Showing papers on "Sorting published in 2017"

The Growing Importance of Social Skills in the Labor Market

Abstract: The labor market increasingly rewards social skills. Between 1980 and 2012, jobs requiring high levels of social interaction grew by nearly 12 percentage points as a share of the U.S. labor force. Math-intensive but less social jobs—including many STEM occupations—shrank by 3.3 percentage points over the same period. Employment and wage growth were particularly strong for jobs requiring high levels of both math skill and social skills. To understand these patterns, I develop a model of team production where workers “trade tasks” to exploit their comparative advantage. In the model, social skills reduce coordination costs, allowing workers to specialize and work together more efficiently. The model generates predictions about sorting and the relative returns to skill across occupations, which I investigate using data from the NLSY79 and the NLSY97. Using a comparable set of skill measures and covariates across survey waves, I find that the labor market return to social skills was much greater in the 2000s than in the mid-1980s and 1990s.

Unsupervised Representation Learning by Sorting Sequences

Abstract: We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification, and object detection tasks.

A cargo-sorting DNA robot.

Abstract: INTRODUCTION Since the 1980s, the design and synthesis of molecular machines has been identified as a grand challenge for molecular engineering. Robots are an important type of molecular machine that automatically carry out complex nanomechanical tasks. DNA molecules are excellent materials for building molecular robots, because their geometric, thermodynamic, and kinetic properties are well understood and highly programmable. So far, the development of DNA robots has been limited to simple functions. Most DNA robots were designed to perform a single function: walking in a controlled direction. A few demonstrations included a second function combined with walking (for example, picking up nanoparticles or choosing a path at a junction). However, these relatively more complex functions were also more difficult to control, and the complexity of the tasks was limited to what the robot can perform within 3 to 12 steps. In addition, each robot design was tailored for a specific task, complicating efforts to develop new robots that perform new tasks by combining functions and mechanisms. RATIONALE The design and synthesis of molecular robots presents two critical challenges, those of modularity and algorithm simplicity, which have been transformative in other areas of molecular engineering. For example, simple and modular building blocks have been used for scaling up molecular information processing with DNA circuits. As in DNA circuits, simple building blocks for DNA robots could enable more complex nanomechanical tasks, whereas modularity could allow diverse new functions performed by robots using the same set of building blocks. RESULTS We demonstrate a DNA robot that performs a nanomechanical task substantially more sophisticated than previous work. We developed a simple algorithm and three modular building blocks for a DNA robot that performs autonomous cargo sorting. The robot explores a two-dimensional testing ground on the surface of DNA origami, picks up multiple cargos of two types that are initially at unordered locations, and delivers each type to a specified destination until all cargo molecules are sorted into two distinct piles. The robot is designed to perform a random walk without any energy supply. Exploiting this feature, a single robot can repeatedly sort multiple cargos. Localization on DNA origami allows for distinct cargo-sorting tasks to take place simultaneously in one test tube or for multiple robots to collectively perform the same task. On average, our robot performed approximately 300 steps while sorting the cargos. The number of steps is one to two magnitudes larger than the previously demonstrated DNA robots performing additional tasks while walking. Using exactly the same robot design, the system could be generalized to multiple types of cargos with arbitrary initial distributions, and to many instances of distinct tasks in parallel, whereas each task can be assigned a distinct number of robots depending on the difficulty of the task. CONCLUSION Using aptamers, antibodies, or direct conjugation, small chemicals, metal nanoparticles, and proteins could be transported as cargo molecules so that the cargo-sorting DNA robots could have potential applications in autonomous chemical synthesis, in manufacturing responsive molecular devices, and in programmable therapeutics. The building blocks developed in this work could also be used for diverse functions other than cargo sorting. For example, inspired by ant foraging, adding a new building block for leaving pheromone-like signals on a path, DNA robots could be programmed to find the shortest path and efficiently transport cargo molecules. With simple communication between the robots, they could perform even more sophisticated tasks. With more effort in developing modular and collective molecular robots, and with simple and systematic approaches, molecular robots could eventually be easily programmed like macroscopic robots, but working in microscopic environments.

Unsupervised Representation Learning by Sorting Sequences

Abstract: We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.

Sorting Carbon Nanotubes

Abstract: Sorting of single-wall carbon nanotubes by their electronic and atomic structures in liquid phases is reviewed in this chapter. We first introduce the sorting problem, and then provide an overview of several sorting methodologies, following roughly the chronological order of their development over the past 15 years or so. Major methods discussed include ion-exchange chromatography, density-gradient ultracentrifugation, selective extraction in organic solvents, gel chromatography, and aqueous two-phase extraction. A main focus of the review is on the common mechanisms underlining all sorting processes. We propose that differences in solvation among different nanotube species are the ultimate driving force of sorting, and we corroborate this proposal by presenting analysis on how the differences are realized in electronic-structure-based sorting and atomic-structure-based sorting. In the end, we offer some suggestions on future directions that may grow out of carbon nanotube sorting. In particular, the prospect of expanding the function of DNA/carbon nanotube hybrid to control inter-particle interactions both inside and outside the nanotube is discussed.

Sorting Through Search and Matching Models in Economics

Abstract: Toward understanding assortative matching, this is a self-contained introduction to research on search and matching. We first explore the nontransferable and perfectly transferable utility matching paradigms, and then a unifying imperfectly transferable utility matching model. Motivated by some unrealistic predictions of frictionless matching, we flesh out the foundational economics of search theory. We then revisit the original matching paradigms with search frictions. We finally allow informational frictions that often arise, such as in college-student sorting.

Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems

Abstract: Differential evolution (DE) is one of the best evolutionary algorithms (EAs). The effort of improving its performance has received great research attentions, such as adaptive DE (JADE). Based on the analysis on the aspects that may improve the performance of JADE, we introduce a modified JADE version with sorting crossover rate ( CR ). In JADE, CR values are generated based on mean value and Gaussian distribution. In the proposed algorithm, a smaller CR value is assigned to individual with better fitness value. Therefore, the components of the individuals, which have better fitness values, can appear in the offspring with higher possibility. In addition, the better offspring generated from last iteration are supposed to have better schemes, hence these schemes are preserved in next offspring generation procedure. This modified version is called as JADE algorithm with sorting CR (JADE_sort). The experiments results with several excellent algorithms show the effectiveness of JADE_sort.

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Abstract: Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi- and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of non-dominated sorting in multi- and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multi- and many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi- and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi- and many-objective evolutionary algorithms.

An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations

Abstract: Efficiency is a key success factor in complex supply chain networks. It is imperative to ensure proper flow of goods and services in humanitarian supply chains in response to a disaster. To this end, we propose a multi-echelon humanitarian logistic network that considers the location of central warehouses, managing the inventory of perishable products in the pre-disaster phase, and routing the relief vehicles in the post-disaster phase. An epsilon-constraint method, a non-dominated sorting genetic algorithm (NSGA-II), and a modified NSGA-II called reference point based non-dominated sorting genetic algorithm-II (RPBNSGA-II) are proposed to solve this mixed integer linear programming (MILP) problem. The analysis of variance (ANOVA) is used to analyze the results showing that NSGA-II performs better than the other algorithms with small size problems while RPBNSGA-II outperforms the other algorithms with large size problems.

Identifying Equilibrium Models of Labor Market Sorting

Abstract: We assess the empirical content of equilibrium models of labor market sorting based on unobserved (to economists) characteristics. In particular, we show theoretically that all parameters of the classic model of sorting based on absolute advantage in Becker, 1973 with search frictions can be nonparametrically identified using only matched employer–employee data on wages and labor market transitions. In particular, these data are sufficient to nonparametrically estimate the output of any individual worker with any given firm. Our identification proof is constructive and we provide computational algorithms that implement our identification strategy given the limitations of the available data sets. Finally, we add on-the-job search to the model, extend the identification strategy, and apply it to a large German matched employer–employee data set to describe detailed patterns of sorting and properties of the production function.

Distributed join algorithms on thousands of cores

Abstract: Traditional database operators such as joins are relevant not only in the context of database engines but also as a building block in many computational and machine learning algorithms. With the advent of big data, there is an increasing demand for efficient join algorithms that can scale with the input data size and the available hardware resources.In this paper, we explore the implementation of distributed join algorithms in systems with several thousand cores connected by a low-latency network as used in high performance computing systems or data centers. We compare radix hash join to sort-merge join algorithms and discuss their implementation at this scale. In the paper, we explain how to use MPI to implement joins, show the impact and advantages of RDMA, discuss the importance of network scheduling, and study the relative performance of sorting vs. hashing. The experimental results show that the algorithms we present scale well with the number of cores, reaching a throughput of 48.7 billion input tuples per second on 4,096 cores.

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem ☆

Abstract: In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

Abstract: A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

Sorting Multidimensional Types: Theory and Application

Abstract: This article studies multidimensional matching between workers and jobs. Workers differ in manual and cognitive skills and sort into jobs that demand different combinations of these two skills. To study this multidimensional sorting problem, I develop a theoretical framework that generalizes the unidimensional notion of assortative matching and sufficient conditions on the technology under which sorting obtains. I derive the equilibrium in closed form and use this explicit solution to study biased technological change. The main finding is that an increase in worker-job complementarities in cognitive relative to manual inputs leads to more pronounced sorting and wage inequality across cognitive relative to manual skills. This can trigger wage polarization and boost aggregate wage inequality. I then estimate the model for the U.S. and identify sizable technology shifts: during the last two decades, worker-job complementarities in cognitive inputs strongly increased, whereas complementarities in manual inputs decreased. In addition to this bias in complementarities, there has been a cognitive skill-bias in production. Counterfactual exercises suggest that these technology shifts (as opposed to changes in skill supply and demand) can account for observed changes in worker-job sorting, wage polarization and a significant part of the increase in U.S. wage dispersion.

Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search

Abstract: Display Omitted A bi-objective multi-skilled project scheduling problem is addressed.The objectives are to minimize processing costs and reworking risks of the activities.Three cuckoo-search-based meta-heuristics are presented to obtain Pareto solutions.The performances of the solution algorithms are compared in terms of different measures.Fuzzy dominance sorting cuckoo search (FSCS) attained the top rank with the best overall performance. In many projects, multi-skilled workforces are able to perform different tasks with different quality levels. In this paper, a real-life version of the multi-skilled resource constrained project scheduling problem is investigated, in which the reworking risk of each activity depends on the assigned level of multi-skilled workforces. The problem is formulated mathematically as a bi-objective optimization model to minimize total costs of processing the activities and to minimize reworking risks of the activities, concurrently. In order to solve the resulting problem, three cuckoo-search-based multi-objective mechanisms are developed based on non-dominance sorting genetic algorithm, multi-objective particle swarm and multi-objective invasive weeds optimization algorithm. The parameters of the algorithms are tuned using the Taguchi method to improve the efficiency of the solution procedures. Furthermore, a competitive multi-objective invasive weeds optimization algorithm is used to evaluate the performance of the proposed methodologies. Finally, a priority based method is employed to compare the proposed algorithms in terms of different metrics.

Custom-tailored spatial mode sorting by controlled random scattering

Abstract: The need to increase data transfer rates constitutes a key challenge in modern information-driven societies. Taking advantage of the transverse spatial modes of light to encode more information is a promising avenue for both classical and quantum photonics. However, to ease access to the encoded information, it is essential to be able to sort spatial modes into different output channels. Here, we introduce a novel way to customize the sorting of arbitrary spatial light modes. Our method relies on the high degree of control over random scattering processes by preshaping of the phase structure of the incident light. We demonstrate experimentally that various sets of modes, irrespective of their specific modal structure, can be transformed to any output channel arrangement. Thus, our method enables full access to all of the information encoded in the transverse structure of the field, for example, azimuthal and radial modes. We also demonstrate that coherence is retained in this complex mode transformation, which opens up applications in quantum and classical information science.

On-chip cell sorting by high-speed local-flow control using dual membrane pumps

Abstract: Although researchers have proposed various methods of on-chip cell sorting, high-throughput sorting of large cells remains hampered by the difficulty of controlling high-speed flow over a wide sorting area. To overcome this problem, we proposed high-speed local-flow control using dual membrane pumps driven by piezoelectric actuators placed on the outside of a microfluidic chip in this paper. We evaluated the controllability of shifting the flow profile by the local-flow. The results indicated that we could sort large cells up to approximately 150 μm in size with an equivalent throughput of 31 kHz. Because our method can control the flow profiles, it is applicable not only to large cells but also to small cells. The cell-sorting efficacy of the proposed method was experimentally evaluated on Euglena gracilis NIES-48 (E. gracilis) cells as large target cells and GCIY-EGFP (GCIY) cells derived from a gastric cancer cell line as small target cells. In E. gracilis cells sorting, the throughput is 23 kHz with a 92.8% success rate, 95.8% purity, and 90.8% cell viability. In GCIY sorting, the throughput is 11 kHz with a 97.8% success rate, 98.9% purity, and 90.7% cell viability. These results confirm that the proposed method sorts differently sized cells with high throughput and hence, overcomes the throughput–size trade-off that exists in conventional on-chip cell sorters.

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

Abstract: Sorting is at the core of many database operations, such as index creation, sort-merge joins, and user-requested output sorting. As GPUs are emerging as a promising platform to accelerate various operations, sorting on GPUs becomes a viable endeavour. Over the past few years, several improvements have been proposed for sorting on GPUs, leading to the first radix sort implementations that achieve a sorting rate of over one billion 32-bit keys per second. Yet, state-of-the-art approaches are heavily memory bandwidth-bound, as they require substantially more memory transfers than their CPU-based counterparts. Our work proposes a novel approach that almost halves the amount of memory transfers and, therefore, considerably lifts the memory bandwidth limitation. Being able to sort two gigabytes of eight-byte records in as little as 50 milliseconds, our approach achieves a 2.32-fold improvement over the state-of-the-art GPU-based radix sort for uniform distributions, sustaining a minimum speed-up of no less than a factor of 1.66 for skewed distributions. To address inputs that either do not reside on the GPU or exceed the available device memory, we build on our efficient GPU sorting approach with a pipelined heterogeneous sorting algorithm that mitigates the overhead associated with PCIe data transfers. Comparing the end-to-end sorting performance to the state-of-the-art CPU-based radix sort running 16 threads, our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for sorting 64 GB key-value pairs with a skewed and a uniform distribution, respectively.

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

Abstract: Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA...

Willingness to Pay for Clean Air in China

Abstract: We develop a residential sorting model incorporating migration disutility to recover the implicit value of clean air in China. The model is estimated using China Population Census Data along with PM2.5 satellite data. Our study provides new evidence on the willingness to pay for air quality improvement in developing countries and is the first application of an equilibrium sorting model to the valuation of non-market amenities in China. We employ two novel instrumental variables based on coal-fired electricity generation and wind direction to address the endogeneity of local air pollution. Results suggest important differences between the residential sorting model and a conventional hedonic model, highlighting the role of moving costs and the discreteness of the choice set. Our sorting results indicate that the economic value of air quality improvement associated with a one-unit decline in PM2.5 concentration is up to $8.83 billion for all Chinese households in 2005.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

Abstract: Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

A vision-based robotic grasping system using deep learning for garbage sorting

Abstract: This paper proposes a robotic grasping system for automatically sorting garbage based on machine vision. This system achieves the identification and positioning of target objects in complex background before using manipulator to automatically grab the sorting objects. The object identification in complex background is the key problem that machine vision algorithm is trying to solve. This paper uses the deep learning method to achieve the authenticity identification of target object in complex background. In order to achieve the accurate grabbing of target object, we apply the Region Proposal Generation (RPN) and the VGG-16 model for object recognition and pose estimation. The machine vision system sends the information of the geometric centre coordinates and the angle of the long side of the target object to the manipulator which completes the classification and grabbing of the target object. The results of sorting experiment of the bottles in the garbage show that the vision algorithm and the manipulator control method of the proposed system can achieve the garbage sorting efficiently.