Exact Weights Research Articles

Structural constraints on the emergence of oscillations in multi-population neural networks

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

Structural constraints on the emergence of oscillations in multi-population neural networks.

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

FCVM(i): Integrated FCNP-VWA-MCDM(i) Methods for On-Demand Charging Scheduling in WRSNs

On-demand charging schemes have been recently proposed to make efficient charging schedules of mobile chargers by introducing MCDM methods in wireless rechargeable sensor networks (WRSNs). However, most of the existing schemes use analytic hierarchy process (AHP) or fuzzy AHP (FAHP) of paired ratio scale (PRS) to exaggerate the actual paired difference between multi-criteria, thereby very likely producing misapplications such as inappropriately ranking the charging locations or inaccurately drawing the partial charging time in charging scheduling (CS) of WRSNs. In addition, in case of using FCNP of paired interval scale (PIS) for weight assignment of multi-criteria, weight compensation has not been considered. In particular, it is still unknown which is the best method for integrating FCNP with several MCDM approaches. This paper proposed novel CS methods by integrated FCNP-VWA-MCDM(i) called FCVM(i) which solves all of these problems. The proposed methods first assign the weights to multiple criteria discriminating charging request nodes (cRNs) using FCNP and make compensation of them to be relatively exact weights with VWA. Then, on the basis of these weights, MCDM(i) is used to elect the best proper next charging position. In this way, drawing up the recharging schedule, at the selected charging locations, we decide the reasonable partial charging time using the assigned weights with FCNP-VWA. Extended experiment results prove that the FCVM(1) using TOPSIS gives the best performance among FCVM(i) methods.

Exact weights, path metrics, and algebraic Wasserstein distances

We use weights on objects in an abelian category to define what we call a path metric. We introduce three special classes of weight: those compatible with short exact sequences; those induced by their path metric; and those which bound their path metric. We prove that these conditions are in fact equivalent, and call such weights exact. As a special case of a path metric, we obtain a distance for generalized persistence modules whose indexing category is a measure space. We use this distance to define Wasserstein distances, which coincide with the previously defined Wasserstein distances for one-parameter persistence modules. For one-parameter persistence modules, we also describe maps to and from an interval module, and we give a matrix reduction for monomorphisms and epimorphisms.

On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models

Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction (NWP) in non-Gaussian setting. In this manuscript, two recent versions of particle filters, namely the Localized Adaptive Particle Filter (LAPF) and the Localized Mixture Coefficient Particle Filter (LMCPF) are studied in comparison with the Ensemble Kalman Filter when applied to the popular Lorenz 1963 and 1996 models. As these particle filters showed mixed results in the global NWP system at the German meteorological service (DWD), the goal of this work is to show that the LMCPF is able to outperform the LETKF within an experimental design reflecting a standard NWP setup and standard NWP scores. We focus on the root-mean-square-error (RMSE) of truth minus background, respectively, analysis ensemble mean to measure the filter performance. To simulate a standard NWP setup, the methods are studied in the realistic situation where the numerical model is different from the true model or the nature run, respectively. In this study, an improved version of the LMCPF with exact Gaussian mixture particle weights instead of approximate weights is derived and used for the comparison to the Localized Ensemble Transform Kalman Filter (LETKF). The advantages of the LMCPF with exact weights are discovered and the two versions are compared. As in complex NWP systems the individual steps of data assimilation methods are overlaid by a multitude of other processes, the ingredients of the LMCPF are illustrated in a single assimilation step with respect to the three-dimensional Lorenz 1963 model.

Social Isolation, Social Support, and Loneliness Profiles Before and After Spousal Death and the Buffering Role of Financial Resources.

ObjectivesWe provide new evidence on the profiles of social isolation, social support, and loneliness before and after spousal death for older widows. We also examine the moderating effects of gender and financial resources on changes in social health before and after widowhood.MethodsWe use 19 waves of data from the Household, Income and Labour Dynamics in Australia Survey, including 749 widowed individuals and a comparison group of around 8,000 married individuals. We apply coarsened exact matching weights and control for age and time trends. Local polynomial smoothed plots show the profiles of social health from 3 years pre- to 3 years postspousal death. All analyses were stratified by gender.ResultsSpousal death was strongly associated with increased loneliness for women and men, but also an increase in interactions with friends and family not living with the bereaved. For men, financial resources (both income and asset wealth) provided some protection against loneliness. Spousal death was not associated with changes in social support or participation in community activities.DiscussionWe demonstrate that loneliness is a greater challenge of widowhood than social isolation or a lack of social support. Our findings suggest that interventions focusing only on increasing social interactions are unlikely to alleviate loneliness following spousal death. Moreover, policies that reduce the cost of formal social participation may have limited effectiveness in tackling loneliness, particularly for women. Alternative strategies, such as helping the bereaved form a new sense of identity and screening for loneliness around widowhood by health care workers, could be beneficial.

A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting

Hybridization of individual models emerged as a predominant alternative for increasing accuracy in time series forecasting. The literature is abundant on providing hybrid methods aiming at improving forecasting accuracy and comprehensive pattern recognition. The principle behind all hybrid models' success is improving forecasting accuracy. One of the most widely used combination methods in line with the common objective of the hybridization concept is a parallel approach in which the forecasting model applied on the original time series and the weighted forecasts generate the final hybrid result. In parallel hybridization, the key is how to select the weights of each model. The weighting algorithm plays a significant role and the degree of accuracy in such models directly depends on it. However, the traditional parallel hybrid models developed to enhance forecasting accuracy employing multiple individual models simultaneously, faced some limitations, e.g., missing considering the reliability and generalization criteria of developed hybrid models. From the literature, it is observed that none of the parallel hybrid modes proposed in the literature focused on the improving reliability of the hybrid model to obtain better model generalization for unseen data. Thus, the main objective of this study is to propose a novel class of hybrid models named reliability-based parallel hybridization (RPH). The main goal of RPH methodology is to improve hybrid models' reliability rather than accuracy using a new reliable-based weighting algorithm (RWA). The RWA instead of traditional accuracy-based weighting algorithms in which error measurements are minimized, the reliability of hybrid models is maximized to determine the exact optimum weights of individual models. The RWA computed the optimum weights of forecasts regarding minimizing performance changing of the hybrid model in the validation data set. The RPH model proposed two crucial concepts for the first time: (1) Bringing up reliability in hybridization procedure (2) Proposing a novel reliable-based weighting algorithm to maximize the generalization power of the hybrid model. The bi-component version of the RPH model is proposed in this paper using ARIMA and MLP models. The forecasting power of the proposed PRH constructed based on ARIMA and MLP models is verified using the benchmark data sets e.g. the closing of Standard and Poor's 500 indexes (S&P500), the closing of the Shenzhen Integrated Index (SZII), and the opening of the Dow Jones Industrial Average Index (DJIAI). The experimental results indicate that the forecasting performance of the RPH model is much better than traditional accuracy-based parallel hybrid models.

Assessment of opioid surrogates for colorimetric testing (Part I)

Opioids pose a serious risk to the medical health of abusers, as well as law enforcement officials that come into contact with suspicious materials that may require presumptive testing or handling. Because milligram or less quantities of certain opioids are considered lethal, first responders must be properly trained on the procedures and safe application of presumptive testing kits (e.g. colorimetric). These kits are generally portable, provide rapid results and rely on the chemical substructures of the target compounds. Clearly, incorporating the direct handling of opioids into training on these testing kits would be considered undesirable. Training users with surrogate compounds minimizes this hazard before on-scene deployment. A total of 17 surrogate compounds for heroin, fentanyl and carfentanil were identified, with each of these compounds bearing many of the same functional groups, structural features and approximate or exact molecular weights possessed by the parent opioid. Commercially available colorimetric testing kits were employed to examine the capability of these compounds to effectively serve as surrogates. Of those tested, 13 were considered to be highly effective surrogates for these kits. The most promising surrogates were cut with lactose and acetaminophen in order to assess matrix effects.

Diffusion and Superdiffusion from Hydrodynamic Projections

Hydrodynamic projections, the projection onto conserved charges representing ballistic propagation of fluid waves, give exact transport results in many-body systems, such as the exact Drude weights. Focussing one one-dimensional systems, I show that this principle can be extended beyond the Euler scale, in particular to the diffusive and superdiffusive scales. By hydrodynamic reduction, Hilbert spaces of observables are constructed that generalise the standard space of conserved densities and describe the finer scales of hydrodynamics. The Green–Kubo formula for the Onsager matrix has a natural expression within the diffusive space. This space is associated with quadratically extensive charges, and projections onto any such charge give generic lower bounds for diffusion. In particular, bilinear expressions in linearly extensive charges lead to explicit diffusion lower bounds calculable from the thermodynamics, and applicable for instance to generic momentum-conserving one-dimensional systems. Bilinear charges are interpreted as covariant derivatives on the manifold of maximal entropy states, and represent the contribution to diffusion from scattering of ballistic waves. An analysis of fractionally extensive charges, combined with clustering properties from the superdiffusion phenomenology, gives lower bounds for superdiffusion exponents. These bounds reproduce the predictions of nonlinear fluctuating hydrodynamics, including the Kardar–Parisi–Zhang exponent 2/3 for sound-like modes, the Levy-distribution exponent 3/5 for heat-like modes, and the full Fibonacci sequence.

On stability of maximal entropy OWA operator weights

The maximal entropy OWA operator (MEOWA) weights can be obtained by solving a nonlinear programming problem with a linear constraint for the level of orness. Since the exact MEOWA weights are not known for the general case we can only find approximate solutions. We will prove that the nonlinear programming problem for obtaining MEOWA weights is well-posed: it has a unique solution and each MEOWA weight changes continuously with the initial level of orness. Using the implicit function theorem we will show that MEOWA weights are Lipschitz-continuous functions of the orness level. The stability property of the MEOWA weights under small changes of the orness level guarantees that small rounding errors of digital computation and small errors of measurement of the orness level can cause only a small deviation in MEOWA weights, i.e. every successive approximation method can be applied to the computation of the approximation of the exact MEOWA weights.

Multi-criteria weighted aggregated sum product assessment method for sustainable biomass crop selection problem using single-valued neutrosophic sets

Determining the most optimum biomass crop alternative for biofuel production is a strategic decision due to inherent uncertainty, subjective human mind and involvement of numerous criteria. As the single-valued neutrosophic set (SVNS) is a valuable tool to tackle the uncertain, indeterminate and inconsistent information arises in real-life decision-making problems. Thus, the aim of the present work is to develop a novel weighted aggregated sum product assessment (WASPAS) framework for solving multi-criteria group decision-making (MCGDM) problems with single-valued neutrosophic information. In this framework, single-valued neutrosophic subjective and objective weight integrated approach (SVN-SOWIA) is discussed to estimate the weights of criteria. This method is based on the similarity measure, aggregation operator and score value, which provides more realistic and exact weights. In this regard, new similarity measures are discussed for SVNSs with their desirable characteristics. Further, to illustrate the potentiality and feasibility of the developed approach, a case study of sustainable biomass crop alternative selection is presented under SVNSs context. The sensitivity analysis shows that the alternative ‘Miscanthus’ constantly obtains its top ranking in spite of how criteria weights vary. Lastly, a comparison is made to verify the robustness of the obtained outcome. The result of this work shows that the developed method can suggest more feasible performance while facing multiple influencing factors and input uncertainties, and thus, lends itself to a broader range of applications.

E-mobility from a multi-actor point of view: Uncertainties and their impacts

Germany has introduced measures favorable to the deployment of electric cars, but, so far, their diffusion has been slow. To assess the decarbonization of the transport sector, a wider range of factors and stakeholder priorities must be considered. A multi-criteria approach shows that car users and vehicle manufacturers are likely to resist e-mobility. However, a decision model based on the multi-criteria approach heavily depends on the characteristics of the car categories and on the weights determining stakeholders’ motivations. In practice, neither the exact characteristics nor the exact weights are known exactly. By systematically varying the characteristics and weights, we take these uncertainties into account and test the robustness of the results. Moreover, we identify factors linked to policy measures promoting a particular car technology. Whilst we find that incentivizing the adoption of hybrids is possible, shifting the attitude of car users towards electric vehicles is difficult, since electric vehicles have disadvantages for this stakeholder. Electric utilities support electric vehicles, as they are consistent with their business model whilst government supports them only once ecological and economic concerns gain equal importance. This indicates that new approaches involving penalizing conventional cars may be necessary to the dissemination of electric cars.

Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules.

Background: Various studies have used multiple attribute decision making (MADM) techniques to assess and rank health technologies. The goal of the present study was to prioritize health technologies using various techniques of MADMs in combination with decision rules. Methods: The study is an applied research using multi-attribute decision making (MADM) methods. This study extracted the attributes related to health technology assessment from global literature and experts’ opinions. In this study, two different types of experts were consulted: the first type, including three experts in the field of the decision-making techniques, on the subject of setting priority on health focusing on MADM; and the second one consists of seven experts in the field of HTA, asked about the selection of attributes and determination their importance. Candidate health technologies were individually weighted and ranked using TOPSIS, SAW and VIKOR by the weight and decision matrix. The results obtained from various techniques were combined and ranked using Copeland’s technique to obtain the final ranking of health technologies. To determine HTA type reports, decision rules were defined. All models were designed via MS Excel. Results: This study chose eight technologies according to six tradeoff attributes. These attributes included health benefits at the population level, vulnerable population size, availability of alternative technologies, budget impact, financial protection, and quality of evidence. Their exact weights were 0.25, 0.121, 0.146, 0.132, 0.167 and 0.181, respectively. Also, safety and uncertainty about the cost-effectiveness were considered as the veto and decision rules respectively. Copeland’s method was therefore used to combine the methods: Whereas HT2 (The technology for treating patients suffering from varicose) was ranked the highest priority and HT3 (The palliative method for patients who suffer from various cancers) was ranked the lowest (for preventing from any ethical issue, the exact name of each technology wasn’t mentioned). Conclusion: Finally, in accordance with decision rules which are based on various conditions of "uncertainty about the cost-effectiveness", it is recommended that full health technology assessment report be performed on three technologies, rapid health technology assessment report be performed on four others, and, finally no prioritizing for health technology assessment be made on one of them.

Resource Management of Heterogeneous Cellular Networks With Hybrid Energy Supplies: A Multi-Objective Optimization Approach

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

An Approximate Formula linking Active Share And Tracking Error

The concept of Active Share in portfolio management has gained widespread acceptance since it was proposed in 2009 by Cremers and Petajisto (2009). An explicit relationship between Active Share and the other popular measure of relative risk, Tracking Error, was proposed by Stowe in 2014 but necessitates the full knowledge of all the weights of the portfolio and of the complete covariance matrix of the assets. I derive an approximation in a simple setup where the exact weights are unknown, the pairwise correlation between the returns of the assets is fixed, and the volatility of the assets is described by its mean and variance. This approximation is really a “toy model” and works for the purpose of understanding changes in Tracking error stemming from changes in a portfolio, and to show what the drivers of the link between Active Share and Tracking error are. Using the approximation and the Fundamental Law of Active Management, I compute the total expected return and variance of a fund with a given active share in our setup. A full derivation of the main results is provided in the appendix.

Birth weight and body fat mass in adults assessed by bioimpedance in the ELSA-Brasil study.

Intrauterine life is a critical period for the development of body fat and metabolic risk. This study investigated associations between birth weight and total and truncal body fat in adults. To do so, we analyzed data on 10,011 adults participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) who self-reported birth weight as < 2.5kg, 2.5-4.0kg, or > 4.0kg at baseline (2008-2010) and underwent bioimpedance in the next follow-up visit (2012-2014). Greater mean total and truncal fat mass were seen in those with high birth weight compared with adequate birth weight (p < 0.001) in both sexes (total fat: 25.2 vs. 23.1kg in men and 31.4 vs. 27.7kg in women, and truncal fat: 13.5 vs. 12.4kg in men and 15.9 vs. 14.2kg in women). U-shaped patterns were observed in restricted cubic-spline analyses in the subset of 5,212 individuals reporting exact birth weights, although statistically significant only for those with high birth weight. In the whole sample, in comparing high to adequate birth weight, the latter predicted having a large (> 85 percentile) total and truncal fat mass, respectively: OR = 1.76, 95%CI: 1.37-2.25 (men) and OR = 1.86, 95%CI: 1.42-2.44 (women); OR = 1.68, 95% CI: 1.31-2.16 (men) and OR = 1.73, 95%CI: 1.31-2.28 (women). However, low birth weight predicted having a large (> 85 percentile) % truncal fat only in women (OR = 1.40, 95%CI: 1.03-1.91). In conclusion, in these men and women born in a period in which fetal malnutrition was prevalent, birth weight showed complex, frequently non-linear associations with adult body fat, highlighting the need for interventions to prevent low and high birth weight during pregnancy.

Generic Detectability and Isolability of Topology Failures in Networked Linear Systems

This paper studies the possibility of detecting and isolating topology failures (including link failures and node failures) of a networked system from subsystem measurements, in which subsystems are of fixed high-order linear dynamics, and the exact interaction weights among them are unknown. We prove that in such class of networked systems with the same network topologies, the detectability and isolability of a given topology failure (set) are generic properties, indicating that it is the network topology that dominates the property of being detectable or isolable for a failure (set). We first give algebraic conditions for detectability and isolability of arbitrary parameter perturbations for a lumped plant, and then derive graph-theoretical necessary and sufficient conditions for generic detectability and isolability of topology failures for the networked systems. On the basis of these results, we consider the problems of deploying the smallest set of sensors for generic detectability and isolability. We reduce the associated sensor placement problems to the hitting set problems, which can be effectively solved by greedy algorithms with guaranteed approximation performances.

Monodispersity of Poly(ethylene glycol) Matters for Low-Fouling Coatings.

Poly(ethylene glycol) (PEG) is the most used hydrophilic polymer for low-fouling coatings to minimize nonspecific interactions. The PEG molecular weight or its length as well as surface density are typically considered as the main impacts on the control over low-fouling properties. However, the influence of PEG monodispersity on such investigations is typically ignored and rarely reported. Herein, we report the assembly of PEGylated coatings on planar surfaces and Au nanorods (AuNRs) using a series of monodisperse PEG (Mono-PEG) with exact molecular weights and commonly used polydisperse PEG (Poly-PEG) to investigate the effect of the PEG molecular weight and monodispersity on the low-fouling properties. Nonspecific interactions with proteins or cells on PEGylated planar surfaces can be significantly reduced when Mono-PEG with a molecular weight of 752 Da and above is used, while a higher average molecular weight of 2000 Da is required for Poly-PEG-modified surfaces. Cell association of PEGylated AuNRs further confirms that a low molecular weight of Mono-PEG is sufficient to achieve low-fouling properties. This study provides an insight and general guidance into the engineering of low-fouling surfaces by the appropriate choice based on the PEG monodispersity and/or molecular weights.

Linear integration and lexicographic models of choice: A cue weight learning perspective.

Choosing between options characterized by multiple cues can be a daunting task. People may integrate all information at hand or just use lexicographic strategies that ignore most of it. Notably, integrative strategies require knowing exact cue weights, whereas lexicographic heuristics can operate by merely knowing the importance order of cues. Here we study how using integrative or lexicographic strategies interacts with learning about cues. In our choice-learning-estimation paradigm people first make choices, learning about cues from the experienced qualities of chosen options, and then estimate qualities of new options. We developed delta-elimination (DE), a new lexicographic strategy that generalizes previous heuristics to any type of environment, and compared it to the integrative weighted-additive (WADD) strategy. Our results show that participants learned cue weights, regardless of whether the DE strategy or the WADD strategy described their choices the best. Still, there was an interaction between the adopted strategy and the cue weight learning process: the DE users learned cue weights slower than the WADD users. This work advances the study of lexicographic choice strategies, both empirically and theoretically, and deepens our understanding of strategy selection, in particular the interaction between the strategy used and learning the structure of the environment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Observational Study of the Downstream Consequences of Inappropriate MRI of the Lumbar Spine.

Contrary to guidelines, magnetic resonance imaging (MRI) is often ordered in the first 6 weeks of new episodes of uncomplicated non-specific low back pain. To determine the downstream consequences of early imaging. Retrospective matched cohort study using data from electronic health records of primary care clinics of the U.S. Department of Veterans Affairs. Patients seeking primary care for non-specific low back pain without a red flag condition or an encounter for low back pain in the prior 6 months (N = 405,965). MRI of the lumbar spine within 6 weeks of the initial primary care visit. Covariates included patient demographics, health history in the prior year, and baseline pain. Outcomes were lumbar surgery, prescription opioid use, acute health care costs, and last pain score recorded within 1 year of the index visit. Early MRI was associated with more back surgery (1.48% vs. 0.12% in episodes without early MRI), greater use of prescription opioids (35.1% vs. 28.6%), a higher final pain score (3.99 vs. 3.87), and greater acute care costs ($8082 vs. $5560), p < 0.001 for all comparisons. Reliance on data gathered in normal clinical care and the potential for residual confounding despite the use of coarsened exact matching weights to adjust for baseline differences. The association between early imaging and increased utilization was apparent even in a setting largely unaffected by incentives of fee-for-service care. Reduced imaging cost is only part of the motivation to improve adherence with guidelines for the use of MRI. Early scans are associated with excess surgery, higher costs for other care, and worse outcomes, including potential harms from prescription opioids.