nils-herrmann/clean_scirepeval huggingface dataset:
A Critical Evaluation and Framework of Business Process Improvement Methods
Venue: Bus. Inf. Syst. Eng.

The redesign of business processes has a huge potential in terms of reducing costs and throughput times, as well as improving customer satisfaction. Despite rapid developments in the business process management discipline during the last decade, a comprehensive overview of the options to methodologically support a team to move from as is process insights to to be process alternatives is lacking. As such, no safeguard exists that a systematic exploration of the full range of redesign possibilities takes place by practitioners. Consequently, many attractive redesign possibilities remain unidentified and the improvement potential of redesign initiatives is not fulfilled. This systematic literature review establishes a comprehensive methodological framework, which serves as a catalog for process improvement use cases. The framework contains an overview of all the method options regarding the generation of process improvement ideas. This is established by identifying six key methodological decision areas, e.g. the human actors who can be invited to generate these ideas or the information that can be collected prior to this act. This framework enables practitioners to compose a well considered method to generate process improvement ideas themselves. Based on a critical evaluation of the framework, the authors also offer recommendations that support academic researchers in grounding and improving methods for generating process improvement ideas. Next to the framework and its critical evaluation, this review investigates the research procedures of the studies that were used to create the framework. Related to this investigation, academic researchers can find additional guidance regarding procedures for building and evaluating new methods.
Influence of connected and autonomous vehicles on traffic flow stability and throughput

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real time information about the surrounding traffic condition and the traffic management center's decisions. Such information is expected to improve drivers' efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model's assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.
Learning temporally correlated representations using lstms for visual tracking
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of the objects in long sequences. The recurrent network is pre trained offline with auxiliary data and then online optimized to adapt to the target specific object. A structured SVM is employed to account for the temporally correlated object appearance as well as distinguish the object from background distraction. Experiment results not only show that the appearance and dynamic patterns of the objects can be characterized via temporally correlated feature learning, but also demonstrate that the proposed tracking algorithm performs favorably against the state of the art methods.
Shape Augmented Regression for 3D Face Alignment
Venue: ECCV Workshops

2D face alignment has been an active topic and is becoming mature for real applications. However, when large head pose exists, 2D annotated points lose geometric correspondence with respect to actual 3D location. In addition, local appearance varies more dramatically when subjects are with large pose or under various illuminations. 3D face alignment from 2D images is a promising solution to tackle this problem. 3D face alignment aims to estimate the 3D face shape which is consistent across all poses. In this paper, we propose a novel 3D face alignment method. This method consists of two steps. First, we perform 2D landmark detection based on the shape augmented regression. Second, we estimate the 3D shape using the detected 2D landmarks and 3D deformable model. Experimental results on benchmark database demonstrate its preferable performances.
Reflective Regression of 2D 3D Face Shape Across Large Pose
Venue: BMVC

In this paper we present a novel reflective method to estimate 2D 3D face shape across large pose. We include the knowledge that a face is a 3D object into the learning pipeline, and formulate face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are learned by an extended cascaded pose regression framework. In order to improve algorithm robustness in difficult poses, we introduce a reflective invariant metric for failure alert. We investigate the relation between reflective variance and face misalignment error, and find there is strong correlation between them. Consequently this finding is exploited to provide feedback to our algorithm. For the samples predicted as failure, we restart the algorithm with better initialisations based on explicit head pose estimation, which enhances the possibility of convergence. Extensive experiments on the challenging AFLW and AFW datasets demonstrate that our approach achieves superior performance over the state of the art methods.
Automated generation of the computer aided design model from the system structure for mechanical systems based on systems modeling language

The system level structure model significantly affects the subsequent development process. However, such a model cannot be used directly due to its document based representation. A significant gap exists between the system level model and the detailed domain specific model. The model integration method significantly facilitates the design scenario involving different design phases because it supports tasks such as complexity management and consistency maintenance. However, the automated generation of the computer aided design model from the systems modeling language based structure model of the mechanical system is a typical issue. In this study, a meta model based approach is proposed to generate the initial computer aided design model represented by the STEP file from the systems modeling language based system structure model. A geometry related system structure modeling method is described, and the triple graph grammar is introduced to transform the systems modeling language model into the EXPRESS model. An automated generation algorithm is proposed to construct the initial computer aided design model for mechanical systems against the ISO 10303 203 (AP 203) standard by parsing the EXPRESS model. Our approach facilitates model integration and helps reduce the gap between the system level structure model and the computer aided design model. This reduction would be beneficial for eliminating misunderstandings between different designers and thus to accelerate the design process. Finally, an example of an engine is provided to illustrate the proposed method.
Alfred Tarski and the "Concept of Truth in Formalized Languages"

Part 1.  Chapter 1. Remarks on the objective of the dissertation. Chapter 2. Remarks on the construction and the content of the dissertation. Chapter 3. The text versions used in the present dissertation. Chapter 4. General terminological questions and translational issues. Chapter 5.  Chapter 6. Materially adequate and formally correct definition. Chapter 7. Sentence vs. Statement. Chapter 8. Quantification. Chapter 9. Consistency.  Chapter 11. Metatheory vs. metascience. Part 2. Commentary. Chapter 12.  Chapter 13. The Concept of True Sentence in Everyday or Colloquial Language. Chapter 14. Formalized Languages, especially the Language of the Calculus of Classes. Chapter 15. The Concept of True Sentence in the Language of the Calculus of Classes. Chapter 16. The Concept of True Sentence in Languages of Finite Order. Chapter 17. The Concept of True Sentence in Languages of Infinite Order. Chapter 18.  Chapter 19. Postscript.
Impact of health research on advances in knowledge, research capacity building and evidence informed policies: a case study on maternal mortality and morbidity in Brazil.
Venue: Sao Paulo medical journal Revista paulista de medicina

National health research systems aim to generate high quality knowledge so as to maintain and promote the population's health. This study aimed to analyze the impact of maternal mortality/morbidity research funded by the Brazilian Ministry of Health and institutional partners, on the dimensions: advancing in knowledge, research capacity building and informing decision making, within the framework of the Canadian Academy of Health Sciences.  Descriptive study based on secondary data, conducted at a public university.  The advancing in knowledge dimension was estimated from the principal investigators' publication counts and h index. Data on research capacity building were obtained from the Ministry of Health's information system. The informing decision making dimension was analyzed from citations in Stork Network (Rede Cegonha) documents.  Between 2002 and 2010, R$ 21.6 million were invested in 128 maternal mortality/morbidity projects. Over this period, the principal investigators published 174 articles, resulting in an h index of 35, thus showing progress in the advancing in knowledge dimension. Within the research capacity building dimension, training of 71 students (undergraduate/postgraduate) was observed. Progress in the informing decision making dimension was modest: 73.5% of the 117 citations in the Stork Network documents were institutional documents and norms. One of the projects funded, the 2006/7 National Demography and Health Survey, was cited in program documents.  Impacts were shown in the advancing in knowledge and research capacity building dimensions. The health research system needs to incorporate research for evidence informed policies.
Professor Forcing: A New Algorithm for Training Recurrent Networks
Venue: NIPS

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one step ahead predictions to do multi step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade offs between Professor Forcing and Scheduled Sampling are discussed. We produce T SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.
Column Store for GWAC: A High cadence, High density, Large scale Astronomical Light Curve Pipeline and Distributed Shared nothing Database

The ground based wide angle camera array (GWAC) a part of the SVOM space mission, will search for various types of optical transients by continuously imaging a field of view (FOV) of 5000 degrees2 every 15 s. Each exposure consists of 36 x 4k x 4k pixels, typically resulting in 36 x ~175,600 extracted sources. For a modern time domain astronomy project like GWAC, which produces massive amounts of data with a high cadence, it is challenging to search for short timescale transients in both real time and archived data, and to build long term light curves for variable sources. Here, we develop a high cadence, high density light curve pipeline (HCHDLP) to process the GWAC data in real time, and design a distributed shared nothing database to manage the massive amount of archived data which will be used to generate a source catalog with more than 100 billion records during 10 years of operation. First, we develop HCHDLP based on the column store DBMS of MonetDB, taking advantage of MonetDB's high performance when applied to massive data processing. To realize the real time functionality of HCHDLP, we optimize the pipeline in its source association function, including both time and space complexity from outside the database (SQL semantic) and inside (RANGE JOIN implementation) as well as in its strategy of building complex light curves. The optimized source association function is accelerated by three orders of magnitude. Second, we build a distributed database using a two level time partitioning strategy via the MERGE TABLE and REMOTE TABLE technology of MonetDB. Intensive tests validate that our database architecture is able to achieve both linear scalability in response time and concurrent access by multiple users. In summary, our studies provide guidance for a solution to GWAC in real time data processing and management of massive data.
Sexual and Overall Quality of Life Improvements After Surgical Correction of "Buried Penis"
Venue: Annals of plastic surgery

Buried penis" is an increasing burden in our population with many possible etiologies. Although surgical correction of buried penis can be rewarding and successful for the surgeon, the psychological and functional impact of buried penis on the patient is less understood.  The study's aim was to evaluate the sexual satisfaction and overall quality of life before and after buried penis surgery in a single surgeon's patient population using a validated questionnaire (Changes in Sexual Functioning Questionnaire short form) Results Using Likert scales generated from the questionnaire and 1 tailed paired t test analysis, we found that there was significantly improved sexual function after correction of a buried penis. Variables individually showed that there was significant improvement with sexual pleasure, urinating, and with genital hygiene postoperatively. There were no significant differences concerning frequency of pain with orgasms.  Surgical correction of buried penis significantly improves the functional, sexual, and psychological aspects of patient's lives.
Penis Allotransplantation in Beagle Dog
Venue: BioMed research international

This is an original research of penis allotransplantation. The paper presents an experiment allogenic penis transplantation model in Beagles, with a focus on recovery of blood supply and changes in tissue architecture. Twenty adult Beagles were allocated to 10 pairs for penile transplantation. After operation, the skin and glans were observed. If adverse symptoms occurred, the transplanted penis was resected and pathologically examined. Frequency of urination, urinary stream, and patency level were recorded 7 days after transplantation. Cystourethrography was performed on Day 10. The transplanted penises were resected on Day 14 for pathological examination. The research showed that transplanted penises survived after allotransplantation, and the dogs regained urination ability. Penis autotransplantation in Beagles is feasible. This preliminary study shows a potential for application of this new procedure for penis transplantation in humans.
The Spin Plane Double Probe Electric Field Instrument for MMS

The Spin plane double probe instrument (SDP) is part of the FIELDS instrument suite of the Magnetospheric Multiscale mission (MMS) Together with the Axial double probe instrument (ADP) and the Electron Drift Instrument (EDI) SDP will measure the 3 D electric field with an accuracy of 0.5 mV/m over the frequency range from DC to 100 kHz. SDP consists of 4 biased spherical probes extended on 60 m long wire booms 90 apart in the spin plane, giving a 120 m baseline for each of the two spin plane electric field components. The mechanical and electrical design of SDP is described, together with results from ground tests and calibration of the instrument.
Watertight ray traversal with reduced precision
Venue: High Performance Graphics

Reduced precision bounding volume hierarchies and ray traversal can significantly improve the efficiency of ray tracing through low cost dedicated hardware. A key approach to enabling reduced precision computations during traversal is to translate the ray origin closer to the bounding volume hierarchy node after each traversal step. However, this approach precludes sharing of intersection computations between a parent node and its two children, which is an important optimization. In this paper, we introduce a novel traversal algorithm that addresses this limitation and achieves a significant reduction in the computational complexity of traversal compared to previous approaches. We also include an analysis that shows how our algorithm guarantees watertight intersections which is a key requirement for robust image quality, especially with reduced precision traversal where numerical errors can be large.
Performance Comparison of Bounding Volume Hierarchies and Kd Trees for GPU Ray Tracing
Venue: Comput. Graph. Forum

We present a performance comparison of bounding volume hierarchies and kd trees for ray tracing on many core architectures (GPUs) The comparison is focused on rendering times and traversal characteristics on the GPU using data structures that were optimized for very high performance of tracing rays. To achieve low rendering times, we extensively examine the constants used in termination criteria for the two data structures. We show that for a contemporary GPU architecture (NVIDIA Kepler) bounding volume hierarchies have higher ray tracing performance than kd trees for simple and moderately complex scenes. On the other hand, kd trees have higher performance for complex scenes, in particular for those with high depth complexity. Finally, we analyse the causes of the performance discrepancies using the profiling characteristics of the ray tracing kernels.
Multi bounding volume hierarchies for ray tracing pipelines
Venue: SIGGRAPH Asia Technical Briefs

High performance ray tracing on CPU is now largely based on Multi Bounding Volume Hierarchy (MBVH) trees. We apply MBVH to a fixed function ray tracing accelerator architecture. According to cycle level simulations and power analysis, MBVH reduces energy per frame by an average of 24% and improves performance per area by 19% in scenes with incoherent rays, due to its compact memory layout which reduces DRAM traffic. With primary rays, energy efficiency improves by 15% and performance per area by 20%
Efficient construction of bounding volume hierarchies into a complete octree for ray tracing
Venue: Comput. Animat. Virtual Worlds

This paper proposes an efficient construction scheme for bounding volume hierarchies based on a complete tree. This construction offers up to 4x faster construction times than binned surface area heuristic and offers competitive ray traversal performance. The construction is fully parallelized on x86 CPU architectures; it takes advantage of the eight wide vector units and exploits the advance vector extensions available for current x86 CPU architectures. Additionally, this work presents a clustering algorithm for grouping primitives, which can be computed in linear time O(n) Furthermore, this construction uses the graphics processing unit to perform intensive operations efficiently.
Object Discovery: Soft Attributed Graph Mining
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

We categorize this research in terms of its contribution to both graph theory and computer vision. From the theoretical perspective, this study can be considered as the first attempt to formulate the idea of mining maximal frequent subgraphs in the challenging domain of messy visual data, and as a conceptual extension to the unsupervised learning of graph matching. We define a soft attributed pattern (SAP) to represent the common subgraph pattern among a set of attributed relational graphs (ARGs) considering both their structure and attributes. Regarding the differences between ARGs with fuzzy attributes and conventional labeled graphs, we propose a new mining strategy that directly extracts the SAP with the maximal graph size without applying node enumeration. Given an initial graph template and a number of ARGs, we develop an unsupervised method to modify the graph template into the maximal size SAP. From a practical perspective, this research develops a general platform for learning the category model (i.e. the SAP) from cluttered visual data (i.e. the ARGs) without labeling "what is where," thereby opening the possibility for a series of applications in the era of big visual data. Experiments demonstrate the superior performance of the proposed method on RGB/RGB D images and videos.
Unsupervised Visual Representation Learning by Graph Based Consistent Constraints
Venue: ECCV

Learning rich visual representations often require training on datasets of millions of manually annotated examples. This substantially limits the scalability of learning effective representations as labeled data is expensive or scarce. In this paper, we address the problem of unsupervised visual representation learning from a large, unlabeled collection of images. By representing each image as a node and each nearest neighbor matching pair as an edge, our key idea is to leverage graph based analysis to discover positive and negative image pairs (i.e. pairs belonging to the same and different visual categories) Specifically, we propose to use a cycle consistency criterion for mining positive pairs and geodesic distance in the graph for hard negative mining. We show that the mined positive and negative image pairs can provide accurate supervisory signals for learning effective representations using Convolutional Neural Networks (CNNs) We demonstrate the effectiveness of the proposed unsupervised constraint mining method in two settings: (1) unsupervised feature learning and (2) semi supervised learning. For unsupervised feature learning, we obtain competitive performance with several state of the art approaches on the PASCAL VOC 2007 dataset. For semi supervised learning, we show boosted performance by incorporating the mined constraints on three image classification datasets.