nils-herrmann/clean_scirepeval huggingface dataset:
MicroNet: Realizing Micro Neural Network via Binarizing GhostNet
Venue: 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)
Year: 2021

Binary Neural Networks (BNNs) are regarded as very effective approaches to reduce the high computational and memory cost of deep convolutional neural networks; however, most of the previous works only focus on the binarization of traditional large scale networks. In this paper, to further decrease the computational and memory complexity, we propose to binarize the lightweight network: GhostNet, which can generate feature maps with fewer operations. We first modify the ghost block for reducing the information loss caused by the binarization. Moreover, to enhance the performance of binarized GhostNet, some training schemes are introduced including gradient approximation and new activation layers. Various experiments on CIFAR10 dataset show that, compared with other methods, the MicroNet can achieve better performance with fewer computational and memory cost.
Association of Patient Centered Medical Home designation and quality indicators within HRSA funded community health center delivery sites
Venue: BMC Health Services Research
Year: 2020

Patient Centered Medical Home (PCMH) adoption is an important strategy to help improve primary care quality within Health Resources and Service Administration (HRSA) community health centers (CHC) but evidence of its effect thus far remains mixed. A limitation of previous evaluations has been the inability to account for the proportion of CHC delivery sites that are designated medical homes.  Retrospective cross sectional study using HRSA Uniform Data System (UDS) and certification files from the National Committee for Quality Assurance (NCQA) and the Joint Commission (JC) Datasets were linked through geocoding and an approximate string matching algorithm. Predicted probability scores were regressed onto 11 clinical performance measures using 10% increments in site level designation using beta logistic regression.  The geocoding and approximate string matching algorithm identified 2615 of the 6851 (41.8% delivery sites included in the analyses as having been designated through the NCQA and/or JC. In total, 74.7% n 777) of the 1039 CHCs that met the inclusion criteria for the analysis managed at least one NCQA and/or JC designated site. A proportional increase in site level designation showed a positive association with adherence scores for the majority of all indicators, but primarily among CHCs that designated at least 50% of its delivery sites. Once this threshold was achieved, there was a stepwise percentage point increase in adherence scores, ranging from 1.9 to 11.8% improvement, depending on the measure.  Geocoding and approximate string matching techniques offer a more reliable and nuanced approach for monitoring the association between site level PCMH designation and clinical performance within HRSA's CHC delivery sites. Our findings suggest that transformation does in fact matter, but that it may not appear until half of the delivery sites become designated. There also appears to be a continued stepwise increase in adherence scores once this threshold is achieved.
Performance of opportunistic routing in low duty cycle wireless sensor networks
Venue: 2012 IFIP Wireless Days
Year: 2012

In this paper, we propose three variants of WSN routing protocol: i) basic opportunistic, ii) opportunistic with delay and iii) opportunistic with backtracking. The main idea is that a node transmits its packet to the next awake node as long as this decreases the remaining distance to the sink. The two last variants allow a packet to be discarded or moved further away from the sink if necessary. The performance evaluation is carried out by simulations and with analytical model based on a Poisson rain model. The comparison is based i) probability of packet delivery, ii) end to end packet delay and iii) number of hops between the source and the sink node.
Climate Change, Inequality, and Human Migration
Year: 2019

This paper investigates the long term implications of climate change on local, interregional, and international migration of workers. For nearly all of the world's countries, our micro founded model jointly endogenizes the effects of changing temperature and sea level on income distribution and individual decisions about fertility, education, and mobility. Climate change intensifies poverty and income inequality creating favorable conditions for urbanization and migration from low to high latitude countries. Encompassing slow and fast onset mechanisms, our projections suggest that climate change will induce the voluntary and forced displacement of 100 to 160 million workers (200 to 300 million climate migrants of all ages) over the course of the 21st century. However, under current migration laws and policies, forcibly displaced people predominantly relocate within their country and merely 20% of climate migrants opt for long haul migration to OECD countries. If climate change induces generalized and persistent conflicts over resources in regions at risk, we project significantly larger cross border flows in the future.
Functional Magnetic Resonance Imaging and Functional Near Infrared Spectroscopy: Insights from Combined Recording Studies
Venue: Front. Hum. Neurosci.
Year: 2017

Although blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a widely available, non invasive technique that offers excellent spatial resolution, it remains limited by practical constraints imposed by the scanner environment. More recently, functional near infrared spectroscopy (fNIRS) has emerged as an alternative hemodynamic based approach that possesses a number of strengths where fMRI is limited, most notably in portability and higher tolerance for motion. To date, fNIRS has shown promise in its ability to shed light on the functioning of the human brain in populations and contexts previously inaccessible to fMRI. Notable contributions include infant neuroimaging studies and studies examining full body behaviors, such as exercise. However, much like fMRI, fNIRS has technical constraints that have limited its application to clinical settings, including a lower spatial resolution and limited depth of recording. Thus, by combining fMRI and fNIRS in such a way that the two methods complement each other, a multimodal imaging approach may allow for more complex research paradigms than is feasible with either technique alone. In light of these issues, the purpose of the current review is to: (1) provide an overview of fMRI and fNIRS and their associated strengths and limitations; (2) review existing combined fMRI fNIRS recording studies; and (3) discuss how their combined use in future research practices may aid in advancing modern investigations of human brain function.
R2MRF: Defocus Blur Detection via Recurrently Refining Multi Scale Residual Features
Venue: AAAI
Year: 2020

Defocus blur detection aims to separate the in focus and out of focus regions in an image. Although attracting more and more attention due to its remarkable potential applications, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. In order to address these issues, we propose a deep neural network which Recurrently Refines Multi scale Residual Features (R2MRF) for defocus blur detection. We firstly extract multi scale deep features by utilizing a fully convolutional network. For each layer, we design a novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) to more accurately detect blur regions from the input image. Considering that the features from bottom layers are able to capture rich low level features for details preservation while the features from top layers are capable of characterizing the semantic information for locating blur regions, we aggregate the deep features from different layers to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch. Since the defocus degree is sensitive to image scales, we finally fuse the side output of each branch to obtain the final blur detection map. We evaluate the proposed network on two commonly used defocus blur detection benchmark datasets by comparing it with other 11 state of the art methods. Extensive experimental results with ablation studies demonstrate that R2MRF consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.
Analisis Laporan Keuangan dengan Rasio Arus Kas dan Common Size untuk Menilai Kinerja Perusahaan PT Aneka Tbk Periode tahun 2013 2018
Year: 2019

PT Aneka Tambang Tbk merupakan perusahaan pertambangan yang terdiversifikasi dan terintegrasi secara vertikal yang berorientasi ekspor. Tujuan dari penelitian ini adalah untuk mengetahui kinerja keuangan PT Aneka Tambang Tbk dari tahun 2013 2018 dengan menggunakan metode rasio arus kas dan metode common size. Secara keseluruhan dari hasil analisa laporan keuangan berdasarkan rasio arus kas bahwa kinerja keuangan arus kas PT Aneka Tamang Tbk pada tahun 2013 2018 kurang baik karena perusahaan tidak mampu membayar kewajiban lancarnya walaupun terjadi peningkatan rasio setiap tahunnya dalam kurun waktu 6 tahun berturut turut yakni tahun 2013 hingga tahun 2018 nilai rasio yang dihasilkan kurang dari 1 sehingga dapat disimpulkan perusahaan tidak mampu membayar kewajiban lancarnya dengan hanya mengandalkan arus kas operasi saja melainkan dapat melalui aktivitas investasi seperti hasil pinjaman investasi. Hal ini memungkinkan perusahaan akan sulit memenuhi pembiayaan aktivitas perusahaan yang mana produktivitas tidak akan meningkat secara otomatis. Sedangkan dengan analisa persentase common size yakni pada asset lancar dan asset tidak lancar menunjukkan likuiditas perusahaan masih dapat disimpulkan baik, hasil analisis laba/rugi perusahaan tahun 2014 menurun karena meningkatnya jumlah pengeluaran modal untuk proses pemurnian, namun tahun 2016 hingga tahun 2018 perusahaan kembali membaik dengan meningkatnya jumlah laba.  Rasio Arus Kas, Persentase Common Size, KInerja Keuangan Parusahaan.
Enhanced thermoelectric figure of merit in Bi containing Sb2Te3 bulk crystalline alloys
Year: 2020

In the present work, the structural and thermoelectrical properties of Sb2 xBixTe3 samples prepared by the usual melting method were studied. The samples were milled, pressed and then annealed under vacuum for 3 h at 250 degC. Investigations of the microstructure and surface morphology were carried out using X ray diffraction analysis, scanning electron microscopy and energy dispersive X ray spectroscopy. The Seebeck coefficient and electrical and thermal conductivities were measured from 300 to 473 K. Thermopower data indicated a p type conduction in the samples. Structural defects were present in Sb2Te3 during Bi addition, acting as scattering centers and leading to a decrease in thermal conductivity, thus increasing the thermopower and power factor. The power factor showed its maximum value of around 24.7 mW/cm K2 for the sample containing the highest Bi content. High figure of merit of 1.14 was recorded for the same sample, indicating that the Bi containing Sb2Te3 and the related alloys are promising materials for more efficient thermoelectric devices.
The Multisensory Museum: Cross Disciplinary Perspectives on Touch, Sound, Smell, Memory, and Space
Year: 2017

Nina Levent Alvaro Pascual Leone Part I: Museums Touch Chapter 1: Please DO Touch the Exhibits! Interactions between Visual Imagery and Haptic Perception Simon Lacey K. Sathian Chapter 2: "First Hand," not "First eye" Knowledge: Bodily Experience in Museums Francesca Bacci Francesco Pavani Chapter 3: Art Making as Multisensory Engagement: Case Studies from The Museum of Modern Art Carrie McGee &Francesca Rosenberg Chapter 4: Multi sensory Engagement with Real Nature Relevant to Real Life Molly Steinwald, Melissa A. Harding, Richard V. Piacentini Chapter 5: Touch and Narrative in Art and History Museums Nina Levent Lynn McRainey Part II: Museums Sound Chapter 6: A Brain Guide to Sound Galleries Stephen R. Arnott Claude Alain Chapter 7: Ephemeral, Immersive,  Sound as Curatorial Theme 1966 2013  Soundwalking the Museum: A Sonic Journey through the Visual Display Salome Voegelin.  The Role of Sensory and Motor Systems in Art Appreciation and Implications for Exhibit Design A. Casile L. F. Ticini. Part III: Smell Taste in Museums Chapter 10: The Forgotten Sense: Using Olfaction in a Museum Context. A Neuroscience Perspective. Richard Stevenson.  The Scented Museum Andreas Keller Chapter 12: The Museum as Smellscape Jim Drobnick Chapter 13: Taste full Museums: Educating the Senses One Plate at a Time Irina Mihalache Part IV: Museum Architecture the Senses Chapter 14: Navigating the Museum Hugo Spiers, Fiona Zisch Steven Gage, Chapter 15: Museum as an Embodied Experience Juhani Pallasmaa Chapter 16: Architectural Design for Living Artifacts Joy Monice Malnar Frank Vodvarka.  Future Museums Chapter 17:  The Secret of Aesthetcis Lies in the Conjugation of the Senses: Reimagining the Museum as a Sensory Gymnasium David Howes Chapter 19: Multisensory Mental Simulation and Aesthetic Perception Salvatore M Aglioti, Ilaria Bufalari Matteo Candidi Chapter 20: Islands of Stimulation: Perspectives on the Museum Experience, Present and Future Rebecca McGinnis Chapter 21: The Future Landscape of 3D in Museums Samantha Sportun Chapter 22: Technology, Senses and the Future of Museums. A Conversation with Nina Levent, Heather Knight Sebastian Chan and Rafael Lozano Hammer.  Index About the Contributors
Fairness Aware Machine Learning: Practical Challenges and Lessons Learned
Venue: WWW
Year: 2019

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision making systems. This tutorial aims to present an overview of algorithmic bias discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness first" approach (as opposed to viewing algorithmic bias fairness considerations as an afterthought) when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness aware machine learning techniques in practice, by highlighting industry best practices and case studies from different technology companies. Based on our experiences in industry, we will identify open problems and research challenges for the data mining machine learning community.
Pembrolizumab plus chemotherapy versus chemotherapy alone in patients with advanced non small cell lung cancer without tumor PD L1 expression: A pooled analysis of 3 randomized controlled trials
Venue: Cancer
Year: 2020

Pembrolizumab plus platinum based chemotherapy has demonstrated improved clinical outcomes over chemotherapy alone in patients with previously untreated advanced/metastatic non small cell lung cancer (NSCLC) regardless of tumor programmed death ligand 1 (PD L1) expression. This study pooled data from 3 randomized controlled trials to evaluate outcomes with pembrolizumab plus chemotherapy versus chemotherapy alone in patients with advanced/metastatic NSCLC negative for PD L1 (ie, a tumor proportion score 1%
Environmental reporting: transparency to stakeholders or stakeholders' manipulation? An analysis of disclosure tone and the role of board of directors
Year: 2015

We study whether environmental reporting serves as a transparency tool to communicate sound environmental policies to stakeholders or rather as a manipulation tool of stakeholders' perceptions. In particular, we focus on the relationship between environmental disclosure tone and future environmental performance and we furthermore explore the role of the board of directors' monitoring and stakeholder orientation in shaping this relationship. Using a sample of 288 US oil and gas firms, we find that the bias towards positive language does not reflect purely opportunistic managerial reasons, but rather is a transparency tool to signal future environmental performance. In addition, we document that the stakeholder orientation of the board plays a transparency role in communicating the firm's superior performance. Our findings contribute to the debate on whether discretionary strategies in environmental reporting are more about increased transparency or about stakeholder manipulation. Moreover, they help investors and policymakers to interpret managers' language choices.
Business environment and innovation persistence: the case of small and medium sized enterprises in Vietnam
Year: 2019

While it is well known that efforts have been increasingly paid to improve the business environment in transition economies to support the private sector, it is unclear how these efforts affect firm innovation. Utilising longitudinal data derived from bi annually repeated surveys of randomly selected small and medium sized enterprises (SMEs) in ten provinces in Vietnam over the period of 2007 2015 combined with yearly collected panel data of Provincial Competitiveness Index (PCI) which is used to measure the quality of provincial business environment, this study employs a dynamic random probit estimation to analyse the effects of business environment on the persistence of innovation of the SMEs. It is found that the persistence of moderately complex innovation including both single dimensional and multidimensional innovation exists in weak business environment, while it disappears in improved business environment. These findings suggest that policies to improve business environment enhance innovation policies and, thus, promote innovation of the SMEs that are inexperienced in carrying out such innovation.
Production of Biodiesel from Algae Oils
Year: 2008

A macroalga (Cladophora fracta) and a microalga (Chlorella protothecoides) samples were used in this work. Most current research on oil extraction is focused on microalgae to produce biodiesel from algal oil. The biodiesel from algal oil in itself is not significantly different from biodiesel produced from vegetable oils. Algal oils, as well as vegetable oils, are all highly viscous, with viscosities ranging 10 20 times those of no. 2 diesel fuel. Transesterification of the oil to its corresponding fatty ester is the most promising solution to the high viscosity problem. Fatty acid (m)ethyl esters produced from natural oils and fats is called biodiesel. Generally, methanol has been mostly used to produce biodiesel as it is the least expensive alcohol. The oil proportion from the lipid fractions of Chlorella protothecoides is considerable higher than that of Cladophora fracta. The higher heating value of Chlorella protothecoides (25.1 MJ/kg) also is higher than that of Cladophora fracta (21.1 MJ/kg) The average polyunsaturated fatty acids of Chlorella protothecoides (62.8% also are higher than those of Cladophora fracta (50.9%
Regression of traits other than production phenotypes for cows milked once a day on estimated breeding values obtained from cows milked twice a day
Year: 2019

The use of traits other than production (TOP) to aid selection for conformation fit for purpose offspring becomes more important as once a day (OAD) milking for whole or part season becomes more common. In 2013 Massey University Dairy 1 farm transitioned their mixed breed herd milked twice a day (TAD) to an OAD spring calving system. All cows on the farm were scored for 14 inspector TOP traits and four farmer scored traits for a total of 1,163 records over five seasons (168 to 254 cows per year) The scores were adjusted for age of the cow and season. The adjusted phenotypes for all cows for each trait were then regressed on their corresponding nationally produced TAD estimated breeding values (EBV) which had been calculated without using these phenotypes. The four farmer scored traits had the lowest relationships with EBV. Estimates of regression coefficients near one were observed for the udder traits (udder support, rear udder, front teats, udder overall) dairy conformation and body capacity. This supports the use of national EBVs in OAD herds derived mostly from records on daughters in TAD herds.
X ray diffraction data assisted structure searches
Venue: Comput. Phys. Commun.
Year: 2017

X ray diffraction (XRD) is an important technique for structure determination. However, in traditional methods, estimated structural information (e.g. unit cell parameters and space group) is required to determine the precise structure from XRD data. We propose a versatile global search method for determining crystal structures from experimental powder XRD data without guessed structural information. Unlike in traditional structure prediction, which uses total energy as the fitness function, the fitness in our method is the degree of dissimilarity between the simulated and the experimental XRD patterns. To improve the efficiency of structure searching using powder XRD data, several techniques are implemented, including background subtraction, profile parameter optimization by simulated annealing, and structure evolution using CALYPSO. The efficiency and robustness of our method is demonstrated here using predictions of previous known structures (hexagonal ZnO and anatase TiO 2 The method is also used to explore the high pressure phases of binary compounds CaLi 2 and Ca 3 C 2 it uncovers candidate structures C 222 1 for CaLi 2 at 54 GPa and C m 2 m for Ca 3 C 2 at 12.5 GPa) whose simulated XRD patterns agree with their experimental patterns.
Contrasting Clients in Dialectical Behavior Therapy for Borderline Personality Disorder: "Marie" and "Dean," Two Cases with Different Alliance Trajectories Outcomes
Year: 2011

Dialectical Behavior Therapy (DBT; Linehan, 1993a) has garnered a strong evidence base to support its efficacy in treating borderline personality disorder (BPD) Despite this, some clients do not benefit from evidenced based approaches. There is a recent emphasis on identifying the processes and mechanisms of DBT in order to improve treatment outcomes. This report describes the course of treatment for two individuals who were treated with one year of standard, outpatient DBT in the context of a randomized control trial. The two clients were selected because (1) both reported poor initial alliances, and (2) they had different outcomes. The first case, "Marie," showed considerable change across a broad range of outcomes whereas the second case, "Dean," made only limited treatment gains. The two cases are contrasted in order to highlight potential factors that may have contributed to the different alliance trajectories and outcomes. We explore several hypotheses to help to explain the relationship between treatment outcome and client characteristics, the therapeutic alliance, the consultation team, and the research context.