nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of road traffic fatal accidents using data mining techniques

Roadway traffic safety is a major concern for transportation governing agencies as well as ordinary citizens. In order to give safe driving suggestions, careful analysis of roadway traffic data is critical to find out variables that are closely related to fatal accidents. In this paper we apply statistics analysis and data mining algorithms on the FARS Fatal Accident dataset as an attempt to address this problem. The relationship between fatal rate and other attributes including collision manner, weather, surface condition, light condition, and drunk driver were investigated. Association rules were discovered by Apriori algorithm, classification model was built by Naive Bayes classifier, and clusters were formed by simple K-means clustering algorithm. Certain safety driving suggestions were made based on statistics, association rules, classification model, and clusters obtained.
Chip-scale packaging of hybrid-integrated Si photonic transceiver: Optical I/O core

A chip-scale package structure for Si photonic optical transceivers has been developed. The foot print of the transceiver is 5 mm × 5mm. By using an optical pin array and glass interposer with through-glass vias (TGVs), high density optical and electrical I/O interfaces are configured on one side of the package. The optical pin acts as a vertical waveguide or spot-size converter (SSC). The combination of the optical pin and the O-band multimode transmission provides large misalignment tolerance for the optical interface. The developed optical transceiver has a high degree of usability for various applications, such as multi-chip modules and active optical cables, and is called “optical I/O core.” The optical I/O core demonstrated 25 Gbps/ch error-free operation over a 300-m multimode fiber. The optical I/O core is a promising solution for relieving the I/O bottleneck in high-bandwidth inter-chip data transmission.
OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media

It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.
VPR: A new packing, placement and routing tool for FPGA research

We describe the capabilities of and algorithms used in a ne w FPGA CAD tool, Versatile Place and Route (VPR). In terms of minimizing routing area, VPR outperforms all published FPGA place and route tools to which we can compare. Although the algorithms used are based on pre viously known approaches, we present se veral enhancements that impro ve run-time and quality . We present placement and routing results on a ne w set of lar ge circuits to allo w future benchmark comparisons of FPGA place and route tools on circuit sizes more typical of today’ s industrial designs. VPR is capable of tar geting a broad range of FPGA architectures, and the source code is publicly a vailable. It and the associated netlist translation / clustering tool VPACK have already been used in a number of research projects w orldwide, and should be useful in man y areas of FPGA architecture research.
Dissertacao para obtencao do Grau de Doutor em

This thesis addresses total variation (TV) image restoration and blind image deconvolution. Classical image processing problems, such as deblurring, call for some kind of regularization. Total variation is among the state-of-the-art regularizers, as it provides a good balance between the ability to describe piecewise smooth images and the complexity of the resulting algorithms. In this thesis, we propose a minimization algorithm for TV-based image restoration that belongs to the majorization-minimization class (MM). The proposed algorithm is similar to the known iterative re-weighted least squares (IRSL) approach, although it constitutes an original interpretation of this method from the MM perspective. The problem of choosing the regularization parameter is also addressed in this thesis. A new Bayesian method is introduced to automatically estimate the parameter, by assigning it a non-informative prior, followed by integration based on an approximation of the associated partition function. The proposed minimization problem, also addressed using the MM framework, results on an update rule for the regularization parameter, and can be used with any TV-based image deblurring algorithm. Blind image deconvolution is the third topic of this thesis. We consider the case of linear motion blurs. We propose a new discretization of the motion blur kernel, and a new estimation algorithm to recover the motion blur parameters (orientation and length) from blurred natural images, based on the Radon transform of the spectrum of the blurred images.
Pressure ulcer prevalence, incidence, risk factors, and impact.

Pressure ulcers are a common problem among older adults in all health care settings. Prevalence and incidence estimates vary by setting, ulcer stage, and length of follow-up. Risk factors associated with increased pressure ulcer incidence have been identified. Activity or mobility limitation, incontinence, abnormalities in nutritional status, and altered consciousness are the most consistently reported risk factors for pressure ulcers. Pain, infectious complications, prolonged and expensive hospitalizations, persistent open ulcers, and increased risk of death are all associated with the development of pressure ulcers. The tremendous variability in pressure ulcer prevalence and incidence in health care settings suggests that opportunities exist to improve outcomes for persons at risk for and with pressure ulcers.
MONet: Unsupervised Scene Decomposition and Representation

The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network – in a purely unsupervised manner – to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically meaningful components, such as objects and background elements.
360 Degree multi sensor fusion for static and dynamic obstacles

In this paper an approach for 360 degree multi sensor fusion for static and dynamic obstacles is presented. The perception of static and dynamic obstacles is achieved by combining the advantages of model based object tracking and an occupancy map. For the model based object tracking a novel multi reference point tracking system, called best knowledge model, is introduced. The best knowledge model allows to track and describe objects with respect to a best suitable reference point. It is explained how the object tracking and the occupancy map closely interact and benefit from each other. Experimental results of the 360 degree multi sensor fusion system from an automotive test vehicle are shown.
CredenceLedger: A Permissioned Blockchain for Verifiable Academic Credentials

Blockchain, the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum, is gaining so much attention from different industry stakeholders, governments and research communities. Its application is extending beyond cryptocurrencies and has been exploited in different domains such as finance, E-commerce, Internet of Things (IoT), healthcare, and governance. Some key attributes of the technology are decentralization, immutability, security and transparency. This paper aims to describe how permissioned Blockchain can be applied to a specific educational use case — decentralized verification of academic credentials. The proposed Blockchain-based solution, named ‘CredenceLedger’, is a system that stores compact data proofs of digital academic credentials in Blockchain ledger that are easily verifiable for education stakeholders and interested third party organizations.
Inverse reinforcement learning for video games

Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer manually specify a reward function. It is often easier to provide demonstrations of a target behavior than to design a reward function describing that behavior. Inverse reinforcement learning (IRL) algorithms can infer a reward from demonstrations in low-dimensional continuous control environments, but there has been little work on applying IRL to high-dimensional video games. In our CNN-AIRL baseline, we modify the state-of-the-art adversarial IRL (AIRL) algorithm to use CNNs for the generator and discriminator. To stabilize training, we normalize the reward and increase the size of the discriminator training dataset. We additionally learn a low-dimensional state representation using a novel autoencoder architecture tuned for video game environments. This embedding is used as input to the reward network, improving the sample efficiency of expert demonstrations. Our method achieves high-level performance on the simple Catcher video game, substantially outperforming the CNN-AIRL baseline. We also score points on the Enduro Atari racing game, but do not match expert performance, highlighting the need for further work.
Oncothermia treatment of cancer: from the laboratory to clinic.

Oncothermia is a long-time applied method (since 1989) in oncology. Its clinical results excellently show its advantages, however the details of its mechanism are under investigation even today. The method is based on a self-selective process of energy concentration and targets the membrane of the malignant cell, using the temperature gradient and the beta-dispersion of the membrane proteins. To prove the theory we show the experimental evidences in vitro experiments where we showed the definite difference between the conventional heating and the oncothermia at the same temperature. In the next step, we studied some xenograft nude-mice models, verifying the temperature-dependent and non temperature dependent factors. In addition, the synergic effect with some chemotherapies were studied, having more efficacy of the oncothermia with drugs than the conventional heating. These experiments show the definite advantages of the oncothermia compared to its classical counterpart, acting on the same temperature. We have also proved the beneficial effect of oncothermia treatment in the veterinary practice Oncothermia is applied in numerous clinics and hospitals, and we would like to show some characteristic case-reports and also the clinical benefit on the survival time elongation of liver-, pancreas-, brain-, and lung-tumor-lesions.
Machine learning and pattern matching in physical design

Machine learning (ML) and pattern matching (PM) are powerful computer science techniques which can derive knowledge from big data, and provide prediction and matching. Since nanometer VLSI design and manufacturing have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning and pattern matching techniques in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based simulations and provide reasonably good quality-of-result. In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design.
Making Top Managers : The Role of Elite Universities and Elite Peers

This paper estimates the causal effect of elite college admission on students’ chances of reaching top management positions, and decomposes the total effect into a component attributable to ties formed between college peers and a component attributable to other institutional inputs. I construct a novel dataset linking archival records of applications to elite colleges in Chile to the census of corporate directors and executive managers at publicly traded Chilean firms. I combine these data with a regression discontinuity design to estimate the causal effect of admission on leadership outcomes. Overall, elite admission raises the number of leadership positions students hold by 50 percent, but gains are larger for students who attended elite private high schools and near zero for students who did not. Admitted students from elite high schools are much more likely to work in top management roles with other elite high school students from their college degree program and cohort, but are no more likely to work with elite high school students from the same degree program in other cohorts or other degree programs in the same cohort. I interpret this difference-in-differences analysis of co-management outcomes using a simple model of referral-based hiring. The model suggests that peer ties account for 80 to 100 percent of admissions gains for elite high school students. I thank Joseph Altonji, Eduardo Engel, Francisco Gallego, Martin Hackmann, Justine Hastings, Lisa Kahn, Adam Kapor, Amanda Kowalski, Fabian Lange, Costas Meghir, Christopher Neilson, Craig Palsson, Jamin Speer, Ebonya Washington, and seminar participants at the Yale Labor and Public Economics Workshop for their valuable suggestions. I thank DEMRE Directors Ivan Silva and Eduardo Rodriguez for access to college application data. I thank Pablo Maino, Valeria Maino, and Nadia Vazquez for assistance with archival research. I thank Anely Ramirez for her contributions to data collection and institutional research. I thank the Yale Program in Applied Economics and Policy and the Cowles Structural Microeconomics Program for financial support. All errors are my own.
Building and Evolving Data Warehousing and Business Intelligence artefacts: the Case of Sysco

This paper investigates the use of data warehouse and business intelligence capabilities to integrate with customers in the supply chain and improve insights into customer sales. By making that same data warehouse sales information available to customers, this paper explores how the data warehouse can provide additional value to those customers, eliminating asymmetries of information in the supply chain. In addition, this paper investigates the evolution of data warehousing into business intelligence, expanding sales information to include marketing associate performance analysis generated for internal use. Further, this paper also examines a methodology that was used for building a business intelligence system. Finally, this paper investigates what appears to be a business intelligence driven focus on enterprise resource planning systems. These issues are illustrated using real world data warehousing and business intelligence artifacts developed at SYSCO.
RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images

This paper proposes a method for hand pose estimation from RGB images that uses both external large-scale depth image datasets and paired depth and RGB images as privileged information at training time. We show that providing depth information during training significantly improves performance of pose estimation from RGB images during testing. We explore different ways of using this privileged information: (1) using depth data to initially train a depth-based network, (2) using the features from the depthbased network of the paired depth images to constrain midlevel RGB network weights, and (3) using the foreground mask, obtained from the depth data, to suppress the responses from the background area. By using paired RGB and depth images, we are able to supervise the RGB-based network to learn middle layer features that mimic that of the corresponding depth-based network, which is trained on large-scale, accurately annotated depth data. During testing, when only an RGB image is available, our method produces accurate 3D hand pose predictions. Our method is also tested on 2D hand pose estimation. Experiments on three public datasets show that the method outperforms the state-of-the-art methods for hand pose estimation using RGB image input.
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently, the deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this paper, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool. We formulate the objective function as a constrained optimization problem and solve it using the alternating direction method of multipliers algorithm. Both simulation data and hybrid real data are used to evaluate the proposed method. Quantification results show that our proposed iterative neural network method can outperform the neural network denoising and conventional penalized maximum likelihood methods.
Self-management education: history, definition, outcomes, and mechanisms.

Self-management has become a popular term for behavioral interventions as well as for healthful behaviors. This is especially true for the management of chronic conditions. This article offers a short history of self-management. It presents three self-management tasks--medical management, role management, and emotional management--and six self-management skills--problem solving, decision making, resource utilization, the formation of a patient-provider partnership, action planning, and self-tailoring. In addition, the article presents evidence of the effectiveness of self-management interventions and posits a possible mechanism, self-efficacy, through which these interventions work. In conclusion the article discusses problems and solutions for integrating self-management education into the mainstream health care systems.
DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, i.e., high processing latency and power consumption. In this paper, we propose DeepMon, a mobile deep learning inference system to run a variety of deep learning inferences purely on a mobile device in a fast and energy-efficient manner. For this, we designed a suite of optimization techniques to efficiently offload convolutional layers to mobile GPUs and accelerate the processing; note that the convolutional layers are the common performance bottleneck of many deep learning models. Our experimental results show that DeepMon can classify an image over the VGG-VeryDeep-16 deep learning model in 644ms on Samsung Galaxy S7, taking an important step towards continuous vision without imposing any privacy concerns nor networking cost.
How Do Simple Positive Activities Increase Well-Being ?

Theory and research suggest that people can increase their happiness through simple intentional positive activities, such as expressing gratitude or practicing kindness. Investigators have recently begun to study the optimal conditions under which positive activities increase happiness and the mechanisms by which these effects work. According to our positive-activity model, features of positive activities (e.g., their dosage and variety), features of persons (e.g., their motivation and effort), and person-activity fit moderate the effect of positive activities on well-being. Furthermore, the model posits four mediating variables: positive emotions, positive thoughts, positive behaviors, and need satisfaction. Empirical evidence supporting the model and future directions are discussed.
Item Silk Road: Recommending Items from Information Domains to Social Users

Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before.
 Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.