Implicit Dependency Research Articles

LEISN: A long explicit–implicit spatio-temporal network for traffic flow forecasting

Recent studies have shown that it is necessary to further improve the prediction accuracy of complex and dynamic traffic flow from two angles: temporal dependence and spatial dependence. Although many spatio-temporal networks are extracting features from both temporal and spatial dependencies, there are still two problems that need to be addressed. Firstly, existing methods often focus on modeling local spatial dependence relationship between adjacent nodes, but neglect non-local spatial dependence relationship between distant nodes. Secondly, while recent work has explored various methods such as convolutional and recurrent neural networks for modeling temporal dependence, they may not fully capture the long-term temporal dependence in traffic data. Therefore, addressing the challenges of long-term dependency, explicit spatial dependency, and implicit spatial dependency, this paper proposes a Long-term Explicit–Implicit Spatio-Temporal Network (LEISN) for traffic flow prediction. A Long-term Dependency Module has been designed to store hidden states generated from multiple previous time steps, facilitating the transmission of long-term features. Based on this, two graph convolution-based spatial feature extraction branches are designed to extract explicit spatial features based on the adjacency matrix generated by spatial topology and implicit spatial features based on the implicit adjacency matrix generated by trend similarity, respectively. Then all spatio-temporal features are fused to produce the next state. Additionally, a new encoder framework, LENSI-ED, was proposed based on LEISN. Comparative experiments are conducted on four datasets, and the results showed that our model has advantages over existing methods. The proposed model addresses the issues of local and non-local spatial dependence relationships and long-term temporal dependence in traffic flow forecasting.

Suicidality and Implicit Dependency in Child and Adolescent Inpatients With Histories of Trauma: Moderating Effect of Gender.

There is lack of empirical findings on a direct link between suicidality and dependency in youth. This is particularly relevant for children and adolescents with a trauma history, since traumatization is a well-established risk factor for suicidality in this population. Research on dependency predominantly uses self-report assessments, which may be susceptible to biases. In this study, performance-based interpersonal dependency scores in inpatient children and adolescents with trauma history were compared with patients' suicidal behavior (suicidal ideation and suicidal attempts) as derived from chart records. The results showed a gender effect. High dependency scores were associated with higher suicidal ideation for girls and with lower suicidal attempts for boys. These findings demonstrate that a relationship between dependency and suicidality for hospitalized traumatized youth is impacted by gender.

Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions

Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships’ flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations.

Reporting of experimental viscosity data and modeling of transport properties of 1,1,1,3,3,3-hexafluoro-2-methoxypropane (HFE-356mmz) using extended corresponding states (ECS) and residual entropy scaling (RES) technique

As a novel low GWP working fluid 1,1,3,3,3-hexafluoro-2-methoxypropane (HFE-356mmz) is regarded as a promising candidate for high-temperature heat pumps and organic Rankine cycle. Recently, the experimental kinematic viscosity and thermal conductivity data of HFE-356mmz were published in the literature. However, at the time of that publication it was not possible to develop any mathematical model of the transport properties due to unavailability of an equation of state. In this work, we report dynamic viscosity data of HFE-356mmz together with mathematical models for dynamic viscosity and thermal conductivity using the extended corresponding states (ECS) and the residual entropy scaling (RES) technique. With R134a used as the reference fluid for ECS method, the transport shape factors and the other adjustable parameters were calculated by utilizing latest multi-parameter Helmholtz-energy-explicit-type equations of state for HFE-356mmz. Furthermore, slight modification of the original RES method has been adopted that removes the implicit dependency on the tuned ECS method. By using the adjustable parameters, the tuned ECS and RES transport equations can represent the experimental data within reported uncertainties. The average absolute deviations (AAD) for viscosity and thermal conductivity were found to be 0.85% and 0.76% using the tuned ECS and 0.83% and 0.44% using the modified RES method respectively.

Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery

Deep learning (DL) approaches based on convolutional encoder–decoder networks have shown promising results in bitemporal change detection. However, their performance is limited by insufficient contextual information aggregation because they cannot fully capture the implicit contextual dependency relationships among feature maps at different levels. Moreover, harvesting long-range contextual information typically incurs high computational complexity. To circumvent these challenges, we propose multilevel deformable attention-aggregated networks (MLDANets) to effectively learn long-range dependencies across multiple levels of bitemporal convolutional features for multiscale context aggregation. Specifically, a multilevel change-aware deformable attention (MCDA) module consisting of linear projections with learnable parameters is built based on multihead self-attention (SA) with a deformable sampling strategy. It is applied in the skip connections of an encoder–decoder network taking a bitemporal deep feature hypersequence (BDFH) as input. MCDA can progressively address a set of informative sampling locations in multilevel feature maps for each query element in the BDFH. Simultaneously, MCDA learns to characterize beneficial information from different spatial and feature subspaces of BDFH using multiple attention heads for change perception. As a result, contextual dependencies across multiple levels of bitemporal feature maps can be adaptively aggregated via attention weights to generate multilevel discriminative change-aware representations. Experiments on very-high-resolution (VHR) datasets verify that MLDANets outperform state-of-the-art change detection approaches with dramatically faster training convergence and high computational efficiency.

Multi-Source and Temporal Attention Network for Probabilistic Wind Power Prediction

The temporal dependencies of wind power are significant to be involved in the modeling of short-term wind power forecasts. However, different time series inputs will contribute differently to the forecasting performance and bring in challenges to the selection of the relevant driving information. In this paper, a Multi-Source and Temporal Attention Network (MSTAN) is proposed for short-term wind power probabilistic prediction. The MSTAN model introduces multi-source NWP and makes three specific designs to improve prediction performance. Firstly, a novel multi-source variable attention module is proposed to select the driving variables of NWP. Secondly, a temporal attention module is used to capture the implicit temporal dependency hidden in the historical measurements and multi-source NWP sequence. Thirdly, the residual module is wrapped in MSTAN to skip some unnecessary nonlinear transformations and provide adaptive complexity to the entire model. After training, multi-horizon density forecasts for the next 48 hours are yielded by MSTAN. The MSTAN is compared with state-of-the-art machine learning schemes in the wind power forecasting system using the operation data from 3 wind farms. We demonstrate that MSTAN outperforms other counterparts on both deterministic and probabilistic prediction. The structure design scheme of MSTAN has been proven effective.

Surface roughness mediated specularity parameter of thin Cu films

Roughness-induced resistivity variation of thin metal films is conveniently described by the Fuchs–Sondheimer model, where the phenomenological parameter p is used to quantify the extent of specular scattering at surfaces. However, p is a lumped parameter and does not include microscopic information that characterizes roughness, viz., auto-correlation function, root-mean-square height, and correlation length. In this work, we extract these roughness parameters for Cu films of thickness ranging from 31 nm to 95 nm. We find that the roughness–roughness correlation function is an exponential with a characteristic correlation length that increases monotonically with the film thickness. Using this, we predict the roughness parameter-dependent specularity coefficient, which has an implicit thickness dependency. This alters the resistivity scaling compared to the prevailing model of resistivity scaling, where p is assumed to be a constant.

Effect of Wind Speed and Leads on Clear-Sky Cooling over Arctic Sea Ice during Polar Night

Abstract A simple analytical model of the atmospheric boundary layer (ABL) coupled to sea ice is presented. It describes clear-sky cooling over sea ice during polar night in the presence of leads. The model solutions show that the sea ice concentration and wind speed have a strong impact on the thermal regime over sea ice. Leads cause both a warming of the ABL and an increase of stability over sea ice. The model describes a sharp ABL transition from a weakly stable coupled state to a strongly stable decoupled state when wind speed is decreasing. The threshold value of the transition wind speed is a function of sea ice concentration. The decoupled state is characterized by a large air–surface temperature difference over sea ice, which is further increased by leads. In the coupled regime, air and surface temperatures increase almost linearly with wind speed due to warming by leads and also slower cooling of the ABL. The cooling time scale shows a nonmonotonic dependency on wind speed, being lowest for the threshold value of wind speed and increasing for weak and strong winds. Theoretical solutions agree well with results of a more realistic single-column model and with observations performed at the three Russian “North Pole” drifting stations (NP-35, -37, and -39) and at the Surface Heat Budget of the Arctic Ocean ice camp. Both modeling results and observations show a strong implicit dependency of the net longwave radiative flux at the surface on wind speed.

Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning.

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance the Bayesian Network Classifier BNC, which is independent and complementary to BNC learned from training data . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNC and BNC. Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.

Geographical Hidden Markov Tree

Given a spatial raster framework with explanatory feature layers, a spatial contextual layer (e.g., a potential field), as well as a set of training samples with class labels, the spatial prediction problem aims to learn a model that can predict a class layer. The problem is important in societal applications such as flood extent mapping for disaster response and national water forecasting, but is challenging due to the noise, obstacles, and heterogeneity in feature maps, implicit spatial dependency between locations based on the contextual layer (e.g., gradient directions on a potential field), and the large number of sample locations. Existing work often assumes undirected spatial dependency, or directed dependency with a total order, and thus cannot reflect complex directed dependency with a partial order. In contrast, we recently proposed geographical hidden Markov tree, a probabilistic graphical model that generalizes the common hidden Markov model from a one-dimensional sequence to a two-dimensional map. Partial order class dependency is incorporated in the hidden class layer with a reverse tree structure. We also investigated computational algorithms for reverse tree construction, model parameter learning and class inference. This paper extends our recent model with overlaying class nodes between observation nodes and underlying hidden class nodes. The additional overlaying class layer makes the model more robust to large scale feature obstacles. We also proposed corresponding learning and inference methods. Extensive evaluations on real world datasets show that our models outperform multiple baselines in flood mapping applications, our algorithms are scalable on large data sizes, and the proposed extension enhances classification performance.

Observability and sensitivity analysis of lightcurve measurement models for use in space situational awareness

ABSTRACTThis paper deals with the observability and sensitivity analysis of the lightcurve measurement for use in the estimation of instantaneous pose (orientation) and shape geometry. Since several reflectance models exist that are typically used in obtaining a synthetic lightcurve measurement, they are compared to assess the observability or/and ‘sensitivity’ to determine the ‘best model’ for use. These measurement models are nonlinear with implicit and non-trivial dependency on the states; a numerical Jacobian as well as an unscented Kalman filter derived observation matrix are synthesized for each choice of the measurement model and compared. As the linearization is about an attitude (orientation) trajectory, distinct cases are considered to elaborate the results. For the cases evaluated here, it is shown that the attitude and shape/size can indeed be estimated from the lightcurve, but this is also dependent upon the initial conditions (subsequent attitude trajectory). Linear observability analysis of a discretized system is also performed with respect to the attitude states and shape/size parameters using the singular value decomposition of the observability matrix synthesized for a batch of measurements. The results are summarized for various initial conditions of the resident space object's attitude and angular velocity states.

Concept coupling learning for improving concept lattice-based document retrieval

The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections.

Causal Consistency for Data Stores and Applications as They are

There have been proposed protocols to achieve causal consistency with a distributed data store that does not make safety guarantees. Such protocols work with an unmodified data store if it is implemented as middleware or a shim layer while it can be implemented inside a data store. But the middleware approach has required modifications to applications. Applications have to explicitly specify data dependency to be managed. Our Letting-It-Be protocol to the contrary, handles all implicit dependency naturally resulting from data accesses even though it is implemented as middleware. Our protocol does not require any modifications to either data stores or applications. It works with them as they are. It trades performance for the merit to some extent. Throughput declines from a data store alone were 21% in the best case and 78% in the worst case without multi-level management of dependency graph, which is a performance optimization technique.

Software cybernetics in BPM: Modeling software behavior as feedback for evolution by a novel discovery method based on augmented event logs

Interpret business process management (BPM) with the view of cybernetics.Discuss the relation between software cybernetics and BPM.Analyze the role of process discovery in software cybernetics in context of BPM.Propose a novel process discovery method based on a kind of augmented event logs.All implicit places and implicit dependencies can be found. Business Process Management (BPM) is a quickly developing management theory in recent years. The goal of BPM is to improve corporate performance by managing and optimizing the businesses process in and among enterprises. The goal is easier to achieve with the closed-loop feedback mechanism from business process execution to redesign in BPM life cycle, where the business process itself and the set of activities in BPM are viewed as a controlled object and a controller respectively. In this feedback control system, process mining plays an important role in generating feedback of process execution for redesign. However, the existing discovery methods cannot mine certain special structures from execution logs (e.g., implicit dependency, implicit place and short loops) correctly and their mining efficiencies cannot meet the requirements of online process mining. In this paper, we propose a novel discovery method to overcome these challenges based on a kind of augmented event log that will also bring new research directions for process discovery. A case study is presented for introducing how the mined model can be used in business process evolution. Results of experiments are described to show the improvements of the proposed algorithm compared with others.

Adaptive Epidemic Dynamics in Networks

Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily accommodated in the biological epidemic models. In this article, we initiate the study of such a perspective, namely that of adaptive defense against epidemic spreading in arbitrary networks. More specifically, we investigate a nonhomogeneous Susceptible-Infectious-Susceptible (SIS) model where the model parameters may vary with respect to time. In particular, we focus on two scenarios we call semi-adaptive defense and fully adaptive defense, which accommodate implicit and explicit dependency relationships between the model parameters, respectively. In the semi-adaptive defense scenario, the model’s input parameters are given; the defense is semi-adaptive because the adjustment is implicitly dependent upon the outcome of virus spreading. For this scenario, we present a set of sufficient conditions (some are more general or succinct than others) under which the virus spreading will die out; such sufficient conditions are also known as epidemic thresholds in the literature. In the fully adaptive defense scenario, some input parameters are not known (i.e., the aforementioned sufficient conditions are not applicable) but the defender can observe the outcome of virus spreading. For this scenario, we present adaptive control strategies under which the virus spreading will die out or will be contained to a desired level.

Semantic concept-enriched dependence model for medical information retrieval

ObjectiveIn medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. DesignStandardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. MeasurementsWe performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. ResultsOur semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept’s explicit importance in the query. ConclusionBy capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance.

Collaboration and nested environmental governance: Scale dependency, scale framing, and cross-scale interactions in collaborative conservation

The problem of fit between social institutions and ecological systems is an enduring challenge in natural resource management and conservation. Developments in the science of conservation biology encourage the management of landscapes at increasingly larger scales. In contrast, sociological approaches to conservation emphasize the importance of ownership, collaboration and stewardship at scales relevant to the individual or local community. Despite the proliferation of initiatives seeking to work with local communities to undertake conservation across large landscapes, there is an inherent tension between these scales of operation. Consequently, questions about the changing nature of effective conservation across scales abound. Through an analysis of three nested cases working in a semiautonomous fashion in the Northern Rocky Mountains in North America, this paper makes an empirical contribution to the literature on nested governance, collaboration and communication across scales. Despite different scales of operation, constituencies and scale frames, we demonstrate a surprising similarity in organizational structure and an implicit dependency between these initiatives. This paper examines the different capacities and capabilities of collaborative conservation from the local to regional to supra regional. We draw on the underexplored concept of ‘scale-dependent comparative advantage’ (Cash and Moser, 2000), to gain insight into what activities take place at which scale and what those activities contribute to nested governance and collaborative conservation. The comparison of these semiautonomous cases provides fruitful territory to draw lessons for understanding the roles and relationships of organizations operating at different scales in more connected networks of nested governance.

Discovering process models from event multiset

The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (λ-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of λ-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions).

Assessing Dependency Using Self-Report and Indirect Measures: Examining the Significance of Discrepancies

The present study addressed convergence between self-report and indirect approaches to assessing dependency. We were moderately successful in validating an implicit measure, which was found to be reliable, orthogonal to 2 self-report instruments, and predictive of external criteria. This study also examined discrepancies between scores on self-report and implicit measures, and has implications for their significance. The possibility that discrepancies themselves are pathological was not supported, although discrepancies were associated with particular personality profiles. Finally, this study offered additional evidence for the relation between dependency and depressive symptomatology and identified implicit dependency as contributing unique variance in predicting past major depression.

Interpersonal Dependency in Borderline Personality Disorder: Clinical Context and Empirical Evidence

Personality theorists and practicing clinicians agree that high levels of interpersonal dependency play a role in Borderline Personality Disorder (BPD), and this link has been codified in several editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM). Although there is widespread agreement that dependency is linked to BPD, there has never been a systematic review of empirical evidence bearing on this issue. This article reviews research in three areas: (1) the comorbidity of Dependent Personality Disorder (DPD) and BPD; (2) the association between trait dependency and BPD; and (3) differences in free-response (i.e., Rorschach) dependency scores in BPD and non-BPD patients. Results support DSM assertions of DPD-BPD comorbidity, confirm that high levels of trait dependency are associated with BPD traits and symptoms, and show that high levels of implicit dependency needs are present in BPD-diagnosed inpatients, but not BPD outpatients. Theoretical, clinical, and empirical implications of these patterns are discussed.