive analysts point out that present security solutions are usually very difficult for several users, and they have expressed a willingness to simplify the security product user experience. Usable security is still mostly unexplored territory in computer science. Which we are all aware with security and usability on many levels, usable security has received little operational attention. Companies have recently focused primarily on usable security. As consumers prefer to discard unsatisfactory solutions, attaining well-balanced degrees of both is a prerequisite for adequate security. Usability should be examined from the viewpoint of security to be properly assessed. The aim of this research study is to explore and discuss universally accepted and solution-independent factors influencing the perceived usable-security of institutional websites. An integrated Hesitant Fuzzy Linguistic term sets based technique was chosen as the methodology, and the experts and academicians opinion were collected. The use of predetermined selection criteria resulted in the compilation of statistical findings and were used to determine the usable-security of five different instituational websites.

Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes

Decision-intensive business processes are performed by decision makers who gather different pieces of information to reach the process objective: a final decision of high quality, for instance, the final price of a quote or the diagnosis of a failure of a hightech machine, as a result of an information-gathering process with minimum costs and efforts. Gathering all possible pieces of information results in high quality decisions, but also yields high costs and efforts. Therefore, decision makers require decision support to determine which information to gather to make the best final decision. This paper introduces an approach that supports a decision maker in the continuous trade-off between the effective acquisition of more information and cost-efficient decision making. The approach uses a well-defined modeling notation for decision-intensive processes, CMMN, and links it to a standard optimization technique, Markov Decision Processes. The approach calculates an optimal information-gathering solution, such that the expected result of the main decision minus the process cost for collecting information is optimized. The approach uses the solution to configure a run-time recommendation tool for the decision maker. The approach is flexible and allows that a decision maker ignores the advice; it then continues to offer recommendations in the subsequent states. We show the feasibility and effectiveness of our approach on a real-world quote process.

Approximate Voronoi cells for lattices, revisited

Abstract We revisit the approximate Voronoi cells approach for solving the closest vector problem with preprocessing (CVPP) on high-dimensional lattices, and settle the open problem of Doulgerakis–Laarhoven–De Weger [PQCrypto, 2019] of determining exact asymptotics on the volume of these Voronoi cells under the Gaussian heuristic. As a result, we obtain improved upper bounds on the time complexity of the randomized iterative slicer when using less than 2 0.076 d + o ( d ) $2^{0.076d + o(d)}$ memory, and we show how to obtain time–memory trade-offs even when using less than 2 0.048 d + o ( d ) $2^{0.048d + o(d)}$ memory. We also settle the open problem of obtaining a continuous trade-off between the size of the advice and the query time complexity, as the time complexity with subexponential advice in our approach scales as d d / 2 + o ( d ) $d^{d/2 + o(d)}$ matching worst-case enumeration bounds, and achieving the same asymptotic scaling as average-case enumeration algorithms for the closest vector problem.

Group sequential designs for clinical trials with bivariate endpoints.

Although all clinical trials are designed and monitored using more than one endpoint, methods are needed to assure that decision criteria are chosen to reflect the clinically relevant tradeoffs that assure the trial's scientific integrity. This article presents a framework for the design and monitoring clinical trials in a bivariate outcome space. The framework uses a rectangular hyperbola to define a bivariate null curve that divides outcome space into regions of benefit and lack of benefit. The curve is shown to be a flexible mapping of bivariate space that allows a continuous tradeoff between the two endpoints in a manner that captures many previous bivariate designs. The curve is extended to a distance function in bivariate space that allows different decisions in each of the four quadrants that comprise bivariate space. The distance function forms a statistic ( ); the distribution of its estimate is derived and used as a basis for trial design and group sequential monitoring plans in bivariate space. A recursive form of the bivariate group sequential density is used to evaluate and control operating characteristics for the proposed design. The bivariate designs are shown to meet or exceed the usual standards for size and power. The proposed design is illustrated in the ongoing NHLBI-sponsored Kids-DOTT multinational randomized controlled trial comparing shortened versus conventional anticoagulation for the treatment of venous thromboembolism in patients less than 21 years of age. The proposed methods are broadly applicable to a wide range of clinical settings and trial designs.

Advanced Modulation Techniques for Flexible Optical Transceivers: The Rate/Reach Tradeoff

This tutorial paper reviews advanced modulation techniques that have been proposed in the literature for the implementation of flexible (or reconfigurable) transceivers, which are fundamental building blocks of next-generation software-defined optical networks. Using a common reference multi-span propagation system scenario, the performance of transceivers employing standard quadrature amplitude modulation with variable-rate forward error correction, probabilistic constellation-shaping, and time-domain hybrid formats is assessed, highlighting the achievable flexibility in terms of continuous tradeoff between transmission rate and distance. The combination of these techniques with sub-carrier multiplexing, which enables an increase of the fiber nonlinearity tolerance thanks to the optimization of the symbol rate per sub-carrier, is also discussed.

Simplifying the nonlinear continuous time-cost tradeoff problem

Equivalent simplification is an effective method for solving large-scale complex problems. In this paper, the authors simplify a classic project scheduling problem, which is the nonlinear continuous time-cost tradeoff problem (TCTP). Simplifying TCTP is a simple path problem in a critical path method (CPM) network. The authors transform TCTP into a simple activity float problem and design a complex polynomial algorithm for its solution. First, the authors discover relationships between activity floats and path lengths by studying activity floats from the perspective of path instead of time. Second, the authors perform simplification and improve the efficiency and accuracy of the solution by deleting redundant activities and narrowing the duration intervals of non-redundant activities. Finally, the authors compare our method with current methods. The relationships between activity floats and path lengths provide new approaches for other path and correlative project problems.

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies.

Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: http://www.ti.uni-tuebingen.de/scanpathcomparison.html .

Some Special Cases of a Continuous Time-Cost Tradeoff Problem with Multiple Milestones under a Chain Precedence Graph

We consider a time-cost tradeoff problem with multiple milestones under a chain precedence graph. In the problem, some penalty occurs unless a milestone is completed before its appointed date. This can be avoided through compressing the processing time of the jobs with additional costs. We describe the compression cost as the convex or the concave function. The objective is to minimize the sum of the total penalty cost and the total compression cost. It has been known that the problems with the concave and the convex cost functions for the compression are NP-hard and polynomially solvable, respectively. Thus, we consider the special cases such that the cost functions or maximal compression amounts of each job are identical. When the cost functions are convex, we show that the problem with the identical costs functions can be solved in strongly polynomial time. When the cost functions are concave, we show that the problem remains NP-hard even if the cost functions are identical, and develop the strongly polynomial approach for the case with the identical maximal compression amounts.

Secondary User Aggressiveness Optimization in Sensing-Transmission Scheduling for Cognitive Radio Networks

The constraint of limited frequency spectrum for wireless communication is very daunting. The radio spectrum licensed to permanent users is wasted at times when not used continuously. Cognitive radio technology involves a change in regulation of radio spectrum. In cognitive radio networks a secondary user network is designed for efficiently using gaps in spectrum and not causing at the same time harmful interference to license holding primary users. There is a continuous tradeoff between the opposing goals of Primary User transmission protection and Secondary User throughput maximization. For a Secondary User, both sensing and transmitting is important so as to avoid collisions, packet loss and improve throughput. This paper presents optimization of Secondary User packet length in sensing-transmission scheduling for dynamically accessing the spectrum in cognitive radios. The need to maximize the Secondary User access is achieved by optimizing its packet length with respect to collision cost in a threshold based sensing-transmission structure using Matlab simulations. The results are presented in the form of traces of thresholds for different packet lengths and collision costs. This scheme optimizes Secondary User aggressiveness and throughput while safeguarding Primary User interests

Tuple lattice sieving

Lattice sieving is asymptotically the fastest approach for solving the shortest vector problem (SVP) on Euclidean lattices. All known sieving algorithms for solving the SVP require space which (heuristically) grows as$2^{0.2075n+o(n)}$, where$n$is the lattice dimension. In high dimensions, the memory requirement becomes a limiting factor for running these algorithms, making them uncompetitive with enumeration algorithms, despite their superior asymptotic time complexity.We generalize sieving algorithms to solve SVP with less memory. We consider reductions of tuples of vectors rather than pairs of vectors as existing sieve algorithms do. For triples, we estimate that the space requirement scales as$2^{0.1887n+o(n)}$. The naive algorithm for this triple sieve runs in time$2^{0.5661n+o(n)}$. With appropriate filtering of pairs, we reduce the time complexity to$2^{0.4812n+o(n)}$while keeping the same space complexity. We further analyze the effects of using larger tuples for reduction, and conjecture how this provides a continuous trade-off between the memory-intensive sieving and the asymptotically slower enumeration.

Secondary User Sensing Time Optimization in Sensing-Transmission Scheduling for Dynamic Spectrum Access in Cognitive Radio

The constraint of limited frequency spectrum for wireless communication is very daunting. The radio spectrum licensed to permanent users is wasted at times when not used continuously. Cognitive radio technology involves a change in regulation of radio spectrum. In cognitive radio networks a secondary user network is designed for efficiently using gaps in spectrum and not causing at the same time harmful interference to license holding primary users. Opportunistic spectrum access involves the sensing of white spaces in the transmissions of Primary Users and their utilization by Secondary Users. There is a continuous tradeoff between the opposing goals of Primary User transmission protection and Secondary User throughput maximization. For a Secondary User, sensing is important so as to avoid collisions, hence packet loss. At the same time being too cautious and having long sensing periods can be costly. This paper presents optimization of Secondary User sensing time in sensing-transmission scheduling for dynamically accessing the spectrum in cognitive radios. The need to maximize the Secondary User access is achieved by optimizing its sensing time with respect to collision cost in a threshold based sensing-transmission structure using simulations. The results are presented in the form of traces of thresholds for different sensing times and collision costs. This scheme optimizes Secondary User throughput while safeguarding Primary User interests. This defines a standard to give an insight into the design of sensing-transmission scheduling in cognitive radio networks.

Spectral Efficiency-Adaptive Optical Transmission Using Time Domain Hybrid QAM for <newline/>Agile Optical Networks

We report the transmission of time domain hybrid QAM (TDHQ) signals for agile optical networks. A continuous tradeoff between spectral efficiency and achievable distance by mixing modulation formats including QPSK, 8QAM, and 16QAM is demonstrated in two scenarios: 1) 28 Gbaud non-return-to-zero (NRZ) signal for fixed 50 GHz grid systems; 2) superchannel transmission at date rates of up to 1.15 Tb/s and spectral efficiencies of up to 7.68 b/s/Hz. The TDHQ signal is generated using high-speed digital-to-analog converters (DACs) at the transmitter, and low-complexity digital signal processing (DSP) is proposed for processing the TDHQ signals at the receiver. Moreover, the nonlinearity tolerance of hybrid QAM signals with different configurations is investigated.

The effectiveness of stackelberg strategies and tolls for network congestion games

It is well known that in a network with arbitrary (convex) latency functions that are a function of edge traffic, the worst-case ratio, over all inputs, of the system delay caused due to selfish behavior versus the system delay of the optimal centralized solution may be unbounded even if the system consists of only two parallel links. This ratio is called the price of anarchy (PoA). In this article, we investigate ways by which one can reduce the performance degradation due to selfish behavior. We investigate two primary methods (a) Stackelberg routing strategies , where a central authority, for example, network manager, controls a fixed fraction of the flow, and can route this flow in any desired way so as to influence the flow of selfish users; and (b) network tolls , where tolls are imposed on the edges to modify the latencies of the edges, and thereby influence the induced Nash equilibrium. We obtain results demonstrating the effectiveness of both Stackelberg strategies and tolls in controlling the price of anarchy. For Stackelberg strategies, we obtain the first results for nonatomic routing in graphs more general than parallel-link graphs, and strengthen existing results for parallel-link graphs. (i) In series-parallel graphs, we show that Stackelberg routing reduces the PoA to a constant (depending on the fraction of flow controlled). (ii) For general graphs, we obtain latency-class specific bounds on the PoA with Stackelberg routing, which give a continuous trade-off between the fraction of flow controlled and the price of anarchy. (iii) In parallel-link graphs, we show that for any given class L of latency functions, Stackelberg routing reduces the PoA to at most α + (1-α)ċρ( L ), where α is the fraction of flow controlled and ρ( L ) is the PoA of class L (when α = 0). For network tolls, motivated by the known strong results for nonatomic games, we consider the more general setting of atomic splittable routing games. We show that tolls inducing an optimal flow always exist, even for general asymmetric games with heterogeneous users, and can be computed efficiently by solving a convex program . This resolves a basic open question about the effectiveness of tolls for atomic splittable games. Furthermore, we give a complete characterization of flows that can be induced via tolls.

Visual working memory for amplitude-modulated shapes

We investigated the trade-off between capacity and precision in visual working memory with two different tasks: delayed discrimination and recall. The stimuli were radial frequency patterns that require global pooling of local visual features. The thresholds in delayed amplitude discrimination were measured with a two-interval, forced-choice setup using the Quest procedure. In the recall experiment, the observers' task was to adjust the amplitude of a probe to match the amplitude of a cued item. For one item, the amplitude thresholds were low (0.01-0.05) and the adjustments precise (standard deviations, 0.03-0.05). As the number of items increased from one to six, there was a linear, 6-to-14-fold increase in the thresholds (0.14-0.29) and a 1.5-to-3-fold increase in the standard deviations (0.06-0.11). No sudden or complete breakdown in performance was observed for any subject. The results show a continuous trade-off between memory capacity and precision; six items can be discriminated with the same performance level (75% correct) as one item if the difference between the stimuli is set accordingly. Thus, the stimulus discriminability determines the capacity of visual working memory, and the trade-off between the capacity and precision is linear.

Achieving a Continuous Diversity-Complexity Tradeoff in Wireless MIMO Systems via Pre-Equalized Sphere-Decoding

In multiple-input multiple-output (MIMO) spatial multiplexing systems, maximum-likelihood (ML) detection achieves maximum diversity at the cost of high computational complexity. In contrast, low-complexity alternatives like zero-forcing (ZF) detection suffer from a significant diversity loss. In this paper, we present a novel detection scheme that allows a continuous tradeoff between diversity order and complexity reduction. The proposed detector consists of two stages: the first stage is a linear pre-equalizer that partially eliminates MIMO interference and improves the channel condition number; the second stage is a mismatched ML detector on the residual channel that ignores the correlation introduced by the equalization stage. This second stage is implemented using a variant of the sphere decoder. ML and ZF detection are extreme special cases of our novel scheme. We give insights into the main effects that determine performance and we provide an explicit expression for the diversity order achieved with the proposed detector. Simulation results confirm that our scheme allows to trade diversity for complexity savings in a continuous manner.