Miracle-dz/UniFAR-FTUs huggingface dataset:
First-Stanza Quotation in Old Norse Prosimetrum

alvíssmál 7 (1997): 61–80 When prosimetrum involves the quotation of poetry — as medieval Scandinavian prosimetrum almost invariably does1 — the narrator’s voice is at once in competition with another voice, which through its poetic form is graced with significance and authority. Verse quotations in sagas are conventionally very short, most typically of only one stanza, but occasionally of two, three, or more stanzas. As the evidence of other records demonstrates, however, a verse presented by a saga-narrator as a lausavísa, or singlestanza composition, may well be an excerpt from a longer poem.2 Sometimes acknowledgement is made of the loosening of the stanza from the whole poem through the narrator’s mention of the poem’s name, particularly in kings’ sagas where praise and memorial poems are frequently cited to verify aspects of an account,3 but even in this genre — avowedly indebted to the existence of whole
200um-class Polymeric Microneedle Fabricated by a Micro Injection Molding Technique

Microneedles for the usage in bio-applications have been fabricated using various kinds of materials, including ceramics, metals, and polymers. The manufacturing cost must be taken into account for low-cost applications such as daily multiple usages for patients. In this work, the designs of a 200um-class microneedle and a runner system suitable for the successful fabrication using micro injection molding have been proposed. The mass-production of microneedles can be accomplished using micro injection molding.
Machine performance assessment based on integrated signal redundancy and bootstrap technique

Prediction of machine performance based on current states and historical data has been a challenging issue in a predictive maintenance of machine performance assessment. Traditional methods mainly focused on developing prediction algorithms, rather than paying attention to the understanding of the data. This paper presents an innovative method to quantitatively evaluate the predictability of machinery performance assessment based on information redundancy and a statistical simulation technique. The predictability of a series of simulated signals including periodicity signal, simulated periodicity signal, chaos signal and random white noise signal were simulated for testing the correctness of the proposed method. In addition, practical vibration data were analyzed and a high-precision prediction was achieved by computing the redundancies of these sample sequences. Results indicate that evaluation tool can present a clear indication of machine performance predictability and therefore can guide the development and selection of prediction algorithms.
Research on Coordinated Multi-point Precoding Algorithm Based on QR-SVD

ABSTRACT. In LTE-A system, the existing coordinated multi-point (CoMP) multiuser precoding algorithms can not suppress multi-user, intra-user and noise interference at the same time, and its computational complexity is high. To overcome the conventional precoding algorithms limitations this paper proposes a novel linear precoding algorithm based on QR-SVD suitable for CoMP transmission system. Firstly, a noise factor is introduced into the complementary channel matrix to exclude the impacts of multi-user and noise interference on the system. Besides, the introduction of low-complexity matrix decomposition method (QR) reduces computational complexity. Finally, by applying singular value decomposition (SVD) to equivalent channel matrix, intra-user interference is supressed, meanwhile, complete precoding matrix and decoding matrix is obtained. Simulation results show that the proposed algorithm has certain advantages in terms of computational complexity, system capacity and bit error rate (BER), compared with other precoding algorithms.
A Highly Reliable CAPTCHA Recognition Algorithm Based on Rejection

CAPTCHA is a kind of network security mechanism that blocks machines from abusing network resource owned by human. Studying the recognition of CAPTCHA can help to find its hidden defects, and thus make it securer. To read closely-connected CAPTCHA that can hardly be recognized by methods of state of art, this paper brought up a new recognition algorithm based on rejection. During the process of this algorithm, recurrent neural network (RNN) was first used to recognize the unknown CAPTCHA. Then, to make the recognition results reliable, a new rejection algorithm was brought up. Data dimension reduction was also performed on rejection features. Experiment results show the following three points: Firstly, our new recognition algorithm can recognize closely-connected CAPTCHA with high reliability. Secondly, the new rejection algorithm is superior to other methods of state of art. Lastly, data dimension reduction algorithm can improve the performance of the rejection algorithm, thus making the recognition results more reliable.
New computer geometric modeling approach with filament assembly model for woven fabric structures

Abstract This paper presents a new computer geometric modeling approach for three-dimensional woven fabric structures. Pierce’s geometry of the weave fabric of yarn from an arc abscissa (Peirce’s) model is presented. Then, new algorithms with a filament assembly model for a single yarn composed of many filaments by twisting along the crimp shape in the warp/weft is developed. The concept of a virtual location is used to simulate the fiber distributions in the yarn cross-section. Each cross-section is rotated along the yarn length by a pre-determined amount to allow for the yarn twist. The curve of each filament in each two successive cross-sections is approximated by NURBS and then each curve is created by sweeping a closed curve along the centerline of the yarn path. The method described is demonstrated by the CAD model of woven fabrics with plain and twill weaves. The simulated woven fabrics using this approach can demonstrate a wider variety and improved visual simulations of real woven fabric and can then be further generalized for different and more complicated fabrics. The method is necessary as an input to many computational models, such as modeling the mechanical properties or the heat transfer of fabrics or composite parts.
An Extensible , Scalable , and Continuously Adaptive Machine Learning Layer for the Internet-of-Things

Internet of Things environments are widespread and often include different sources of sensor data, which can be used for machine learning applications. However, in IoT settings, the ambient environment itself is not static but changes over time, leading to variations in the sensor data and thereby decrease in accuracy for any ML application using that data. Furthermore, in complex environments, applications are exposed to various new conditions over time. Each IoT environment also has unique sensors and devices present. All of these special cases, which are symptomatic of IoT environments, make ML based applications very challenging. This thesis presents an IoT centric, end-to-end Machine Learning Layer which addresses these challenges. Our ML Layer architecture enables each of the aspects (training and prediction serving) to provide feedback to each other leading to a continuous cycle. Our ML Layer includes a flexible model definition that allows us to incorporate any type of model or ML framework, and we initially implement several common models for frameworks such as Scikit Learn and Keras. Initially, the ML Layer optimizes models, and performs ensemble model selection, based on expressive policies. Over time, as a part of its autonomous feedback loop our ML Layer is able to automatically identify different patterns in environmental data, and continuously adapt these models based on this feedback solicited from users. In addition, our system performs dimensionality reduction using environmental data over longer periods to improve prediction efficiency. The system is designed to be general purpose to accommodate any type or combination of IoT data sources. Our Machine Learning Layer for IoT is also a fully managed service, designed to be flexible and adaptive to facilitate ease of use and deployability. It can be deployed in a variety of settings (including smart homes and buildings) which require specialized learning on the spot to fit the environment and continuously improve accuracy. Acknowledgements I would like to thank my advisor Yuvraj Agarwal for his support. He has provided valuable guidance and suggestions throughout the entire process. Furthermore, he spent valuable time both in and outside our weekly meetings, which was very helpful for my project. I appreciate the opportunity to work with him very much. I would also like to thank Vyas Sekar for agreeing to be the second faculty member for my thesis committee, reviewing my thesis document, and taking the time to come to my presentation. Finally, I would also like to thank Peter Steenkiste and Tracy Farbacher for the help related to the program.
Multiple transport disadvantages : A cluster analysis of the social distribution of accessibility and pedestrian mobility in a metropolitan area

The concept of social justice is often used in the assessment of the social and environmental sustainability of urban transport policies. This paper brings together two strands of the literature on this topic: the role of accessibility in social inclusion and the distribution of the environmental impacts of transport. Using the Lisbon Metropolitan Area as case study, we test if there are cumulative social inequalities in terms of accessibility and pedestrian mobility, considering that the latter depends on local environmental quality. GIS methods are used to estimate a set of neighbourhood-level indicators such as private and public transport accessibility to jobs and urban facilities, community severance, and pedestrian exposure to traffic noise. Neighbourhoods are then classified based on the scores of those indicators in two moments in time. Six clusters can be identified. Accessibility increases and pedestrian mobility decreases as we move from the ‘main centre’ towards the ‘suburban’, ‘small centres’, ‘semi-rural’ and ‘rural’ clusters. A sixth cluster is labelled ‘multiple disadvantages’ and groups dispersed neighbourhoods that fare poorly in all indicators. The clusters are then characterized in terms of their socio-economic composition. Central areas have elderly populations, while rural areas and the “multiple disadvantaged” cluster have low-qualified populations. We also tested if disadvantages based on a neighbourhood’s location persist after accounting for the daily destinations and travel modes used by its population. It was found that ‘multiple disadvantaged’ areas have the poorest scores in time to work, effects of congestion, and pedestrian noise exposures for commuters on the way to work. The main conclusion is that ‘hotspots’ of multiple transport-related disadvantages tend to have populations traditionally at risk of social exclusion.
Unsupervised Monitoring of Networkand Service Behaviour Using SelfOrganizing Maps

Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. This paper investigates the capability of the Self Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doing so, the aim is to understand how far such an approach could be pushed to analyze the network traffic, and to detect malicious behaviours in the wild. To this end, three different unsupervised SOM training scenarios for different data acquisition conditions are designed, implemented and evaluated. The approach is evaluated on publicly available network traffic (flows) and web server access (web requests) datasets. The results show that the approach has a high potential as a data analytics tool on unknown traffic/web service requests, and unseen attack behaviours. Malicious behaviours both on network and service datasets used could be identified with a high accuracy. Furthermore, the approach achieves comparable performances to that of popular supervised and unsupervised learning methods in the literature. Last but not the least, it provides unique visualization capabilities for enabling a simple yet effective network/service data analytics for security management.
Measurements of routing protocols performance for high-throughput computer networks in OPNET environment

In recent years, we are observing the rapid development of information systems, especially distributed systems. One of the important components of these systems is the communication infrastructure, consisting of both the topology and routing protocols. The article presents an analysis of commonly used routing protocols in high-throughput computer networks conducted in OPNET environment. Possibilities of analysis of these protocols for different topologies are presented. From the point of the functioning of the routing protocol performance view, measurements were made for relevant parameters such as convergence time, bandwidth etc. In addition, there was proposed the use of the butterfly topology to create an effective environment for routing in computer networks based on the classic three layer connection system.
Evaluating the Flicker caused byElectric Arc Furnacethrough the Multi-scale Entropy MSE Algorithm SampEn

This paper presents a new method to evaluate the Arc voltage quality in electric arc furnace (EAF). It has been shown that is possible to allow the representation of nonlinear dynamics and coupling effects between different waves for irregularity of electrical signals issued by electrical components though multi-scale entropy algorithm SampEn. This work shows the behavior of the feature extraction with SampEn when it’s used to evaluate the signals that deviate the arc voltage in (EAF). Flicker effect with voltage imbalances are analyzed by means of the SampEn concept.
AS-level Internet Topology Degree and Connectivity Analysis

The Internet topology,especially the AS-level topology,is the hotspot of the research.We can comprehend well the inner connective mechanism of the network by researching the evolvement trend of the Internet topology.In this thesis,the research task is based on the massive data authorized by CAIDA(The Cooperative Association for Internet Data Analysis)Skitter project and the data's time span is from January 2004 to June 2008.This paper introduced the essential basic conceptions first,then carried out the evolvement analysis of the node average degree,the maximum node degree,the average degree of the top-degree node,the connectivity of rich-club and the clustering coefficient.It is discovered that the affect of the top-degree nodes and the connectivity between the top-degree nodes is descended by the time movement.However the network puts up still obvious rich-club character and clustering coefficient totally.
RUNNING HEAD: COMBINING PARENT AND TEACHER TRAINING FOR EARLY ADHD 1 TITLE: COMBINING PARENT AND TEACHER TRAINING FOR EARLY ADHD: A RANDOMIZED STUDY OF EFFECTIVENESS COMBINING PARENT AND TEACHER TRAINING FOR ADHD

Parent training (PT) has been documented as an effective intervention for early ADHD based on parental assessment, but documentation of transfer of its results outside the family context has been limited. Due to the cross-contextual impairments typically caused by ADHD, it has been proposed that PT in combination with an intervention in kindergarten or school based on systematic teacher training (TT) would be better suited. The objective of this study was to evaluate the effectiveness of a combination of PT and TT in a community sample of children with early ADHD problems. Families of 64 3-8-year old children were randomized to either PT alone with “The Incredible Years” PT or to a combined PT + TT intervention. Moderate to large pre-post effect sizes on mother-reported outcomes were revealed in both groups, in line with those generally reported in the literature on PT interventions for early ADHD. With an n of 64, the current study was powered to detect superiority of the PT+TT intervention if the difference was of a moderate effect size (Cohen’s d= 0.5). There were no statistically significant differences between the two groups on any of the outcome measures as rated by parents or teachers. In conclusion, the study did not provide support for the combined PT + TT intervention for early ADHD problems as a superior intervention in comparison with PT alone.
Analytical Solution of Steady State Substrate Concentration of an Immobilized Enzyme Kinetics by Laplace Transform Homotopy Perturbation Method

The nonlinear dynamical system modeling the immobilized enzyme kinetics with Michaelis-Menten mechanism for an irreversible reaction without external mass transfer resistance is considered. Laplace transform homotopy perturbation method is used to obtain the approximate solution of the governing nonlinear differential equation, which consists in determining the series solution convergent to the exact solution or enabling to built the approximate solution of the problem. Numerical solutions are obtained and the results are discussed graphically. The method allows to determine the solution in form of the continuous function, which is significant for the analysis of the steady state dimensionless substrate concentration with dimensionless distance on the different support materials.
The DrFurby classifier submission to the process discovery contest

To check the current state-of-the-art in process discovery, a contest has been set up. In this contest, 10 undisclosed process models were created by the organizers. For every created process model, the organizers created two logs: a training log containing 1000 traces and a test log containing 20 traces. Both logs were disclosed to the participants. The challenge for the participants was to classify every trace from every test log whether or not it fits the corresponding original process model, that is, whether or not it can be replayed perfectly by that process model. As the original process model was not disclosed, the participants had to first discover an as-good-as-possible process model from the training log, and second use this discovered process model to classify every trace from the corresponding test log. The main assumption here is that a better discovery technique discovers a better (that is, more matching to the original) process model, which then results in a better classification. In the end, the one that classifies the most traces correctly, wins the contest. This paper presents a submission to this contest that, in the end, classifies 193 out of 200 traces correctly.
A non-target structural displacement measurement method using advanced feature matching strategy

Most of the existing vision-based displacement measurement methods require manual speckles or targets to improve the measurement performance in non-stationary imagery environments. To minimize the use of manual speckles and targets, feature points regarded as virtual markers can be utilized for non-target measurement. In this study, an advanced feature matching strategy is presented, which replaces the handcrafted descriptors with learned descriptors called Visual Geometry Group, of the University of Oxford descriptors to achieve better performance. The feasibility and performance of the proposed method is verified by comparative studies with a laboratory experiment on a two-span bridge model and then with a field application on a railway bridge. The proposed approach of integrated use of Scale Invariant Feature Transform and Visual Geometry Group improved the measurement accuracy by about 24% when compared with the commonly used existing feature matching-based displacement measurement method using Scale Invariant Feature Transform feature and descriptor.
Parthenolide induces apoptosis and inhibits proliferation of human 786-O kidney cancer cells in vitro

Background: Clear cell renal carcinoma is one of the most common malignant tumors, with high incidence and mortality rates. Prognosis of RCC is poor, with 5-year survival rates less than 5%. PTL, a naturally occurring sesquiterpene lactone isolated from Tanacetum parthenium, can inhibit tumor cell growth and induce tumor cell apoptosis. Methods: MTT and apoptosis assays were performed to evaluate the function of PTL on human 786-O kidney cancer cells. Moreover, Western blotting analysis was performed to detect expression levels of cell apoptosis-related proteins. Results: PTL inhibits cell proliferation and induces apoptosis in human 786-O kidney cancer cells. Western blot analysis indicated a downregulation of phosphorylated survival, in a dose-dependent manner, in 786-O cells with PTL treatment. Regarding gene and protein levels, PTL treatment of 786-O cell-lines resulted in a significant reduction in Bcl-2 expression, but an increase in Bax protein, in a dose-dependent manner. Conclusion: PTL may display powerful activities against kidney cancer. Thus, it should be considered a potential candidate facilitating anticancer treatment.
Remote Telemanipulation with Adapting Viewpoints in Visually Complex Environments

—In this paper, we introduce a novel method to support remote telemanipulation tasks in complex environments by providing operators with an enhanced view of the task environment. Our method features a novel viewpoint adjustment algorithm designed to automatically mitigate occlusions caused by workspace geometry, supports visual exploration to provide operators with situation awareness in the remote environment, and mediates context-speciﬁc visual challenges by making view-point adjustments based on sparse input from the user. Our method builds on the dynamic camera telemanipulation viewing paradigm, where a user controls a manipulation robot, and a camera-in-hand robot alongside the manipulation robot servos to provide a sufﬁcient view of the remote environment. We discuss the real-time motion optimization formulation used to arbitrate the various objectives in our shared-control-based method, particularly highlighting how our occlusion avoidance and viewpoint adaptation approaches ﬁt within this framework. We present results from an empirical evaluation of our proposed occlusion avoidance approach as well as a user study that compares our telemanipulation shared-control method against alternative telemanipulation approaches. We discuss the implications of our work for future shared-control research and robotics applications.
Redistribution, Risk Premia, and the Macroeconomy∗

We study the effects of redistribution on risk premia and investment in a heterogeneous agent New Keynesian environment. Heterogeneity in agents’ marginal propensity to take risk (MPR) summarizes differences in risk aversion, constraints, rules of thumb, and background risk relevant for portfolio choice on the margin. Shocks which redistribute to agents with high MPRs reduce risk premia and, absent a monetary policy tightening, raise investment. We quantitatively evaluate the role of this mechanism for the transmission of conventional monetary policy in the U.S. economy. An unexpected reduction in the nominal interest rate redistributes to agents with high MPRs and lowers the risk premium. This rationalizes the relative roles of dividend growth, risk-free rates, and excess returns in generating an increase in the stock market and contributes substantially to the transmission of monetary policy through investment. JEL codes: E44, E63, G12 ∗We thank Adrien Auclert, Marcus Brunnermeier, George Constantinides, Emmanuel Farhi, Zhiguo He, Anil Kashyap, Gita Gopinath, Veronica Guerrieri, Nobu Kiyotaki, Ben Moll, Stefan Nagel, Brent Neiman, Richard Rogerson, Martin Schneider, Dejanir Silva, Chris Sims, Ludwig Straub, Gianluca Violante, Ivan Werning, Tom Winberry, and Moto Yogo for discussions. †University of Chicago Booth School of Business. Email: rohan.kekre@chicagobooth.edu. ‡Princeton University. Email: mlenel@princeton.edu.