Utility-Aware Ridesharing on Road Networks

There is a strong need for synthetic yet realistic distribution system test data sets that are as diverse, large, and complex to solve as real systems. Such data sets can facilitate the development of advanced algorithms and the assessment of emerging distributed energy resources while avoiding the need to acquire proprietary critical infrastructure or private data. Such synthetic data sets, however, are useful only if they are realistic enough to look and behave similarly to actual systems. This paper presents a comprehensive framework for validating synthetic distribution data sets using a three-pronged statistical, operational, and expert validation approach. It also presents a set of statistical and operational metric targets for achieving realistic data sets based on detailed characterization of more than 10,000 real U.S. utility feeders. The paper demonstrates the use of the proposed validation approach to validate three large-scale synthetic data sets developed by the authors representing Santa Fe, New Mexico; Greensboro, North Carolina; and the San Francisco Bay Area, California.

Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications. We benchmarked the performance of model-based clustering on several synthetic and real gene expression data sets for which external evaluation criteria were available. The model-based approach has superior performance on our synthetic data sets, consistently selecting the correct model and the number of clusters. On real expression data, the model-based approach produced clusters of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of suggesting the number of clusters and an appropriate model. We also explored the validity of the Gaussian mixture assumption on different transformations of real data. We also assessed the degree to which these real gene expression data sets fit multivariate Gaussian distributions both before and after subjecting them to commonly used data transformations. Suitably chosen transformations seem to result in reasonable fits. MCLUST is available at http://www.stat.washington.edu/fraley/mclust. The software for the diagonal model is under development. kayee@cs.washington.edu. http://www.cs.washington.edu/homes/kayee/model.

Given a network with one source and multiple destinations, the multicast delay variation problem attempts to construct a multicast tree such that the interdestination delay variation among all the paths from the source to each destination is minimized. In this paper, we show that the problem is NP-hard and that it is not approximable within a constant factor in polynomial time unless NP = P. These findings indicate that it is impossible to develop efficient optimal or approximation algorithms for any multicast routing problem under the interdestination delay variation constraint unless NP = P.

Many work zones exist in the urban road network and have a great negative impact on city traffic. Finding the optimal work zone schedule can minimize the negative impact of work zones on traffic. This paper focuses on the day-based work zone scheduling problem in the urban network. Existing studies on the day-based work zone scheduling problem do not consider the progression of day-to-day traffic from a non-equilibrium state to an equilibrium state during the construction period. For the first time, this paper proposes a model for day-based work zone scheduling by introducing a day-to-day traffic assignment model, in which the target of the optimization problem is minimizing the increase in travel cost caused by work zones. Numerical examples are presented to explore the variations of the optimal construction sequence for different work durations, crew numbers, and model parameter values. Some new findings are obtained in the paper. When the construction duration of each work zone is relatively short (for example, less than 20 days), the optimal scheduling will obviously change with the work duration; when all of the construction durations increase to a threshold (for example, 60 days), the optimal construction sequence will no longer change. An optimal crew number exists that can minimize the increment of travel cost caused by work zones. During the construction period, the total travel cost in the network can be decreased by guiding travelers to change their original travel habits.

Dimensionality reduction is essential in multidimensional data mining since the dimensionality of real time data could easily reach higher dimensions. Most recent efforts on dimensionality reduction, however, are not adequate to multidimensional data due to lack of scalability. In this paper, we improve the performance of the fractional cuckoo search algorithm by utilizing the FCM function as objective function and we utilize the FCM operator which helps to improve the fitness value of worse solution. The fractional cuckoo search (FCS) algorithm is modified to reduce the dimension and select the best dimension. Once the high dimensional data is reduced in to low dimensional data, then the data is supplied to the clustering algorithm to make the partition easily. Finally, the experimentation is made with synthetic and real datasets and has been we have proved that efficiency of the FCM-FCS algorithm is 1.21% better than FPSO algorithm on iris dataset. The proposed algorithm is 0.66% better than FPSO 4.91% better than FCS on wine dataset and 3.4% better than FPSO 0.1% better than FCS on synthetic dataset.

Auditor travel can significantly complicate the scheduling process and have an important impact on audit cost, especially for audits of multinational companies. This paper formulates an optimization model that specifically incorporates auditor travel time and costs in the determination of the overall optimal audit schedule. From a more theoretical perspective, this research extends the method of cost control by proposing a method by which a sequence-dependent setup cost (i.e., travel cost) and an associated variable cost (i.e., out-of-town cost) can be programmed into an optimization process for audit planning. The proposed model provides an optimal schedule considering such costs in a multiple nonidentical manpower (i.e., different auditors) environment. Previous research on manpower scheduling has not dealt with sequence-dependent travel costs. The out-of-town time and cost elements are unique for manpower scheduling and not applicable for machine scheduling. An implementation of the model to an actual empirical audit is presented.

Extracting interesting points from a large dataset is an important problem for multi-criteria decision making. Recently, k-regret query was proposed and received attentions from the database community because it does not require any utility function from users and the output size is controllable. In this paper, we consider k-regret queries with binary constraints which doesn’t be addressed before. Given a collection of binary constraints, we study the problem of extracting the k representative points with small regret ratio while satisfying the binary constraints. To express the satisfaction of data points by the binary constraints, we propose two models named NBC and LBC in quantitative and qualitative ways respectively. In quantitative way, the satisfaction is expressed by a real number between 0 and 1 which quaNtifies the satisfaction of a point by the Binary Constraints. While in qualitative way, the satisfaction is modeled quaLitatively by a set of Binary Constraints which are satisfied by the point. Further, two efficient approximate algorithms called \(NBC_P\)-Greedy and \(NBC_{DN}\)-Greedy are developed based on NBC while \(LBC_{DN}\)-Greedy algorithm is proposed based on LBC. Extensive experiments on synthetic and real datasets confirm the efficiency and effectiveness of our proposed algorithms.

SummaryAsian options are popular path-dependent derivatives in the financial market. However, how to price them efficiently and accurately has been a longstanding research and practical problem. No known exact pricing formulas are available to price the Asian option. Although approximate pricing formulas exist, they lack accuracy guarantees. Asian options can be priced on the lattice. A lattice divides a time interval into n equal-length time steps. It is known that the value computed by the lattice converges to the true option value as n → ∞. Unfortunately, only subexponential-time algorithms are available if Asian options are to be priced on the lattice without approximations. Efficient approximation algorithms are available for the lattice. The best known in the literature is an O(n 3.5)-time approximation lattice algorithm and an O(n 3)-time approximation PDE algorithm. Our paper suggests an O(n 2.5)-time lattice algorithm. Our algorithm uses a novel technique based on the method of Lagrange multipliers to minimize the approximation error. Numerical results verify the accuracy and the excellent performance of our algorithm.

The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world, user preferences are not static but full of dynamics. Though there are several previous works that addressed this time-varying issue of user preferences, it seems (to the best of our knowledge) that none of them are specifically designed for tracking concept drift in individual user preferences . Motivated by this, we develop a temporal MF approach for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: 1) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step and 2) by Lasso regression, we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic data set and several real data sets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSEs) for both the synthetic and real data sets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic data set and the Ciao data set, there are quite a few users with that property and the performance gains for these two data sets are roughly 20% and 5%, respectively.

We initiate the study of the computational complexity of the covering radius problem for point lattices, and approximation versions of the problem for both lattices and linear codes. We also investigate the computational complexity of the shortest linearly independent vectors problem, and its relation to the covering radius problem for lattices. For the covering radius on n-dimensional lattices, we show that the problem can be approximated within any constant factor /spl gamma/(n) > 1 in random exponential time 2/sup O(n)/, it is in AM for /spl gamma/(n) = 2, in coAM for /spl gamma/(n) = /spl radic/(n log n), and in NP /spl cap/ coNP for /spl gamma/(n) = /spl radic/n. For the covering radius on n-dimensional linear codes, we show that the problem can be solved in deterministic polynomial time for approximation factor /spl gamma/(n) = log n, but cannot be solved in polynomial time for some /spl gamma/(n) = /spl Omega/(log log n) unless NP can be simulated in deterministic n/sup O(log log log n)/ time. Moreover, we prove that the problem is NP-hard for every constant approximation factor, it is /spl Pi//sub 2/-hard for some constant approximation factor, and it is in AM for approximation factor 2. So, it is unlikely to be /spl Pi//sub 2/-hard for approximation factors larger than 2. This is a natural hardness of approximation result in the polynomial hierarchy. For the shortest independent vectors problem, we give a coAM protocol achieving approximation factor /spl gamma/(n) = /spl radic/(n/log n), solving an open problem of Blomer and Seifert (1999), and prove that the problem is also in coNP for /spl gamma/(n) = /spl radic/n. Both results are obtained by giving a gap-preserving nondeterministic polynomial time reduction to the closest vector problem.

With the development of the information age, the awareness of archives informatization of governments, organizations and groups at all levels is constantly increasing. The key content extraction of scanned archive images has been widely concerned by researchers. Based on the deep learning method, we extract text and key elements (ID photo, seal, date of birth) by analyzing the archive images. Based on the real archive images data set and synthetic archive images data set, we use the PP-OCR [1] model to finish the text structured extraction and YOLOv5 [2] to train a target detection model of ID photos, seals, and dates of birth to achieve the extraction of key elements. Since the real data set cannot be made public, we use ordinary archive data for explanation.KeywordsArchive imagesSynthetic archive imagesPP-OCRYOLOv5Text structured extractionKey elements extraction

In the case of two small toy data sets, we found out that subsampling has a much weaker behavior in the finding of the true number of clusters K than bootstrapping (Mucha and Bartel, Soft bootstrapping in cluster analysis and its comparison with other resampling methods. In: M. Spiliopoulou, L. Schmidt-Thieme, R. Janning (eds.) Data analysis, machine learning and knowledge discovery. Springer, Cham, 2014). In contradiction, Möller and Dörte (Intell Data Anal 10:139–162, 2006) pointed out that “subsampling … clearly outperformed the bootstrapping technique in the detection of correct clustering consensus results.” Obviously, there is a need for further investigations. Therefore here we compare these two resampling techniques based on real and artificial data sets by means of different indices: ARI or Jaccard. We consider hierarchical cluster analysis methods because they find all partitions into K = 2, 3, … clusters in one run only, and, moreover, these results are (usually) unique (Spaeth, Cluster analysis algorithms for data reduction and classification of objects. Ellis Horwood, Chichester, 1982). The methods are tested on two synthetic data sets and two real data sets. Obviously, bootstrapping is better than subsampling in finding the true number of clusters.

Some insight into the benefits of regularly and frequently maintaining traffic signal timing within and across jurisdictional boundaries is provided. The rapidly growing Cranberry Township, located at the crossroads of two interstate highways (Pennsylvania Turnpike [Interstate-76] and Interstate-79) 20 miles north of Pittsburgh, Pennsylvania, is used as a case study. Environmental and economic costs due to the absence of monitoring and updating traffic signal timing are estimated. While traffic signal timing optimization and coordination is a cost effective strategy to reduce travel time and its consequential air emissions along corridors, implementing such timing plans is only effective at and around the time of implementation, while travel demand remains constant. As time passes, with the fluctuation of vehicular demands, especially in rapidly developing areas, timing plans need to be monitored and modified. In Cranberry Township such developments and travel demand fluctuations result in a significant increase in costs of travel (time and fuel) and external environmental costs. Estimates show that timing plans that were at their optimal level at the time of implementation, if left unmodified, result in an 18% increase in the cost of fuel, a 13% increase in the cost of travel time, and an 11% increase in the external environmental costs in less than two years.

The popularity of mobile Internet techniques and Online-To-Offline(O2O) business models has led to the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC platforms is to assign tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects,tasks and workers, and let workers to travel to the location of usersto provide services, which is a 2D matching problem. However, recent services provided by some new platforms, such as person-alized haircut service1and station ride-sharing, need users andworkers travel together to a third workplace to complete the service, which is indeed a 3D matching problem. Approaches in the existingstudies either cannot solve such 3D matching problem, or lack aassignment plan satisfying both users' and workers' preference inreal applications. Thus, in this paper, we propose a 3-Dimensional Stable Spatial Matching(3D-SSM) for the 3D matching problem innew SC services. We prove that the 3D-SSM problem is NP-hard, and propose two baseline algorithms and two efficient approximatealgorithms with bounded approximate ratios to solve it. Finally, weconduct extensive experiment studies which verify the efficiencyand effectiveness of the proposed algorithms on real and synthetic datasets.

Postoperative hand therapy is important for recovery and enhanced functional outcomes following hand surgery. Access to this essential care is limited in Alabama, however, particularly for vulnerable populations in rural areas where public transportation is scarce. These challenges are further amplified for patients who are unable to drive due to their injuries. This study aimed to quantify disparities and barriers to certified hand therapy access in Alabama. Certified hand therapists in Alabama were identified through the Hand Therapy Certification Commission directory. Practice addresses were geocoded and paired with county center coordinates to calculate travel distances from each county center to the nearest certified hand therapist. These distances were then converted into one-way travel costs using Internal Revenue Service standard mileage rates. Counties were categorized by rurality using Rural-Urban Continuum Codes (RUCC) and assessed for social vulnerability using the Social Vulnerability Index (SVI), which considers socioeconomic, housing, and transportation factors. Statistical analyses, including t tests, χ2 tests, and multivariate regression, were used to evaluate patterns and predictors of travel costs. Alabama counties had a mean SVI of 0.67 ± 0.195-significantly higher than the national mean of 0.50 (P<0.0001). More than 80% of Alabama counties rank in the most vulnerable 50% of US counties, with Shelby County being the only county in the least vulnerable quartile (SVI 0.116). In addition, 36 counties (53.7%) are classified as rural (RUCC 4-9). Both SVI and RUCC independently correlated with increased travel costs. Multivariate regression modeling revealed that each unit increase in RUCC was associated with a $3.43 increase in travel costs (P<0.0001), and each 10% increase in SVI corresponded to a $1.64 increase in travel costs (P=0.006). These factors explained 56.4% of the variability in travel costs. Heatmaps visually highlighted higher travel costs in rural, socially vulnerable areas. Rural and socially vulnerable populations in Alabama face significant travel barriers to accessing certified hand therapy. The combined impact of geographic isolation and social vulnerability underscores the need for targeted interventions to ensure equitable access to postoperative care.