Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.
Draft Genome Sequence of Subantarctic Rhodococcus sp. Strain 1139

ABSTRACT The draft genome sequence of subantarctic Rhodococcus sp. strain 1139 is reported here. The genome size is 7.04 Mb with high G+C content (62.3%) and it contains a large number of genes involved in lipid synthesis. This lipid synthesis system is characteristic of oleaginous Actinobacteria, which are of interest for biofuel production.
Soil respiration response to prescribed burning and thinning in mixed-conifer and hardwood forests

The effects of management on soil carbon efflux in different ecosystems are still largely unknown yet cru- cial to both our understanding and management of global carbon flux. To compare the effects of common forest man- agement practices on soil carbon cycling, we measured soil respiration rate (SRR) in a mixed-conifer and hardwood forest that had undergone various treatments from June to August 2003. The mixed-conifer forest, located in the Sierra Nevada Mountains of California, had been treated with thinning and burning manipulations in 2001, and the hardwood forest, located in the southeastern Missouri Ozarks, had been treated with harvesting manipulations in 1996 and 1997. Litter depth, soil temperature, and soil moisture were also measured. We found that selective thinning produced a simi- lar effect on both forests by elevating SRR, soil moisture, and soil temperature, although the magnitude of response was greater in the mixed-conifer forest. Selective harvest increased SRR by 43% (from 3.38 to 4.82 µmol·m -2 ·s -1 )i n the mixed-conifer forest and by 14% (from 4.25 to 4.84 µmol·m -2 ·s -1 ) in the hardwood forest. Burning at the conifer site and even-aged harvesting at the mixed-hardwood site did not produce significantly different SRR from controls. Mean SRR were 3.24, 3.42, and 4.52 µmol·m -2 ·s -1 , respectively. At both sites, manipulations did significantly alter
Boolean operations on triangulated solids

In this paper an efficient and robust method for Boolean operations on triangulated solids is presented. It is applied to regularized Boolean operations including union, difference, and intersection. This approach is better than other methods because three optimizations have been introduced. First, the constructed topology information improves the data structure from discrete triangles to point indices, face indices, and their connectivity information. Second, the space dividing algorithm has improved the computational complexity from O (m * n) to O (k (log K)). Third, the tessellation has enumerated a number of special triangle-triangle intersection examples, which are then dealt with separately. Finally, this method is implemented by a program written in C++ and OSG. With some examples, this system is proved to be efficient and robust.
A Generalized Hybrid Generation Scheme of Differential Evolution for Global Numerical Optimization

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.
Free-Breathing Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging in a Rat Liver Tumor Model Using Dynamic Radial T1 Mapping

Objectives:The high sensitivity to motion artifacts is a major limiting factor for applying the dynamic 3D T1-weighted gradient-echo (3D T1w GRE) technique for dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) experiments in small rodents. Dynamic quantification of the relaxation rate R1 (1/T1) presents an alternative approach to reduce these motion artifacts. In this work, an optimized 2D single-shot Look-Locker based T1 mapping technique, named GOLD, applying radial sampling in the golden-angle view order and contrast-enhancing k-space filter was evaluated for its use in free-breathing quantitative DCE-MRI of rat liver on a clinical 1.5 T MRI system. Materials and Methods:In vitro measurements and initial in vivo experiments in healthy rats were performed to evaluate the accuracy and resilience of the GOLD technique to motion artifacts. Unifocal hepatocellular carcinoma (HCC) was established in 20 male Buffalo rats. Twelve days after tumor cell implantation, animals were screened for intrahepatic tumor nodules by high-resolution T2-weighted MRI. Quantitative DCE-MRI experiments applying bolus injected gadopentetate dimeglumine were performed in 11 HCC-bearing rats using the GOLD technique. For comparison, a standard 3D T1w GRE sequence was applied in 6 additional rats. Results:Phantom experiments showed good agreement for T1 values measured by the GOLD method and an inversion recovery spectroscopy measurement. The in vivo experiments in healthy rats confirmed the robustness of the GOLD method in T1 value determination and its resilience to motion artifacts. Gadopentetate dimeglumine concentration (CGd) time curves determined from free-breathing GOLD-based DCE-MRI experiments of HCC-bearing rats allowed reliable and robust pharmacokinetic modeling (Ktrans, ve, lag time Td, and slow washout rate rwo) of tumor, liver, and spinal muscle. In comparison to the dynamic 3D T1w GRE, the GOLD method showed less variation and jitter in the CGd time curves and significantly increased accuracy (in terms of the goodness of fit) in the pharmacokinetic modeling. Significant differences were detected for Ktrans and ve with the 3D T1w GRE method apparently underestimating those parameters. Conclusions:The GOLD technique allowed dynamic sampling of 2D axial T1 maps of the rat abdomen with 6-second temporal resolution enabling simultaneous and robust pharmacokinetic modeling of HCC, normal liver, and spinal muscle.
Toolglass and magic lenses: the see-through interface

ToolglassTM widgets are new user interface tools that can appear, as though on a transparent sheet of glass, between an application and a traditional cursor. They can be positioned with one hand while the other positions the cursor. The widgets provide a rich and concise vocabulary for operating on application objects. These widgets may incorporate visual filters, called Magic LensTM filters, that modify the presentation of application objects to reveal hidden information, to enhance data of interest, or to suppress distracting information. Together, these tools form a see-through interface that offers many advantages over traditional controls. They provide a new style of interaction that better exploits the user? s everyday skills. They can reduce steps, cursor motion, and errors. Many widgets can be provided in a user interface, by designers and by users, without requiring dedicated screen space. In addition, lenses provide rich context-dependent feedback and the ability to view details and context simultaneously. Our widgets and lenses can be combined to form operation and viewing macros, and can be used over multiple applications.
MicroRNA and mRNA Features of Malignant Pleural Mesothelioma and Benign Asbestos-Related Pleural Effusion

Introduction. We investigated the expression of microRNAs and mRNAs in pleural tissues from patients with either malignant pleural mesothelioma or benign asbestos-related pleural effusion. Methods. Fresh frozen tissues from a total of 18 malignant pleural mesothelioma and 6 benign asbestos-related pleural effusion patients were studied. Expression profiling of mRNA and microRNA was performed using standard protocols. Results. We discovered significant upregulation of multiple microRNAs in malignant pleural mesothelioma compared to benign asbestos-related pleural effusion. Hsa-miR-484, hsa-miR-320, hsa-let-7a, and hsa-miR-125a-5p were able to discriminate malignant from benign disease. Dynamically regulated mRNAs were also identified. MET was the most highly overexpressed gene in malignant pleural mesothelioma compared to benign asbestos-related pleural effusion. Integrated analyses examining microRNA-mRNA interactions suggested multiple altered targets within the Notch signaling pathway. Conclusions. Specific microRNAs and mRNAs may have diagnostic utility in differentiating patients with malignant pleural mesothelioma from benign asbestos-related pleural effusion. These studies may be particularly helpful in patients who reside in a region with a high incidence of mesothelioma.
Product Sophistication and Spillovers from Foreign Direct Investment

Foreign direct investment (FDI) in developing countries is often associated with higher economic growth due to knowledge and technology spillovers to local firms. One way that FDI speeds up growth is by facilitating the manufacturing of more sophisticated products by local firms. So far, there is a lack of firm-level evidence how the presence of multinational firms affects the product sophistication of firms in a developing country. The aim of this paper is to fill this gap. We compile an extensive firm-product-level data set of Indian manufacturing firms, which we complement with information on product sophistication and spillovers from FDI. We then explore different channels through which spillovers from multinationals to local Indian firms foster the manufacturing of sophisticated products. We find evidence that spillovers through supplier linkages strongly increase the manufacturing of sophisticated products in India.
Replacing Saturated Fat With Walnuts or Vegetable Oils Improves Central Blood Pressure and Serum Lipids in Adults at Risk for Cardiovascular Disease: A Randomized Controlled‐Feeding Trial

Background Walnuts have beneficial effects on cardiovascular risk factors, but it is unclear whether these effects are attributable to the fatty acid (FA) content, including α‐linolenic acid (ALA), and/or bioactives. Methods and Results A randomized, controlled, 3‐period, crossover, feeding trial was conducted in individuals at risk for cardiovascular disease (n=45). Following a 2‐week standard Western diet run‐in (12% saturated FAs [SFA], 7% polyunsaturated FAs, 12% monounsaturated FAs), participants consumed 3 isocaloric weight‐maintenance diets for 6 weeks each: a walnut diet (WD; 7% SFA, 16% polyunsaturated FAs, 3% ALA, 9% monounsaturated FAs); a walnut FA‐matched diet; and an oleic acid–replaced‐ALA diet (7% SFA, 14% polyunsaturated FAs, 0.5% ALA, 12% monounsaturated FAs), which substituted the amount of ALA from walnuts in the WD with oleic acid. This design enabled evaluation of the effects of whole walnuts versus constituent components. The primary end point, central systolic blood pressure, was unchanged, and there were no significant changes in arterial stiffness. There was a treatment effect (P=0.04) for central diastolic blood pressure; there was a greater change following the WD versus the oleic acid–replaced‐ALA diet (−1.78±1.0 versus 0.15±0.7 mm Hg, P=0.04). There were no differences between the WD and the walnut fatty acid‐matched diet (−0.22±0.8 mm Hg, P=0.20) or the walnut FA‐matched and oleic acid–replaced‐ALA diets (P=0.74). The WD significantly lowered brachial and central mean arterial pressure. All diets lowered total cholesterol, LDL (low‐density lipoprotein) cholesterol, HDL (high‐density lipoprotein) cholesterol, and non‐HDL cholesterol. Conclusions Cardiovascular benefits occurred with all moderate‐fat, high‐unsaturated‐fat diets. As part of a low‐SFA diet, the greater improvement in central diastolic blood pressure following the WD versus the oleic acid–replaced‐ALA diet indicates benefits of walnuts as a whole‐food replacement for SFA. Clinical Trial Registration URL: https://www.clinicaltrials. gov. Unique identifier: NCT02210767.
Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition

Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.
The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood. NBER Working Paper No. 17699.

2.5 Million Students and 18 million tests scores were used from a large urban school district in the United States. The data spanned years 1989-2009 and was selected from tax records in order to collect information such as college attendance, teenage births, earnings, student outcomes, household outcomes, retirement savings, and mother’s age at child birth. This study only looks at the impact of VA teachers on grades 4 to 8.
On the design of next-generation wireless networks

Wireless technology has gained great attention from both academia and industry in the past decades. It is believed to be the essential part to achieve the ultimate goal of communications: communication happens whenever, wherever, and to whomever. With the proliferation of different wireless technologies and their vast deployment, we will soon enter the age of ubiquitous computing. Compared to its wireline counterpart, wireless network faces more technical challenges. The wireless channel is less reliable and more variable than fibers and cables. Wireless devices are usually mobile and energy-constraint. Wireless transceiver has limited coverage area. All these characteristics make the resource allocation and algorithm design in wireless networks challenging tasks. This dissertation investigates both issues in different types of next-generation wireless networks. This dissertation first explores the problem of providing a traffic-oblivious routing and scheduling for wireless mesh networks. We present an optimization model which generates a static routing and scheduling policy with worst-case performance guarantees under a range of traffic conditions. Packet-level simulations demonstrate good average performance for the generated routing and scheduling in addition to its worse-case guarantees. Next, we investigate the problem of identifying the maximum capacity of multichannel multi-radio wireless networks, given the number of available channels. We provide an Integer Linear Program (ILP) framework to compute the maximum capacity. We also discuss how to extend the framework to consider heterogeneous network. Then the framework will output the minimum number of radios necessary for each node to fully utilize the available channels, which can be used to identify the bottleneck in the topology, and also indicates the “goodness” of the topology. We also study the integration gain of heterogenous WiFi and WiMAX networks. We propose a generic framework to identify the integration gain, the gain comes from better utilization of resources rather than the addition of resources. For the performance objective, we focus on the max-min throughput fairness, and also briefly cover the proportional fairness. We propose approximation algorithms to achieve different objectives and compute the integration gain based on the framework. The proposed framework can be used to evaluate the potential benefit of combining different heterogeneous wireless networks. The approximation algorithms can be directly used to perform intelligent user association in the integrated network. Last, we study the unique characteristics of cognitive radio networks. Based on our observations, we propose distributed algorithms to perform channel allocation among secondary users. We consider both throughput and fairness, and study their tradeoff. This dissertation makes important contributions by introducing novel models, algorithms and architectures that will help improve the network analysis, planning, and deployment of next-generation wireless networks.
Dataset linking in a multilingual linked open data context

Although, the syntactical and structural heterogeneities among inter-language linked open data (LOD) data sources bring many challenges, entity co-reference resolution in a multilingual linked open data (MLOD) setting is not well studied. In this research, a three phase approach is proposed. First, statistical relational learning (SRL) with factorization of three way tensor is used to compute structural similarity between entities. Second, textual data from the Web of documents is associated in order to increase our knowledge of entities. Through a latent Dirichlet allocation (LDA), entities' textual data is projected into a cross-lingual topic space. This cross-lingual topic space is used to find textual similarities between entities. Third, a belief aggregation strategy is used to combine the structural and textual similarity results into a global similarity score. We have shown by experiments that our algorithm out-performs state of the art approaches based on tensor decomposition for the task of entity co-reference resolution in a MLOD setting.
Efficient Mapping of Virtual Networks onto a Shared Substrate

Virtualization has been proposed as a vehicle for overcoming the growing problem of internet ossification [1]. This paper studies the problem of mapping diverse virtual networks onto a common physical substrate. In particular, we develop a method for mapping a virtual network onto a substrate network in a cost-efficient way, while allocating sufficient capacity to virtual network links to ensure that the virtual network can handle any traffic pattern allowed by a general set of traffic constraints. Our approach attempts to find the best topology in a family of backbone-star topologies, in which a subset of nodes constitute the backbone, and the remaining nodes each connect to the nearest backbone node. We investigate the relative cost-effectiveness of different backbone topologies on different substrate networks, under a wide range of traffic conditions. Specifically, we study how the most cost-effective topology changes as the tightness of pairwise traffic constraints and the constraints on traffic locality are varied. In general, we find that as pairwise traffic constraints are relaxed, the least-cost backbone topology becomes increasingly “tree-like”. We also find that the cost of the constructed virtual networks is usually no more than 1.5 times a computed lower bound on the network cost and that the quality of solutions improves as the traffic locality gets weaker.
The role of the interaction network in the emergence of diversity of behavior

How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.