nils-herrmann/clean_scirepeval huggingface dataset:
Variational Autoencoder Coupled with Deep Generative Neural Network for the Identification of Handwritten Digits
Year: 2018

In the present paper, a novel approach of machine learning technique has been implemented that will help the machine to learn from the experience and understand hand written digits using Deep Generative Neural Network (DGNN) The current work describes a process to generate unique images and recognize the hand written digits sent as input image to the proposed model by using unsupervised learning. In the proposed method, a variational auto encoder (VAE) has been utilized to encode and decode the input image. Inside an auto encoder, there consists of several hidden layers 'h' which represents the input. Then clustering is undertaken and the result of clustering reflects. loss of information, so an Convolutional Neural network (CNN) lossy function is adopted to retrieve the lost information and finally DGNN is employed to predict the output and a digit generator is utilized to produce images of digits(0 9) as result of the model. Clustering of digits has been performed to show the similarities and dissimilarities among the different digits contained in the data set. Manually handwritten digits can be identified very easily but it is tough and challenging for machines to understand because handwriting differs from person to person. Although it is difficult to achieve, but it has been observed that DGNN produces acceptable results and performs identification of handwritten digits (taken input as no label data) in a better manner.
A continental scale analysis of feral cat diet in Australia
Year: 2015

Reducing the impacts of feral cats (Felis catus) is a priority for conservation managers across the globe, and success in achieving this aim requires a detailed understanding of the species' ecology across a broad spectrum of climatic and environmental conditions. We reviewed the diet of the feral cat across Australia and on Australian territorial islands, seeking to identify biogeographical patterns in dietary composition and diversity, and use the results to consider how feral cats may best be managed. Location Australia and its territorial islands.  Using 49 published and unpublished data sets, we modelled trophic diversity and the consumption of eight food groups against latitude, longitude, mean temperature, precipitation, environmental productivity and climate habitat regions.  We recorded 400 vertebrate species that feral cats feed on or kill in Australia, including 28 IUCN Red List species. We found evidence of continental scale prey switching from rabbits to small mammals, previously recorded only at the local scale. The consumption of arthropods, reptiles, rabbits, rodents and medium sized native mammals varied with different combinations of latitude, longitude, mean annual precipitation, temperature and environmental productivity. The frequency of rodents and dasyurids in cats' diets increased as rabbit consumption decreased.  The feral cat is an opportunistic, generalist carnivore that consumes a diverse suite of vertebrate prey across Australia. It uses a facultative feeding strategy, feeding mainly on rabbits when they are available, but switching to other food groups when they are not. Control programmes aimed at culling rabbits could potentially decrease the availability of a preferred food source for cats and then lead to greater predation pressure on native mammals. The interplay between cat diet and prey species diversity at a continental scale is complex, and thus cat management is likely to be necessary and most effective at the local landscape level.
Growing the practice of biomimicry: opportunities for mission based organisations based on a global survey of practitioners
Venue: Technol. Anal. Strateg. Manag.
Year: 2020

Biomimicry is a growing field within design communities, attracting practitioners and researchers from a wide range of domains. In the USA, The Biomimicry Institute, The Biomimicry Center and Biomimicry 3.8 share a common mission of supporting the growth of the practice of biomimicry, through commercial consulting, professional training and public education. Research to date has highlighted potential weaknesses and challenges to its growth as a practice. By gaining insights into the current state and funding of completed and on going biomimicry projects we aim to provide more intelligence and strategic information for organisations with the mission of supporting the practice of biomimicry. We present the results of an online survey of 270 biomimicry practitioners from 44 countries and describe distribution trends, the level of practice support, and difficulties they are currently experiencing. We conclude with guiding recommendations for supporting the impact and growth of the practice of biomimicry.
A Model Stitching Architecture for Continuous Full Flight Envelope Simulation of Fixed Wing Aircraft and Rotorcraft from Discrete Point Linear Models
Year: 2016

A comprehensive model stitching simulation architecture has been developed, which allows continuous, full flight envelope simulation based on a collection of discrete point linear models and trim data. The model stitching simulation architecture is applicable to any aircraft configuration readily modeled by state equations and for which test data can be obtained. Individual linear models and trim data for specific flight conditions are incorporated with nonlinear elements to produce a continuous, quasi nonlinear simulation model. Extrapolation methods within the model stitching architecture permit accurate simulation of off nominal aircraft loading configurations, including variations in weight, inertia, and center of gravity, and variations in altitude, which together minimize the required number of point models for full envelope simulation. The model stitching simulation architecture is applied herein to a model of a CJ1 business jet and to a model of a UH 60 utility helicopter. For both the fixed wing and the rotorcraft application, configuring the stitched simulation models with 8 discrete point linear models (4 point models each at two altitudes) plus additional trim data was found to allow accurate simulation over the full airspeed and altitude envelope. Flight test implications for the development of stitched models from flight identified point models are presented for fixed wing and rotorcraft applications.
The effectiveness of VIPP V parenting training for parents of young children with a visual or visual and intellectual disability: study protocol of a multicenter randomized controlled trial
Venue: Trials
Year: 2015

BackgroundVisual or visual and intellectual disabilities of children make daily interactions more difficult for their parents and may impact the quality of the parent child relationship. To support these parents, an existing intervention (Video feedback Intervention to promote Positive Parenting; VIPP; Juffer F, Bakermans Kranenburg MJ, van IJzendoorn MH, 2008. Promoting positive parenting; an attachment based intervention. Mahwah,  Lawrence Erlbaum Associates; 2008) was adapted for use with parents of children with a visual or visual and intellectual disability (VIPP V) This attachment based intervention was hypothesized to support parents' interpretation and understanding of the behavior of their child with a visual or visual and intellectual disability and respond to their child's signals in a sensitive way to improve parent child interaction quality. randomized controlled trial (RCT) will be conducted to assess the effectiveness of the adapted intervention VIPP V (Video feedback Intervention to promote Positive Parenting in parents of children with Visual or visual and intellectual disabilities) Parent child dyads will be randomized into two groups: 50 dyads will receive VIPP V in combination with care as usual and 50 dyads will receive care as usual. Families with a child (1 5 years of age) with a visual or visual and intellectual disability will be recruited for participation in the study. Primary outcome measures are parental sensitivity and the quality of parent child interaction. Secondary outcome measures are parental self efficacy, and parenting stress. To assess feasibility of implementation of the intervention the experiences of early intervention workers with regard to using VIPP V are assessed. Moderator variables are the child's developmental age, working alliance between parent and VIPP V intervention worker and empathy of the VIPP V intervention worker. Data will be collected approximately one week before the intervention starts (T1) one week (T2) and three months (T3) after the intervention. Parent child dyads in the care as usual only condition will be assessed at the same time points. Both intention to treat and completer analyses will be performed.DiscussionDescriptive findings in pilot cases suggest benefits from VIPP V, and compatibility with existing services for parents of children with a visual or visual and intellectual disability. The current study will provide insight into the effectiveness of this intervention for parents of children with a visual or visual and intellectual disability, and, if the intervention is effective, prepare the field for broad scale implementation.
Joint Activity Recognition and Indoor Localization With WiFi Fingerprints
Venue: IEEE Access
Year: 2019

Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. Past work falls into two major categories, i.e. activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize human daily activities such as smoking, walking, and dancing. The latter one, indoor localization, can be used for indoor navigation, location based services, and through wall surveillance. The key rationale behind WiFi sensing is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations. In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi channel state information (CSI) fingerprints. More precisely, we develop a system running standard IEEE 802.11n WiFi protocol and collect more than 1400 CSI fingerprints on 6 activities at 16 indoor locations. Then we propose a dual task convolutional neural network with one dimensional convolutional layers for the joint task of activity recognition and indoor localization. The experimental results and ablation study show that our approach achieves good performances in this joint WiFi sensing task. Data and code have been made publicly available at https:/github.com/geekfeiw/apl.
Characterization and Automatic Update of Deprecated Machine Learning API Usages
Venue: ArXiv
Year: 2020

Due to the rise of AI applications, machine learning libraries have become far more accessible, with Python being the most common programming language to write them. Machine learning libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for developers to update their usages. However, updating usages of deprecated APIs are typically not a priority for developers, leading to widespread usages of deprecated APIs which expose library users to vulnerability issues. In this paper, we built a tool to automate these updates. We first conducted an empirical study to seek a better understanding on how updates of deprecated machine learning API usages in Python can be done. The study involved a dataset of 112 deprecated APIs from Scikit Learn, TensorFlow, and PyTorch. We found dimensions of deprecated API migration related to its update operation (i.e. the required operation to perform the migration) API mapping (i.e. the number of deprecated and its corresponding updated APIs),and context dependency (i.e. whether we need to consider surrounding contexts when performing the migration) Guided by the findings on our empirical study, we created MLCatchUp, a tool to automate the update of Python deprecated API usage that automatically infers the API migration transformation through comparison of the deprecated and updated API signatures. These transformations are expressed in a Domain Specific Language (DSL) We evaluated MLCatchUp using test dataset containing 258 files with 514 API usages that we collected from public GitHub repositories. In this evaluation, MLCatchUp achieves a precision of 86.19% We further improve the precision of MLCatchUp by adding a feature that allows it to accept additional user input to specify the transformation constraints in the DSL for context dependent API migration, where MLCatchUp achieves a precision of 93.58%
Machine Learning Based Congestion Estimation for Modern FPGAs
Venue: 2018 28th International Conference on Field Programmable Logic and Applications (FPL)
Year: 2018

Avoiding congestion for routing resources has become one of the most important placement objectives. In this paper, we present a machine learning model for accurately and efficiently estimating congestion during FPGA placement. Compared with the state of the art machine learning congestion estimation model, our results show a 25% improvement in prediction accuracy. This makes our model competitive with congestion estimates produced using a global router. However, our model runs, on average, 291x faster than the global router.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
Venue: Neurocomputing
Year: 2018

Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors, which have irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co centric patches of different sizes from the input images. Recent developments in deep neural networks such as dropout, batch normalization, non linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over fitting problem arising due to scarcity of data using dropout regularizer. Images are normalized and bias field corrected in the pre processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post processing to remove small false positives around the edges. A two phase weighted training method is introduced and evaluated using  2015 datasets, where it improves the performance parameters of state of the art techniques under similar settings.
Dynamic causal modeling of the effective connectivity between the cerebrum and cerebellum in social mentalizing across five studies
Venue: Cognitive, affective behavioral neuroscience
Year: 2019

In this analysis we explored the effective connectivity of the cerebellum with the cerebrum in social mentalizing, across five studies (n 91) involving abstract and complex forms of mentalizing, such as (a) person and group impression formation, based on behavioral descriptions, and (b) constructing personal counterfactual events. Connectivity was analyzed by applying dynamic causal model analysis, which revealed effective connectivity between the mentalizing areas of the cerebellum and cerebrum. The results revealed a significant pattern of bidirectional (closed loop) connectivity linking the right posterior cerebellum with bilateral temporo parietal junction (TPJ) associated with behavior understanding. These connections are consistent with known anatomical data on closed loops between the cerebellum and cerebrum, although contralateral closed loops typically dominate. This analysis improves on an earlier psychophysiological interaction analysis of this dataset, which had failed to reveal such evidence of closed loops. Within the cerebrum, there were connections between the bilateral areas of TPJ, as well as connections between bilateral TPJ and the (ventral and dorsal) medial prefrontal cortex. The discussion centers on the function of cerebro cerebellar connections in generating internal cerebellar "forward" models, potentially serving the automatic understanding, prediction, and error correction of behavioral sequences.
High brightness diode pump sources for solid state and fiber laser pumping across 8xx 9xx nm range
Venue: Defense Commercial Sensing
Year: 2011

Advanced solid state laser architectures place increasingly demanding requirements on high brightness, low cost QCW laser diode pump sources, with custom apertures both for side and end rod pumping configurations. To meet this need, a new series of scalable QCW pump sources at 808nm and 940nm was developed. The stacks, available in multiple output formats, allow for custom aperture filling by varying both the length and quantity of stacked laser bars. For these products, we developed next generation laser bars based on improved epitaxial wafer designs delivering power densities of 20W/mm of emission aperture. With >200W of peak QCW power available from a full length 1cm bar, we have demonstrated power scaling to over 2kW in 10 bar stacks with 55% wall plug efficiency. We also present the design and performance of several stack configurations using full length and reduced length (mini) bars that demonstrate the versatility of both the bar and packaging designs. We illustrate how the ROBUST HEAD packaging technology developed at SCD is capable of accommodating variable bar length, pitch and quantity for custom rod pumping geometries. The excellent all around performance of the stacks is supported by reliability data in line with the previously reported 20 Gshot space grade qualification of SCD's stacks.
An effector from the Huanglongbing associated pathogen targets citrus proteases
Venue: Nature Communications
Year: 2018

The citrus industry is facing an unprecedented challenge from Huanglongbing (HLB) All cultivars can be affected by the HLB associated bacterium 'Candidatus Liberibacter asiaticus' (CLas) and there is no known resistance. Insight into HLB pathogenesis is urgently needed in order to develop effective management strategies. Here, we use Sec delivered effector 1 (SDE1) which is conserved in all CLas isolates, as a molecular probe to understand CLas virulence. We show that SDE1 directly interacts with citrus papain like cysteine proteases (PLCPs) and inhibits protease activity. PLCPs are defense inducible and exhibit increased protein accumulation in CLas infected trees, suggesting a role in citrus defense responses. We analyzed PLCP activity in field samples, revealing specific members that increase in abundance but remain unchanged in activity during infection. SDE1 expressing transgenic citrus also exhibit reduced PLCP activity. These data demonstrate that SDE1 inhibits citrus PLCPs, which are immune related proteases that enhance defense responses in plants.Greening disease threatens the productivity of citrus crops worldwide yet the pathosystem is poorly understood. Here, Clark et al. show that an effector cloned from the associated bacteria can suppress host plant papain like cysteine proteases' activity, suggesting its probable role in pathogenesis.
Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.
Venue: Journal of stroke and cerebrovascular diseases the official journal of National Stroke Association
Year: 2019

The manual adjudication of disease classification is time consuming, error prone, and limits scaling to large datasets. In ischemic stroke (IS) subtype classification is critical for management and outcome prediction. This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping.  Among IS patients from an observational registry with TOAST subtyping adjudicated by board certified vascular neurologists, we analyzed unstructured text based EHR data including neurology progress notes and neuroradiology reports using natural language processing. We performed several feature selection methods to reduce the high dimensionality of the features and 5 fold cross validation to test generalizability of our methods and minimize overfitting. We used several machine learning methods and calculated the kappa values for agreement between each machine learning approach to manual adjudication. We then performed a blinded testing of the best algorithm against a held out subset of 50 cases.  Compared to manual classification, the best machine based classification achieved a kappa of .25 using radiology reports alone, .57 using progress notes alone, and .57 using combined data. Kappa values varied by subtype being highest for cardioembolic .64) and lowest for cryptogenic cases .47) In the held out test subset, machine based classification agreed with rater classification in 40 of 50 cases (kappa .72)  Automated machine learning approaches using textual data from the EHR shows agreement with manual TOAST classification. The automated pipeline, if externally validated, could enable large scale stroke epidemiology research.