Search Model Research Articles

The Rationality of Search Theory Framework in Late and No MarriageFindings and Discussion Based on the Survey of Marriage Attitudes of College Students in Jiangsu, Zhejiang, and Shanghai

In recent years, China's marriage model has a tendency to converge with the European marriage model, and the tendency of young people to get married late and not to get married has become obvious. College students are the main body of China's marriage behavior in the next ten years, and college students in Jiangsu, Zhejiang, and Shanghai have certain regional characteristics and unique views on late marriage and non-marriage, which are worth studying and exploring. This study will utilize the search theory, the theory as an economic concept in marriage has a certain reference, but the previous research has not been "Jiangsu, Zhejiang, and Shanghai" This region of the youth of late marriage, non-marriage point of view of systematic research, and in the era of information technology college students thinking changes fast update, the previous research has a certain lag. Therefore, the purpose of this paper is to demonstrate the rationality of the search theory in explaining the problem of late marriage and non-marriage among college students in Jiangsu, Zhejiang, and Shanghai, and to try to make discoveries. Through in-depth interviews, this study concludes that the search theory is reasonable in explaining the situation of late marriage and non-marriage among college students in Jiangsu, Zhejiang, and Shanghai, and based on this, it is found that college students in Jiangsu, Zhejiang, and Shanghai have a fixed-sample search behavior in the search process.

Deep code search efficiency based on clustering

AbstractThe deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

Structure of a phosphodiesterase from Streptomyces sanglieri with a novel C-terminal domain

A glycerophosphoethanolamine ethanolaminephosphodiesterase (GPE-EP) from Streptomyces sanglieri hydrolyzes glycerophosphoethanolamine to phosphoethanolamine and glycerol. The structure of GPE-EP was determined by the molecular replacement method using a search model generated with AlphaFold2. This structure includes the entire length of the mature protein and it is composed of an N-terminal domain and a novel C-terminal domain connected to a flexible linker. The N-terminal domain is the catalytic domain containing calcium ions at the catalytic site. Coordination bonds were observed between five amino acid residues and glycerol. Although the function of the C-terminal domain is currently unknown, inter-domain interactions between the N- and C-terminal domains may contribute to its relatively high thermostability.

Modeling the employment decisions of young men and women in nine European countries: An application of random utility theory and revealed preference

In this paper we examine the decisions of over 15,000 young adults aged 18–35 from nine European countries to choose employment over unemployment or remaining in education. Instead of focusing only on who makes those decisions as is typically done, we attempt to answer the question of why a particular decision is made. Using data from the Cultural Pathways to Economic Self-sufficiency and Entrepreneurship (CUPESSE) project, we estimate a series of country and gender-specific conditional logit (CLGT) models adjusted for individual work values, soft skills, demographic characteristics and local labor markets. We find that young women in Czechia, Germany, Greece, Hungary and Italy and both men and women in Spain and the United Kingdom, respond to exogenous wage offers providing some evidence for job search theory and for the reservation wage hypothesis. Our uptake rate simulations show that a 10% increase in wages have the potential to increase employment by as little as 2 percentage points in the UK and by as much as 14 percentage points in Czechia and Spain. Further, if unemployment benefits are also simultaneously reduced by 10% employment increases in these latter countries by 20 and 17 percentage points. We demonstrate the ways that revealed preference (RP) analysis can aid policy makers through strength of preference, uptake rate and willingness-to-pay applications.

Multi-agent system consistency-based cooperative scheduling strategy of regional integrated energy system

This article presents a multi-agent system (MAS)-consistency-based cooperative scheduling strategy for regional integrated energy system to address the real-time economic scheduling problem with high penetration of multiple flexible loads. First, the MAS-based hierarchical optimal operation framework is developed to perform the real-time scheduling, where the framework with distributed control is further developed into the hierarchical framework with low-dimensional adjacency matrix. Furthermore, the economic scheduling optimization models corresponding to hierarchical distributed control mode and distributed control mode are established. The optimal objective of the proposed control structure is to 1) minimize the total operation cost, 2) optimize the electrical and heat power of all dispatchable units in balancing energy supply-demand. The operational rule of following heat load for hierarchical distributed control mode and a hill-climbing search model for distributed control mode are proposed to achieve the consistency of agents, so as to realize the cooperative optimal dispatch of electricity and heat vectors. Simulation results validate the effectiveness of the proposed MAS-based strategy in optimizing the cooperative operation of electricity-heat vectors. The real-time energy generation and load demand can be accurately balanced. The proposed strategy can perform efficient real-time economic scheduling and has satisfied adaptive capability for random variations of flexible loads.

AlphaFold-assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography.

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

Prior Information and Consumer Search: Evidence from Eye Tracking

Do consumers search the brands they know more or less frequently than the brands with which they are unfamiliar? In this paper, we attempt to answer this question using data from an experiment with two novel features: (i) survey information on consumers’ prior brand ownership, familiarity with each brand, and prior experience using different product features and (ii) eye-tracking data capturing search behavior at a very granular level. We find that consumers are generally more likely to search and buy brands they own and with which they are familiar, highlighting the importance of accounting for prior information. For this reason, we develop a search model in which both the information obtained during the search process and the information possessed by consumers prior to search are allowed to influence search and purchase decisions. Our model contributes to prior work by modeling search at the brand and attribute levels within a Bayesian learning framework. Using this model, we then quantify the impact of prior information on consumer choices as well as document the estimation bias arising when prior information is absent from the model. Finally, through a series of counterfactuals, we explore the managerial value of prior information data. This paper was accepted by Matt Shum, marketing. Funding: Q. Wang acknowledges financial support from the National Science Foundation of China [Grant 72072145] and the Natural Science Basic Research Program of Shaanxi Province [Grant 2020JQ-226]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.00611 .

A Combined Alignment Model for Code Search

A Combined Alignment Model for Code Search

EVSE Effectiveness in Multi-Unit Residential Buildings Using Composite Optimization and Heuristic Search

The effectiveness of electric vehicle supply equipment (EVSE) is a very important factor in multi-unit residential buildings (MRBs) when planning to invest in an electric vehicle (EV). However, the expected benefits depend on not only technology but also need many non-functional requirements such as building facilities, electrical infrastructure, operating costs, and energy demand management of EVSE. We discuss the functional and non-functional constraints in utilizing EVSEs in MRBs and addresses them by constructing a composite optimization problem with a heuristic boundary selection. The proposed model urges the use of electrical safety codes –to mitigate the hazard risk– and promotes deploying a flexible and scalable energy management system (EMS) to schedule, reserve, and tune the charging sessions. This article provides an effective EMS for mitigating the energy demand growth caused by EV charging on MRBs, by finding the equilibrium number of EVSEs and their energy usage through a heuristic search. The proposed EMS is reinforced by embedding machine learning tools such as k-means, silhouette scoring, heuristic search, and autoregressive model to adjust the optimal operational state of EVSEs. We delineated the proposed framework and the outcomes using two case studies with real data. The results showed that more EVSEs can be utilized in MRBs and 100% EVs can be fully charged with a lower capital cost and no additional energy demand cost.

Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data

State-of-the-art searches for gravitational waves (GWs) in pulsar timing array (PTA) datasets model the signal as an isotropic, Gaussian, and stationary process described by a power law. In practice, none of these properties are expected to hold for an incoherent superposition of GWs generated by a cosmic ensemble of supermassive black hole binaries (SMBHBs). This stochastic signal is usually referred to as the GW background (GWB) and is expected to be the primary signal in the PTA band. We performed a systematic investigation of the performance of current search algorithms, using a simple power-law model to characterise GW signals in realistic datasets. We used, as the baseline dataset, synthetic realisations of timing residuals mimicking the European PTA (EPTA) second data release (DR2). Thus, we included in the dataset uneven time stamps, achromatic and chromatic red noise, and multi-frequency observations. We then injected timing residuals from an ideal isotropic, Gaussian, single power-law stochastic process and from a realistic population of SMBHBs, performing a methodical investigation of the recovered signal. We found that current search models are efficient at recovering the GW signal, but several biases can be identified due to the signal-template mismatch, which we identified via probability-probability (P–P) plots and quantified using Kolmogorov-Smirnov (KS) statistics. We discuss our findings in light of the signal observed in the EPTA DR2 and corroborate its consistency with a SMBHB origin.

Dense and uniform displays facilitate the detection of salient targets

ABSTRACT Increasing the density or uniformity of nontarget stimuli appears to increase the saliency of singleton stimuli. Consequently, search times should be shorter. Surprisingly, however, effects of density or uniformity on search times were not always observed in detection tasks. We re-examined this finding with stimuli having two features, color and shape. Half of the participants indicated the presence or absence of a color singleton, and the other half indicated the presence or absence of a shape singleton. Density was changed by increasing the number of stimuli from 4 to 10. We found that the effects of density were either limited to target-absent trials or to target-present trials, which may explain previous failures to observe these effects. When color was the target feature, we found shorter RTs to dense than sparse displays on target-absent trials, but no difference on target-present trials. When shape was the target feature, it was the opposite. Concerning the uniformity of the nontargets, we found shorter RTs with uniform than mixed displays and this difference was larger on target-absent than target-present trials. These results are mostly consistent with the Guided Search Model.

Non-Adaptive and Adaptive Two-Sided Search with Fast Objects

In 1946, Koopman introduced a two-sided search model. In this model, a searched object is active and can move, at most, one step after each test. We analyze the model of a combinatorial two-sided search by allowing more moves of the searched object after each test. We give strategies and show that they are optimal. We consider adaptive and non-adaptive strategies. We show the surprising result that with the combinatorial two-sided search on a path graph, the optimal non-adaptive search needs the same number of tests as the corresponding adaptive strategy does. The strategy obtained can also be used as an encoding strategy to sent the position of a moving element through a transmission channel.

Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer

Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer

Real-time FPGA Integration for ECG Monitoring: Bidirectional Recurrent Chimp Search Model

The primary diagnostic tool for monitoring problems with the heart or related diseases is the electrocardiogram (ECG). The rising mortality rate caused by cardiac abnormalities leads to the development of more advanced techniques to detect heart anomalies are is of paramount importance. An electrocardiogram (ECG) is used for multiple diagnostic purposes since it produces continuous tracings of the electrophysiological activity generated from the heart. This paper developed a Bidirectional recurrent chimp search (Bi-RCS) method for diagnosing abnormalities by collecting ECG signals via various phases such as the data acquisition phase, signal processing phase, feature extraction phase, abnormality detection phase, real-time FPGA integration phase, monitoring and reporting phase as well as feedback phase. Signal pre-processing removes contaminants like noise using the Adaptive filtering methodology which severely limits the utility of the recorded ECG for better clinical evaluation. Bi-RNN is utilized for extracting related features and detecting abnormalities and the Chimp search optimization is employed to tune the parameters of Bi-RNN. After detecting the abnormalities, the reports of patients are monitored and recorded to provide an alert signal to healthcare providers. Finally, the potency of the methodology is analyzed with the ECG signal dataset and metrics like precision, F1-score accuracy, sensitivity, and specificity values. The obtained outputs equalized with the existing methods like KNN, EBT, and SVM. The analysis showed convincing performance with an accuracy of 99% and less error rate of 0.05.

Cooperative search model to detect a lost target based on its probabilistic distribution in a bounded zone

ABSTRACT This study uses two unit-speed searchers to solve the challenge of finding a randomly located target in a bounded region, such as searching for bodies found under the rubble resulting from the erosion of residential units due to horrific natural disasters. The search procedure begins at ( 0 , 0 ) . The region is subdivided into a limited even number of cells. The target’s position has a symmetric probability distribution. It is preferable to search in an optimal method in order to reduce the predicted time for discovering the target. An algorithm is used to generate the optimal search strategy. We discuss an example when the target has a symmetric truncated normal distribution. We provide an illustrated case to demonstrate this technique’s applicability.

The metamorphosis of machine translation: The rise of neural machine translation and its challenges

Machine translation refers to the process of using computers to translate source language into target language, which has undergone significant transformations since its inception, with the current mainstream neural machine translation achieving satisfactory translation performance. This paper overviews the three developmental stages of machine translation: rule-based machine translation, statistical machine translation, and neural machine translation, with a focus on neural machine translation. It introduces the key models that emerged in the development process of neural machine translation, namely the recurrent neural network encoder-decoder model, recurrent neural network search model, and Transformer, and compares their strengths and limitations. Other relevant technologies and models developed alongside neural machine translation are also discussed. Addressing the current challenges of neural machine translation, the paper delves into issues of overfitting, low-resource translation, structural optimization of Transformer models, and enhancement of neural machine translation interpretability. Finally, the paper explores the prospects of applying neural machine translation to multimodal translation.

A novel approach for prediction of groundwater quality using gradient boosting-based algorithms

ABSTRACT This study explores the potential of the GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid search, and Bayesian optimization were used to find the optimal values of various hyperparameters with all four-gradient boosting-based algorithms. One hundred and two samples of Entropy weighted water quality index with 14 input parameters are used for assessing groundwater quality. The calculated EWQI values for drinking range between 80.4 and 394.96 in pre-monsoon and 39.6 to 338.79 during the post-monsoon period. Moreover, spatial distribution maps displayed that the central portions of the study area fall under medium water quality. The performances of models were compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). The results reveal that the CC value by all modeling approaches is more than 0.93, suggesting a comparable performance by all methods. Results in terms of RMSE values in predicting the EWQI values suggest GPBoost (random search) model performed better than the other three models, thus suggesting a competitive performance by GPBoost in comparison to other gradient boosting-based approaches. Relative importance analysis provided by random and grid search methods highlights the significance of NO3 −, Mg2+, TDS, EC, and TH as important input parameters for predicting EWQI.

Labour Market Screening and the Design of Social Insurance: An Equilibrium Analysis of the Labour Market for the Disabled

Abstract This article studies how firms’ screening incentives in the labour market affect the optimal design of social insurance programs and quantitatively assesses the U.S. disability policies accounting for firms’ screening of the disabled. We develop an equilibrium search model where workers with different productivities have heterogeneous preferences over non-wage benefits and firms cannot offer an employment contract that explicitly depends on worker types. In this environment, firms may use contracts to screen out a certain type of workers, distorting employment rates and contracts in equilibrium. Therefore, the optimal structure of social insurance policies depends on firms’ screening incentives. We extend and structurally estimate this framework to quantitatively understand the inefficiencies arising from firms’ incentives to screen out disabled workers and examine the optimal joint design of disability insurance (DI) and various forms of firm subsidies. We find that hiring subsidies mitigate screening distortions; at the same time, they interact with DI by reducing the labour supply disincentives it generates. The optimal policy structure leads to a considerable welfare gain by simultaneously making firm subsidies and DI benefits more generous.

Ethnicity and UK graduate migration: An identity economics approach

AbstractThis paper reports on the employment migration behavior of non‐White ethnic minority graduates in the United Kingdom for the 2018/2019 graduation cohort, which is the last cohort to enter the labor market before the COVID‐19 pandemic. Using data from the new Graduate Outcomes survey and controlling for a rich set of background characteristics, the findings indicate that ethnic minority graduates are more likely than their White counterparts to find work in ethnically diverse areas of the United Kingdom after leaving higher education. An identity utility framework is then formalized that combines identity economics with traditional approaches of human capital theory and job search theory. A test of an ethnic identity‐based hypothesis reveals that Asian, Black, and Mixed‐background graduates are comparatively more likely to migrate to areas with higher ethnic diversity levels, rather than less diverse areas. In addition to traditional explanations based on human capital theory and job search theory, this paper argues that these patterns are best explained by ethnic identity norms, which introduce a preference for working in ethnically diverse places. However, the results should be interpreted with some caution because of concerns related to heterogeneity within the ethnic group classifications used in the paper and possible omitted and unobserved variables.

Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.