bluuebunny/crossref_metadata_2025_split huggingface dataset:
The problem of regulating the easy way out – EU money laundering regulation
Authors: Rose Kalle Johannes 
Published: November 2019

Purpose
Current research within law and economics focus on money laundering as an externality problem caused by financial institutions. Thus, when existing research and legislation place the responsibility on financial institutions, it creates a void where it is neglected that clients of financial institutions may, in fact, play a vital role in the problem of externality. However, based on the definition of money laundering, this paper aims to examine and analyze the need to focus on the clients as part of the externality problem with regard to money laundering.

Design/methodology/approach
This paper examines how a lack of regulatory focus on the clients of financial institutions lead to inefficient anti-money laundering regulation. Through a functional approach of law and economics, it analyzes the externality problem of money laundering based on both the legal definition and the economic conditions of the problem.

Findings
Based on the fourth anti-money laundering directive, the paper argues that present regulation has a tendency to focus on financial institutions, thereby not considering the entire scope of the externality problem in money laundering. For regulation to efficiently combat money laundering, it is necessary to place some responsibility on the clients of financial institutions and not solely on the financial institutions. Nevertheless, the inclusion of client responsibility might lead to some legal or economic complications, which need to be subject to further research.

Originality/value
The paper identifies the need for a fundamental change in the perception of the externality problem of money laundering, and thus, presents the required approach to reach an efficient solution.
The hoax of demonetization in Indian economy: a mathematical analysis
Authors: Roy Debasish 
Published: November 2019

Purpose
Over one and half years have passed since the demonetization of Indian economy had occurred on November 8, 2016. The drastic step was initiated by the Prime Minister Narendra Modi with an intention to curb the “huge” circulation of illicit or “black” money of Indian economy by means of withdrawal of high value denominations of Rupees 500 and Rupees 1,000 from the supply of broad money (M3). This step helped to demonetize around 86 per cent value of total money supply leading to an unprecedented chaos in the economy and public life. The long delays in issuing fresh currency notes at the banks and ATMs further deteriorated the sudden economic crisis.

Design/methodology/approach
This research paper is aimed at exploring the proclaimed “efficacy” of demonetization policy as proposed by Reserve Bank of India by means of a mathematical approach and critically examines the effects of demonetization on the illicit money supply of Indian economy on the basis of macroeconomic theory.

Findings
From the mathematical model and related estimates, it may be easily deduced that the Indian policymakers deliberately hurled the masses in one of the gravest economic crises with a clear-cut intention of creating a political gimmick, when in reality, the proportion of illegitimate money supply was not even 1 per cent of total legitimate supply of money.

Originality/value
The analyses and findings related to this paper are based on mathematical modeling and logical interpretations. This paper is free of plagiarism as all the necessary sources and references are properly cited.
Halkbank and OFAC: a sanctions evasion case study
Authors: Scott Benjamin Fraser 
Published: November 2019

Purpose
The purpose of this paper is to use two recent US prosecutions of Turkish nationals for sanctions evasion, the Zarrab and Atilla cases, as case studies of recent developments in US sanctions law and law enforcement.

Design/methodology/approach
This paper uses primary sources (pleadings and other court documents) to articulate the key facts and arguments in the Zarrab and Atilla cases and to explain the sanctions evasion methodologies used by the group. This paper then draws out the lessons of these cases for the practice of financial crime compliance in banking institutions.

Findings
This paper highlights the expanding scope of US sanctions laws and the challenges for banks in complying with them. In particular, it shows the similarities between sanctions evasion and other financial crime methodologies, arguing that banks need to become more interdisciplinary in their operational approach to financial crime.

Originality/value
The Zarrab and Atilla cases are of international significance in sanction law. This paper is the first in-depth case study of these cases from a legal and compliance perspective.
Maintenance strategy selection in a cement industry using analytic network process
Authors: Kurian Mary C. , P.R. Shalij , V.R. Pramod 
Published: November 2019

Purpose
The purpose of this paper is to demonstrate the application of analytic network process (ANP) as a methodology to make multiple criteria decision in selecting the most appropriate maintenance strategy for organizations with critical manufacturing requirements.

Design/methodology/approach
Maintenance strategy selection problems are multiple criteria decision making (MCDM) problems which consist of many qualitative and quantitative characteristics. For solving MCDM problems, the ANP is highly recommended as it considers the interdependent influences among and between the various levels of decision attributes. In this research paper, the ANP method is used to select the optimum maintenance strategy in a cement industry in India.

Findings
The ANP method can be used as an effective tool for the evaluation of possible alternatives in maintenance strategy decision problems by considering the dependency among the strategic factors.

Research limitations/implications
As illustrated in this paper, ANP method can also be used in other industries for adopting the optimum maintenance strategy to enhance the business performance by decreasing the losses associated with equipment effectiveness.

Practical implications
The major contribution of this research is the successful development of the comprehensive ANP model for the cement plant. ANP model incorporates diversified variables of the cement plant supply chain and includes their interdependencies. The proposed ANP model in this paper, not only guides the decision makers in the selection of the best services but also enables them to visualize the impact of various criteria in the arrival of the final solution.

Social implications
The model can be extended to certain other manufacturing sectors as the future scope of research and may assist in obtaining a clear idea regarding the status of current maintenance strategies. It should be carried out with a larger number of firms in India focusing on small and medium firms to confirm these results and reinforce their applicability to these kinds of firms. Studies of such a nature would help in identifying successful organizational factors or successful maintenance practices that lead to superior performance.

Originality/value
This paper explores the value of implementing ANP as a decision making method in maintenance strategy, which is currently not a prevalent method.
Prevalence and Economic Burden of Malnutrition Diagnosis Among Patients Presenting to United States Emergency Departments
Authors: Lanctin David P. , Merced‐Nieves Francheska , Mallett Renee M. , Arensberg Mary Beth , Guenter Peggi , Sulo Suela , Platts‐Mills Timothy F. 
Published: November 2019

ABSTRACTBackgroundMalnutrition is a potentially remediable condition that when untreated contributes to poor health and economic outcomes. While assessment of malnutrition risk is improving, its identification rate and economic burden in emergency departments (EDs) is largely unknown. We sought to determine prevalence and economic burden of diagnosed malnutrition among patients presenting to U.S. EDs.MethodsThis is a retrospective analysis of Healthcare Cost and Utilization Project Nationwide Emergency Department Sample data. Malnutrition prevalence was confirmed via International Classification of Diseases, 9th Edition, diagnosis codes. The economic burden was assessed by comparing probability of hospitalization and the average total charges between propensity‐score matched visits with and without a malnutrition diagnosis.ResultsData from 238 million ED visits between 2006 and 2014 were analyzed. Over this period, the prevalence of diagnosed malnutrition increased for all demographic categories assessed. For older adults (≥65 years), the prevalence increased from 2.5% (2006) to 3.6% (2014). Older age, high‐income community residence, Western region, urban areas, and Medicare coverage were associated with higher diagnosis prevalence. Malnutrition diagnosis was associated with a 4.23 (95% confidence interval [CI] = 3.93 to 4.55) times higher odds of hospitalization and $21,892 higher mean total charges (95% CI = $19,593 to $24,192).ConclusionsWhile malnutrition is currently diagnosed at a low rate in U.S. EDs, the economic burden of malnutrition is substantial in this care setting. Given the potential for systematic malnutrition screening and treatment protocols to alleviate this burden, future research is warranted.
Stereoselective neurochemical, behavioral, and cardiovascular effects of α‐pyrrolidinovalerophenone enantiomers in male rats
Authors: Schindler Charles W. , Thorndike Eric B. , Walters Hailey M. , Walther Donna , Rice Kenner C. , Baumann Michael H. 
Published: November 2019

AbstractThe synthetic cathinone α‐pyrrolidinovalerophenone (α‐PVP) continues to be abused despite being banned by regulatory agencies. The abused formulation of α‐PVP is a racemic mixture consisting of two enantiomers, S‐α‐PVP and R‐α‐PVP. In this study, we investigated the neurochemical, behavioral, and cardiovascular effects of racemic α‐PVP and its enantiomers in male rats. Racemic α‐PVP blocked the uptake of both dopamine and norepinephrine ex vivo, but did not block the uptake of serotonin (5‐HT), at their respective transporters. S‐α‐PVP was slightly more potent than racemic α‐PVP, while R‐α‐PVP was 10 to 20 times less potent at blocking dopamine and norepinephrine uptake. In microdialysis studies, racemic and S‐α‐PVP increased extracellular dopamine levels in the nucleus accumbens, but not levels of 5‐HT. Racemic and S‐α‐PVP also increased locomotor activity. When tested at the same doses, S‐α‐PVP produced larger effects than racemic α‐PVP. R‐α‐PVP also increased extracellular dopamine levels and locomotor activity, but only at 30 times higher doses than S‐α‐PVP. Racemic and S‐α‐PVP were self‐administered by rats at 0.03 mg/kg/injection, whereas R‐α‐PVP was self‐administered at a 10 times higher dose. Dose‐effect determinations following acquisition suggested that R‐α‐PVP was at least 30 times less potent than S‐α‐PVP. Finally, racemic and S‐α‐PVP increased blood pressure and heart rate at doses approximately 30 times less than was required for R‐α‐PVP to produce similar effects. These results show that the neurochemical, behavioral, and cardiovascular effects of racemic α‐PVP most likely reflect the actions of S isomer.
Superior survival of unmanipulated haploidentical haematopoietic stem cell transplantation compared with intensive chemotherapy as post‐remission treatment for children with very high‐risk philadelphia chromosome negative B‐cell acute lymphoblastic leukaemia in first complete remission
Authors: Xue Yu‐juan , Suo Pan , Huang Xiao‐jun , Lu Ai‐dong , Wang Yu , Zuo Ying‐xi , Yan Chen‐hua , Wu Jun , Kong Jun , Zhang Xiao‐hui , Chen Yu‐hong , Jia Yue‐ping , Liu Kai‐yan , Han Wei , Xu Lan‐ping , Zhang Le‐ping , Cheng Yi‐fei 
Published: November 2019

SummaryWe explored the prognostic factors for children with very high‐risk (VHR) Philadelphia chromosome (Ph) negative B‐cell acute lymphoblastic leukaemia (B‐ALL) and compared the therapeutic effects of intensive chemotherapy and unmanipulated haploidentical haematopoietic stem cell transplantation (haplo‐HSCT) as post‐remission treatment in these patients undergoing first complete remission (CR1). A total of 104 paediatric patients with VHR B‐ALL in CR1 were retrospectively enrolled in this study, including 42 receiving unmanipulated haplo‐HSCT (Group A) and 62 receiving ongoing chemotherapy (Group B). Estimated 3‐year overall survival (OS), disease‐free survival (DFS) and cumulative incidence of relapse (CIR) at 36·2 months median follow‐up were 69·5 ± 4·7%, 63·5 ± 4·8% and 32·4 ± 4·7%, respectively. Maintenance of persistent positive or conversion from negative to positive of measurable residual disease (MRD) and chemotherapy were independent risk factors associated with inferior long‐term survival and higher CIR. OS, DFS, and CIR differed significantly between the groups in patients with persistent positive or negative‐to‐positive MRD. Haplo‐HSCT may be an option for children with VHR Ph‐negative B‐ALL in CR1, especially for patients with persistent positive or negative‐to‐positive MRD, and could achieve better survival than intensive chemotherapy as post‐remission treatment.
Learning to Trace: Expressive Line Drawing Generation from Photographs
Authors: Inoue N. , Ito D. , Xu N. , Yang J. , Price B. , Yamasaki T. 
Published: November 2019

AbstractIn this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end‐to‐end trainable fully‐convolutional CNN to learn the model in a data‐driven manner. The model consists of two networks to cope with two sub‐tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications.
Deep Line Drawing Vectorization via Line Subdivision and Topology Reconstruction
Authors: Guo Yi , Zhang Zhuming , Han Chu , Hu Wenbo , Li Chengze , Wong Tien‐Tsin 
Published: November 2019

AbstractVectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high‐resolution images. To deal with a variety of challenging containing different kinds of complex junctions, we propose a two‐phase line drawing vectorization method that analyzes the global and local topology. In the first phase, we subdivide the lines into partial curves, and in the second phase, we reconstruct the topology at junctions. With the overall topology estimated in the two phases, we can trace and vectorize the curves. To qualitatively and quantitatively evaluate our method and compare it with the existing methods, we conduct extensive experiments on not only existing datasets but also our newly synthesized dataset which contains different types of complex and ambiguous junctions. Experimental statistics show that our method greatly outperforms existing methods in terms of computational speed and achieves visually better topology reconstruction accuracy.
Surface Fairing towards Regular Principal Curvature Line Networks
Authors: Chu Lei , Bo Pengbo , Liu Yang , Wang Wenping 
Published: November 2019

AbstractFreeform surfaces whose principal curvature line network is regularly distributed, are essential to many real applications like CAD modeling, architecture design, and industrial fabrication. However, most designed surfaces do not hold this nice property because it is hard to enforce such constraints in the design process. In this paper, we present a novel method for surface fairing which takes a regular distribution of the principal curvature line network on a surface as an objective. Our method first removes the high‐frequency signals from the curvature tensor field of an input freeform surface by a novel rolling guidance tensor filter, which results in a more regular and smooth curvature tensor field, then deforms the input surface to match the smoothed field as much as possible. As an application, we solve the problem of approximating freeform surfaces with regular principal curvature line networks, discretized by quadrilateral meshes. By introducing the circular or conical conditions on the quadrilateral mesh to guarantee the existence of discrete principal curvature line networks, and minimizing the approximate error to the original surface and improving the fairness of the quad mesh, we obtain a regular discrete principal curvature line network that approximates the original surface. We evaluate the efficacy of our method on various freeform surfaces and demonstrate the superiority of the rolling guidance tensor filter over other tensor smoothing techniques. We also utilize our method to generate high‐quality circular/conical meshes for architecture design and cyclide spline surfaces for CAD modeling.
Generating 3D Faces using Multi‐column Graph Convolutional Networks
Authors: Li Kun , Liu Jingying , Lai Yu‐Kun , Yang Jingyu 
Published: November 2019

AbstractIn this work, we introduce multi‐column graph convolutional networks (MGCNs), a deep generative model for 3D mesh surfaces that effectively learns a non‐linear facial representation. We perform spectral decomposition of meshes and apply convolutions directly in the frequency domain. Our network architecture involves multiple columns of graph convolutional networks (GCNs), namely large GCN (L‐GCN), medium GCN (M‐GCN) and small GCN (S‐GCN), with different filter sizes to extract features at different scales. L‐GCN is more useful to extract large‐scale features, whereas S‐GCN is effective for extracting subtle and fine‐grained features, and M‐GCN captures information in between. Therefore, to obtain a high‐quality representation, we propose a selective fusion method that adaptively integrates these three kinds of information. Spatially non‐local relationships are also exploited through a self‐attention mechanism to further improve the representation ability in the latent vector space. Through extensive experiments, we demonstrate the superiority of our end‐to‐end framework in improving the accuracy of 3D face reconstruction. Moreover, with the help of variational inference, our model has excellent generating ability.
Polycube Shape Space
Authors: Zhao Hui , Li Xuan , Wang Wencheng , Wang Xiaoling , Wang Shaodong , Lei Na , Gu Xiangfeng 
Published: November 2019

AbstractThere are many methods proposed for generating polycube polyhedrons, but it lacks the study about the possibility of generating polycube polyhedrons. In this paper, we prove a theorem for characterizing the necessary condition for the skeleton graph of a polycube polyhedron, by which Steinitz's theorem for convex polyhedra and Eppstein's theorem for simple orthogonal polyhedra are generalized to polycube polyhedra of any genus and with non‐simply connected faces. Based on our theorem, we present a faster linear algorithm to determine the dimensions of the polycube shape space for a valid graph, for all its possible polycube polyhedrons. We also propose a quadratic optimization method to generate embedding polycube polyhedrons with interactive assistance. Finally, we provide a graph‐based framework for polycube mesh generation, quadrangulation, and all‐hex meshing to demonstrate the utility and applicability of our approach.
Two‐phase Hair Image Synthesis by Self‐Enhancing Generative Model
Authors: Qiu H. , Wang C. , Zhu H. , Zhu X. , Gu J. , Han X. 
Published: November 2019

AbstractGenerating plausible hair image given limited guidance, such as sparse sketches or low‐resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image‐to‐image translation networks can generate recognizable results, but finer textures are usually lost and blur artifacts commonly exist. In this paper, we propose a two‐phase generative model for high‐quality hair image synthesis. The two‐phase pipeline first generates a coarse image by an existing image translation model, then applies a re‐generating network with self‐enhancing capability to the coarse image. The self‐enhancing capability is achieved by a proposed differentiable layer, which extracts the structural texture and orientation maps from a hair image. Extensive experiments on two tasks, Sketch2Hair and Hair Super‐Resolution, demonstrate that our approach is able to synthesize plausible hair image with finer details, and reaches the state‐of‐the‐art.
Unsupervised Dense Light Field Reconstruction with Occlusion Awareness
Authors: Ni Lixia , Jiang Haiyong , Cai Jianfei , Zheng Jianmin , Li Haifeng , Liu Xu 
Published: November 2019

AbstractLight field (LF) reconstruction is a fundamental technique in light field imaging and has applications in both software and hardware aspects. This paper presents an unsupervised learning method for LF‐oriented view synthesis, which provides a simple solution for generating quality light fields from a sparse set of views. The method is built on disparity estimation and image warping. Specifically, we first use per‐view disparity as a geometry proxy to warp input views to novel views. Then we compensate the occlusion with a network by a forward‐backward warping process. Cycle‐consistency between different views are explored to enable unsupervised learning and accurate synthesis. The method overcomes the drawbacks of fully supervised learning methods that require large labeled training dataset and epipolar plane image based interpolation methods that do not make full use of geometry consistency in LFs. Experimental results demonstrate that the proposed method can generate high quality views for LF, which outperforms unsupervised approaches and is comparable to fully‐supervised approaches.
Real‐time Indirect Illumination of Emissive Inhomogeneous Volumes using Layered Polygonal Area Lights
Authors: Kuge Takahiro , Yatagawa Tatsuya , Morishima Shigeo 
Published: November 2019

AbstractIndirect illumination involving with visually rich participating media such as turbulent smoke and loud explosions contributes significantly to the appearances of other objects in a rendering scene. However, previous real‐time techniques have focused only on the appearances of the media directly visible from the viewer. Specifically, appearances that can be indirectly seen over reflective surfaces have not attracted much attention. In this paper, we present a real‐time rendering technique for such indirect views that involves the participating media. To achieve real‐time performance for computing indirect views, we leverage layered polygonal area lights (LPALs) that can be obtained by slicing the media into multiple flat layers. Using this representation, radiance entering each surface point from each slice of the volume is analytically evaluated to achieve instant calculation. The analytic solution can be derived for standard bidirectional reflectance distribution functions (BRDFs) based on the microfacet theory. Accordingly, our method is sufficiently robust to work on surfaces with arbitrary shapes and roughness values. In addition, we propose a quadrature method for more accurate rendering of scenes with dense volumes, and a transformation of the domain of volumes to simplify the calculation and implementation of the proposed method. By taking advantage of these computation techniques, the proposed method achieves real‐time rendering of indirect illumination for emissive volumes.