First-order Dependencies Research Articles

Doubly strongly first-order dependencies

Abstract Team Semantics is a generalization of Tarskian Semantics in which formulas are satisfied with respect to sets of assignments, called teams. In this semantics, it is possible to add new atoms and connectives expressing dependencies between possible values of variables. Some of the resulting logics are more expressive than first-order logic while others are not. I study the (relativizable) atoms and families of atoms that do not increase the expressive power of first-order logic when they and their complements are added to it, separately or jointly, calling them doubly strongly first-order dependencies and finding a characterization for them.

Hexa-Fe(III) Carboxylate Complexes Facilitate Aerobic Hydrocarbon Oxidative Functionalization: Rh Catalyzed Oxidative Coupling of Benzene and Ethylene to Form Styrene.

Fe(II) carboxylates react with dioxygen and carboxylic acid to form Fe6(μ-OH)2(μ3-O)2(μ-X)12(HX)2 (X = acetate or pivalate), which is an active oxidant for Rh-catalyzed arene alkenylation. Heating (150-200 °C) the catalyst precursor [(η2-C2H4)2Rh(μ-OAc)]2 with ethylene, benzene, Fe(II) carboxylate, and dioxygen yields styrene >30-fold faster than the reaction with dioxygen in the absence of the Fe(II) carboxylate additive. It is also demonstrated that Fe6(μ-OH)2(μ3-O)2(μ-X)12(HX)2 is an active oxidant under anaerobic conditions, and the reduced material can be reoxidized to Fe6(μ-OH)2(μ3-O)2(μ-X)12(HX)2 by dioxygen. At optimized conditions, a turnover frequency of ∼0.2 s-1 is achieved. Unlike analogous reactions with Cu(II) carboxylate oxidants, which undergo stoichiometric Cu(II)-mediated production of phenyl esters (e.g., phenyl acetate) as side products at temperatures ≥150 °C, no phenyl ester side product is observed when Fe carboxylate additives are used. Kinetic isotope effect experiments using C6H6 and C6D6 give k H/k D = 3.5(3), while the use of protio or monodeutero pivalic acid reveals a small KIE with k H/k D = 1.19(2). First-order dependencies on Fe(II) carboxylate and dioxygen concentration are observed in addition to complicated kinetic dependencies on the concentration of carboxylic acid and ethylene, both of which inhibit the reaction rate at a high concentration. Mechanistic studies are consistent with irreversible benzene C-H activation, ethylene insertion into the formed Rh-Ph bond, β-hydride elimination, and reaction of Rh-H with Fe6(μ-OH)2(μ3-O)2(μ-X)12(HX)2 to regenerate a Rh-carboxylate complex.

Validating functional redundancy with mixed generative adversarial networks

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query processing. While it has been studied for four decades, existing works on data redundancy mostly focus on syntactic formulations such as normal forms and functional dependencies, which lead to intractable discovery problems. In this work, we propose a new concept, namely functional redundancy, that overcomes the limitations of functional dependencies, especially on continuous data. We design and develop efficient algorithms based on generative adversarial networks to validate any functional redundancy without heavily depending on the number of attributes and the number of tuples like functional dependencies. The core idea is to use the imputation power of generative adversarial networks to model any semantic dependencies between attributes. Extensive experiments on different real-world and synthetic datasets show that our approach outperforms representative baselines, is applicable for first-order and high-order dependencies, and is extensible for different types of data.

Sequential dependencies of responses in a questionnaire survey and their effects on the reliability and validity of measurement.

The reliability and validity of measurements in questionnaire surveys are affected by the survey design. Previous studies have discussed whether the items on a multidimensional scale should be grouped by subscale or randomized to increase the reliability and validity of the measurement. However, it is not clear what effect item order has on the true reliability and validity and whether the order causes bias in these estimates. Furthermore, it is not clear whether there are sequential dependencies of responses, which refers to the effect of past responses on current responses and, if so, what effects that they have on reliability, coefficient α, and validity. Study 1 empirically revealed that each respondent's previous response had a positive effect on his or her current response. In Study 2, we conducted a theoretical investigation and found that the first-order positive sequential dependencies of responses reduced the reliability, coefficient α, and validity of the subscales. In addition, sequential dependencies were found to render these indicators higher when the items were grouped than when they were randomized and caused an overestimation of reliability by coefficient α. Study 3 confirmed the effects of sequential dependencies and item order on coefficient α using real data. Thus, this study showed the potential impact of sequential dependencies of responses on psychological measurements.

Overtopping volume of impulse waves in glacier lakes: Experimental and numerical investigation using rigid dams

Glacier lake outburst floods (GLOFs) that result from the breaching of moraine dams are typical disasters in high mountain regions of the Tibetan Plateau, which endanger vital infrastructures such as the Sichuan-Tibet Railway and Highway. Breaching can be caused by tsunami-type waves, resulting from glaciers calving and falling into impounded lakes, which overtop and destroy their natural boundaries. In this work, we develop an equation for the overtopping volume based on the simplified geometry of fluids running up an incline. Specifically, we propose that overtopping can be viewed in terms of the run-up of solitary waves impacting a structure from which relationships with the dam and wave properties can be established. This equation is tested and verified against physical experiments and numerical simulations of waves overtopping rigid, impermeable triangular structures which serve as idealized natural dams. The proposed equation is found to adequately account for the first-order dependencies of the overtopping volume on the different dam and wave properties. We extend the applicability of this analysis to erodible dams made up of glacier tills, wherein we show the basic dependence of the overtopping volume on the erodibility of the dams and flood scales.

Closed-Form Formulas for Automated Design of SiC-Based Phase-Shifted Full Bridge Converters in Charger Applications

Phase-Shifted Full Bridge (PSFB) topology in its four-diode variant is the choice with the lowest part count in applications that demand high power, high voltage, and galvanic isolation, such as in Electric Vehicle (EV) chargers. Even though the topology is prevalent in power electronics applications, no single, unified analytical model has been proposed for the design process of four-diode PSFB converters. As a result, engineers must rely on simulations and empirical results obtained from previously built converters when selecting components to properly match the DC source voltage level with the DC load voltage requirements. In this work, the authors provide a design-oriented analysis approach for obtaining the output voltage and semiconductor current values, ready for implementation in a spreadsheet- or MATLAB-type software to automate design optimization. The proposed formulas account for all the first-order nonlinear dependencies by considering the impact of each of the following eight design parameters: DC-link voltage, load resistance, phase-shift ratio, switching frequency, transformer turns ratio, magnetizing inductance, series inductance, and output inductance. The results are verified through experiments at the power level of 10 kW and the DC-link voltage level of 800 V by using a grid simulator and a SiC-based two-level Active Front End (AFE) with a DC–DC stage based on the PSFB topology. The accuracy of the output voltage formula is determined to be around 99.6% in experiments and 100.0% in simulations. Based on this exact model, an automated design procedure for high-power high-voltage SiC-based PSFB converters is developed. By providing the desired DC-link voltage, output voltage, output power, output current ripple factor, maximum temperatures, and semiconductor and heatsink databases, the algorithm calculates a set of feasible designs and points to the one with the lowest semiconductor losses, dimensions, or cost.

Towards Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning

AbstractIn the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multi-dimensional statistical modeling approach. Amongst different machine learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created which can be deployed in the future for flash flood forecasting. The results confirm that although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of sub-basin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins which could most benefit from distributed hydrologic modeling.

A Revised Pseudo-Second-Order Kinetic Model for Adsorption, Sensitive to Changes in Adsorbate and Adsorbent Concentrations.

The development of new adsorbent materials for the removal of toxic contaminants from drinking water is crucial toward achieving the United Nations Sustainable Development Goal 6 (clean water and sanitation). The characterization of these materials includes fitting models of adsorption kinetics to experimental data, most commonly the pseudo-second-order (PSO) model. The PSO model, however, is not sensitive to parameters such as adsorbate and adsorbent concentrations (C0 and Cs) and consequently is not able to predict changes in performance as a function of operating conditions. Furthermore, the experimental conditionality of the PSO rate constant, k2, can lead to erroneous conclusions when comparing literature results. In this study, we analyze 103 kinetic experiments from 47 literature sources to develop a relatively simple modification of the PSO rate equation, yielding . Unlike the original PSO model, this revised rate equation (rPSO) provides the first-order and zero-order dependencies upon C0 and Cs that we observe empirically. Our new model reduces the residual sum of squares by 66% when using a single rate constant to model multiple adsorption experiments with varying initial conditions. Furthermore, we demonstrate how the rPSO rate constant k' is more appropriate for comparing literature studies, highlighting faster kinetics in the adsorption of arsenic onto alumina versus iron oxides. This revised rate equation should find applications in engineering studies, especially since the rPSO rate constant k' does not show a counter-intuitive inverse relationship with increasing reaction rates when C0 is increased, unlike the PSO rate constant k2.

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

BackgroundAlthough vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.MethodsThe influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.ResultsThe results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.ConclusionsThis study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors’ incidence and the transmission relationships.

Capturing Joint Label Distribution for Multi-Label Classification Through Adversarial Learning

Label correlations are important for multi-label learning. Although current multi-label learning approaches can exploit first-order, second-order, and high-order label dependencies, they fail to exploit complete label correlations, which are included in the joint label distribution of the ground truth labels. However, directly modeling the complex and unknown joint label distribution is very challenging, if not impossible. In this paper, we propose an adversarial learning framework to enforce similarity between joint distribution of the ground truth multi-labels and the predicted multiple labels. Specifically, the proposed multi-label learning method includes a multi-label classifier and a label discriminator. The classifier minimizes error between predicted labels and corresponding ground truth labels and gives the discriminator room for error. The object of the discriminator is to distinguish the predicted labels from the ground truth labels. The classifier and discriminator are trained simultaneously through an alternate process. By adversarial learning, the joint label distribution of the predicted multi-labels converges to the joint distribution inherent in the ground truth multi-labels, and thus boosts the performance of multi-label learning as demonstrated in the experiments on 11 benchmark databases.

Temporal dynamics of saccades explained by a self-paced process

Sensory organs are thought to sample the environment rhythmically thereby providing periodic perceptual input. Whisking and sniffing are governed by oscillators which impose rhythms on the motor-control of sensory acquisition and consequently on sensory input. Saccadic eye movements are the main visual sampling mechanism in primates, and were suggested to constitute part of such a rhythmic exploration system. In this study we characterized saccadic rhythmicity, and examined whether it is consistent with autonomous oscillatory generator or with self-paced generation. Eye movements were tracked while observers were either free-viewing a movie or fixating a static stimulus. We inspected the temporal dynamics of exploratory and fixational saccades and quantified their first-order and high-order dependencies. Data were analyzed using methods derived from spike-train analysis, and tested against mathematical models and simulations. The findings show that saccade timings are explained by first-order dependencies, specifically by their refractory period. Saccade-timings are inconsistent with an autonomous pace-maker but are consistent with a “self-paced” generator, where each saccade is a link in a chain of neural processes that depend on the outcome of the saccade itself. We propose a mathematical model parsimoniously capturing various facets of saccade-timings, and suggest a possible neural mechanism producing the observed dynamics.

Fe hydroxyphosphate precipitation and Fe(II) oxidation kinetics upon aeration of Fe(II) and phosphate-containing synthetic and natural solutions

Exfiltration of anoxic Fe-rich groundwater into surface water and the concomitant oxidative precipitation of Fe are important processes controlling the transport of phosphate (PO4) from agricultural areas to aquatic systems. Here, we explored the relationship between solution composition, reaction kinetics, and the characteristics of the produced Fe hydroxyphosphate precipitates in a series of aeration experiments with anoxic synthetic water and natural groundwater. A pH stat device was used to maintain constant pH and to record the H+ production during Fe(II) oxidation in the aeration experiments in which the initial aqueous P/Fe ratios ((P/Fe)ini), oxygen concentration and pH were varied. In general, Fe(II) oxidation proceeded slower in the presence of PO4 but the decrease of the PO4 concentration during Fe(II) oxidation due to the formation of Fe hydroxyphosphates caused additional deceleration of the reaction rate. The progress of the reaction could be described using a pseudo-second-order rate law with first-order dependencies on PO4 and Fe(II) concentrations. After PO4 depletion, the Fe(II) oxidation rates increased again and the kinetics followed a pseudo-first-order rate law. The first-order rate constants after PO4 depletion, however, were lower compared to the Fe(II) oxidation in a PO4-free solution. Hence, the initially formed Fe hydroxyphosphates also affect the kinetics of continuing Fe(II) oxidation after PO4 depletion. Presence of aqueous PO4 during oxidation of Fe(II) led to the formation of Fe hydroxyphosphates. The P/Fe ratios of the precipitates ((P/Fe)ppt) and the recorded ratio of H+ production over decrease in dissolved Fe(II) did not change detectably throughout the reaction despite a changing P/Fe ratio in the solution. When (P/Fe)ini was 0.9, precipitates with a (P/Fe)ppt ratio of about 0.6 were formed. In experiments with (P/Fe)ini ratios below 0.6, the (P/Fe)ppt decreased with decreasing (P/Fe)ini and pH value. Aeration experiments with natural groundwater showed no principal differences in Fe(II) oxidation kinetics and in PO4 immobilisation dynamics compared with synthetic solutions with corresponding P/Fe ratio, pH and oxygen pressure. However, aeration of groundwater with relative high DOC concentrations and a low salinity lead to P-rich Fe colloids that were colloidally stable. The formation of a Fe hydroxyphosphate phase with a molar P/Fe ratio of 0.6 can be used for predictive modelling of PO4 immobilisation upon aeration of pH-neutral natural groundwater with an (P/Fe)ini ratio up to 1.5. These findings provide a solid basis for further studies on transport and bioavailability of phosphorus in streams, ditches and channels that receive anoxic Fe-rich groundwater.

The effect of presentation rate on implicit sequence learning in aging

ABSTRACTImplicit sequence learning is thought to be preserved in aging when the to-be learned associations are first-order; however, when associations are second-order, older adults (OAs) tend to experience deficits as compared to young adults (YAs). Two experiments were conducted using a first (Experiment 1) and second-order (Experiment 2) serial-reaction time task. Stimuli were presented at a constant rate of either 800 milliseconds (fast) or 1200 milliseconds (slow). Results indicate that both age groups learned first-order dependencies equally in both conditions. OAs and YAs also learned second-order dependencies, but the learning of lag-2 information was significantly impacted by the rate of presentation for both groups. OAs showed significant lag-2 learning in slow condition while YAs showed significant lag-2 learning in the fast condition. The sensitivity of implicit sequence learning to the rate of presentation supports the idea that OAs and YAs different processing speeds impact the ability to build complex associations across time and intervening events.

Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data.

BackgroundStatistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question. Recent high-throughput sequencing methods have provided data sets of sufficient size and quality for studying the benefits of more complex models. However, learning more complex models typically entails the danger of overfitting, and while model classes that dynamically adapt the model complexity to data have been developed, effective model selection is to date only possible for fully observable data, but not, e.g., within de novo motif discovery.ResultsTo address this issue, we propose a stochastic algorithm for performing robust model selection in a latent variable setting. This algorithm yields a solution without relying on hyperparameter-tuning via massive cross-validation or other computationally expensive resampling techniques. Using this algorithm for learning inhomogeneous parsimonious Markov models, we study the degree of putative higher-order intra-motif dependencies for transcription factor binding sites inferred via de novo motif discovery from ChIP-seq data. We find that intra-motif dependencies are prevalent and not limited to first-order dependencies among directly adjacent nucleotides, but that second-order models appear to be the significantly better choice.ConclusionsThe traditional PWM model appears to be indeed insufficient to infer realistic sequence motifs, as it is on average outperformed by more complex models that take into account intra-motif dependencies. Moreover, using such models together with an appropriate model selection procedure does not lead to a significant performance loss in comparison with the PWM model for any of the studied transcription factors. Hence, we find it worthwhile to recommend that any modern motif discovery algorithm should attempt to take into account intra-motif dependencies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0797-4) contains supplementary material, which is available to authorized users.

Conditional random field with high-order dependencies for sequence labeling and segmentation

Dependencies among neighboring labels in a sequence are important sources of information for sequence labeling and segmentation. However, only first-order dependencies, which are dependencies between adjacent labels or segments, are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper, we give effcient inference algorithms to handle high-order dependencies between labels or segments in conditional random fields, under the assumption that the number of distinct label patterns used in the features is small. This leads to effcient learning algorithms for these conditional random fields. We show experimentally that exploiting high-order dependencies can lead to substantial performance improvements for some problems, and we discuss conditions under which high-order features can be effective.

Assessing n-order dependencies between critical infrastructures

The protection of critical infrastructures (CI) is a complex task, since it involves the assessment of both internal and external security risk. In the recent literature, methodologies have been proposed that can be used to identify organisation-wise security threats, or even first-order dependency risk (i.e., risk deriving from direct dependencies). However, there is a lack of work in the area of multi-order dependencies, i.e., assessing the cumulative effects of a single incident, on infrastructures that are connected indirectly. In this paper, we propose a method to identify and assess multi-order dependencies. Based on previous work, we utilise existing first-order dependency graphs, in order to assess the effect of a disruption to consequent infrastructures. In this way, it may be possible to identify and prevent security threats of very high impact from a macroscopic view, which would be hard to identify if we only examine first-order dependencies. We also present a scenario, which provides some evidence on the applicability of the proposed approach.

Phrase Parses Reranking Based on Higher-Order Lexical Dependencies

在句法分析中,已有研究工作表明,词汇依存信息对短语结构句法分析是有帮助的,但是已有的研究工作都仅局限于使用一阶的词汇依存信息.提出了一种使用高阶词汇依存信息对短语结构树进行重排序的模型,该模型首先为输入句子生成有约束的搜索空间(例如,N-best 句法分析树列表或者句法分析森林),然后在约束空间内获取高阶词汇依存特征,并利用这些特征对短语结构候选树进行重排序,最终选择出最优短语结构分析树.在宾州中文树库上的实验结果表明,该模型的最高 F1 值达到了 85.74%,超过了目前在宾州中文树库上的最好结果.另外,在短语结构分析树的基础上生成的依存结构树的准确率也有了大幅提升.;The existing works on parsing show that lexical dependencies are helpful for phrase tree parsing.However, only first-order lexical dependencies have been employed and investigated in previous research. Thispaper proposes a novel method for employing higher-order lexical dependencies for phrase tree evaluation. Themethod is based on a parse reranking framework, which provides a constrained search space (via N-best lists orparse forests) and enables the parser to employ relatively complicated lexical dependency features. The models areevaluated on the UPenn Chinese Treebank. The highest F1 score reaches 85.74% and has outperformed allpreviously reported state-of-the-art systems. The dependency accuracy of phrase trees generated by the parser hasbeen significantly improved as well.

Customer credit scoring based on HMM/GMDH hybrid model

Hidden Markov model (HMM) has made great achievements in many fields such as speech recognition and engineering. However, due to its assumption of state conditional independence between observations, HMM has a very limited capacity for recognizing complex patterns involving more than first-order dependencies in customer relationships management. Group Method of Data Handling (GMDH) could overcome the drawbacks of HMM, so we propose a hybrid model by combining the HMM and GMDH to score customer credit. There are three phases in this model: training HMM with multiple observations, adding GMDH into HMM and optimizing the hybrid model. The proposed hybrid model is compared with other exiting methods in terms of average accuracy, Type I error, Type II error and AUC. Experimental results show that the proposed method has better performance than HMM/ANN in two credit scoring datasets. The implementation of HMM/GMDH hybrid model allows lenders and regulators to develop techniques to measure customer credit risk.

A runs-test algorithm: contingent reinforcement and response run structures.

Four rats' choices between two levers were differentially reinforced using a runs-test algorithm. On each trial, a runs-test score was calculated based on the last 20 choices. In Experiment 1, the onset of stimulus lights cued when the runs score was smaller than criterion. Following cuing, the correct choice was occasionally reinforced with food, and the incorrect choice resulted in a blackout. Results indicated that this contingency reduced sequential dependencies among successive choice responses. With one exception, subjects' choice rule was well described as biased coin flipping. In Experiment 2, cuing was removed and the reinforcement criterion was changed to a percentile score based on the last 20 reinforced responses. The results replicated those of Experiment 1 in successfully eliminating first-order dependencies in all subjects. For 2 subjects, choice allocation was approximately consistent with nonbiased coin flipping. These results suggest that sequential dependencies may be a function of reinforcement contingency.

Effects of grammar complexity on artificial grammar learning

The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.