nils-herrmann/clean_scirepeval huggingface dataset:
UJI ANTIMIKROBA MINYAK ATSIRI MASOYI (Massoia aromatica) TERHADAP BAKTERI Streptococcus mutans
Year: 2019

Karang gigi merupakan salah satu keadaan patologis dari gigi. Salah satu penyebab karang gigi adalah bakteri Streptococcus mutans. Bakteri tersebut tidak bekerja sendiri tetapi didukung oleh beberapa faktor lainnya yaitu host (gigi dan saliva) substrat (makanan) dan waktu. Salah satu cara yang dapat dilakukan dalam pencegahan tumbuhnya karang gigi adalah dengan mengontrol akumulasi plak pada permukaan gigi. Tumbuhan masoyi merupakan salah satu tumbuhan yang tumbuh di Indonesia. Dari kajian teoritis yang ada, minyak atsiri masoyi (Massoia aromatica) mengandung senyawa yang mampu menghambat pertumbuhan bakteri. Penelitian ini bertujuan untuk mengetahui efektifitas minyak atsiri masoyi (Massoia aromatica) sebagai antibakteri terhadap Streptococcus mutans dengan melihat nilai KBM (Kadar Bunuh Minimum) dan KHM (Kadar Hambat Minimum) Penentuan nilai KBM dan KHM dilakukan metode kualitatif dan kuantitatif. Pada pengujian dengan metode kualitatif dilakukan disc diffusion test, metode dilusi cair, dan metode dilusi padat. Sedangkan pada pengujian kuantitatif dilakukan metode mikrodilusi. Penelitian ini menggunakan 8 konsentrasi yaitu 2000, 1000, 500, 250, 125, 62,5, 31,25, dan 15,625 ug/mL. Hasil penelitian dengan metode kualitatif menunjukkan nilai KHM didapatkan pada konsentrasi 500 ug/mL dan nilai KBM didapatkan pada konsentrasi lebih dari 2000 ug/mL. Hasil penelitian dengan metode kuantitatif menunjukkan nilai IC50 85,047 ug/mL dan IC90 491,481 ug/mL. Kesimpulan dari penelitian ini adalah minyak atsiri masoyi (Massoia aromatica) terbukti dapat menghambat pertumbuhan bakteri Streptococcus mutans.
Imbalanced phosphorus and nitrogen deposition in China's forests
Year: 2016

Acceleration of anthropogenic emissions in China has substantially increased nitrogen (N) deposition during the last 3 decades and may result in an imbalance of atmospheric N and phosphorus (P) inputs in terrestrial ecosystems. However, the status of P deposition in China is poorly understood. This study synthesized data on total P and total N concentrations in bulk precipitation and throughfall from published literature to assess the characteristics of P deposition, N deposition and N P deposition ratio in China's forests. Our results show relatively high mean rates of bulk P deposition (0.38 kg P ha 1 yr 1) and total P deposition (0.69 kg P ha 1 yr 1) but they were accompanied by even more elevated N inputs via bulk deposition (16.5 kg N ha 1 yr 1) and total deposition (21.6 kg N ha 1 yr 1) resulting in high N P ratios in bulk deposition (44.4) and total deposition (32.8) Based on the difference between total deposition and bulk deposition, canopy captured dry P and N deposition was estimated to be 0.31 kg P ha 1 yr 1 and 5.1 kg N ha 1 yr 1, respectively. We found significantly higher P deposition and lower N P ratios at sites nearby than those far from semiarid regions. The estimated bulk and total deposition of P and N both showed a significant power law increase with decreasing distance to the nearest large cities either in the areas nearby or far from semiarid regions. Our results suggest an anthropogenic alternation of regional P and N cycling, which may shift large areas of China's forests towards human induced P limitation especially in southern China.
Aversive learning shapes neuronal orientation tuning in human visual cortex
Venue: Nature communications
Year: 2015

The responses of sensory cortical neurons are shaped by experience. As a result perceptual biases evolve, selectively facilitating the detection and identification of sensory events that are relevant for adaptive behaviour. Here we examine the involvement of human visual cortex in the formation of learned perceptual biases. We use classical aversive conditioning to associate one out of a series of oriented gratings with a noxious sound stimulus. After as few as two grating sound pairings, visual cortical responses to the sound paired grating show selective amplification. Furthermore, as learning progresses, responses to the orientations with greatest similarity to the sound paired grating are increasingly suppressed, suggesting inhibitory interactions between orientation selective neuronal populations. Changes in cortical connectivity between occipital and fronto temporal regions mirror the changes in visuo cortical response amplitudes. These findings suggest that short term behaviourally driven retuning of human visual cortical neurons involves distal top down projections as well as local inhibitory interactions.
DOCTOR OF PHYSICAL THERAPY
Year: 2015

We are proud of the quality of our faculty and students. We have wellcredentialed faculty many hold doctorates and/or advanced specializations. Many of our courses are team taught by faculty in a case based approach that helps students keep clinical practice in mind. Practice labs typically have 2 or 3 full time faculty supervising and instructing students. Also, students practice hands on skills with faculty in our Pro Bono Clinic. In addition, the faculty work hard to meet each student's individual needs. Students work well together, learning to make clinical decisions and to be part of an exciting profession.
A novel feature extraction method based on discriminative graph regularized autoencoder for fault diagnosis
Year: 2019

Autoencoder has been popularly used as an effective feature extraction method in fault diagnosis. However, the autoencoder algorithms neglect local structure and class information that is available in the training set. To address this problem, a novel feature extraction approach based on discriminative graph regularized autoencoder is proposed for fault diagnosis task. A single layer autoencoder with nonlinear layers is adopted to extract nonlinear features automatically from input signals. Locality relationship of original data is propagated to the feature extraction stage via a graph to learn internal representations that go beyond reconstruction and on to locality preservation. To better exploit the discriminative information, the label information of training samples is embedded to the graph to improve the fault diagnosis performance. A real industrial process are used to comparing the performance with commonly used diagnosis method, the promising experimental results validate the superiority of the proposed method.
Concept of "People as Sensors"
Year: 2019

The goal of this chapter is to provide a brief summary of the related literature regarding "people as sensors" and to propose a conceptual study framework for this book. A variety of studies have tried to use social media to improve situational awareness based on various approaches. Although, as discussed in Chap. 1, there is a possibility of using social media to fill the gap of existing recovery indicators by its timeliness and variety, to the author's best knowledge, there is a paucity of research into how social media could be used to analyze and gauge community recovery. Although detecting incidents and situational awareness during and immediate after a disaster are essential tasks to save lives and reduce further losses, this study argues that there is enormous potential in utilizing social media data to look into affected people's recovery activities to regain their normal routines. This chapter is structured as follows: in Sect. 2.1, the current study's main viewpoint, the "people as sensors" approach in the context of disaster related research, is introduced. In Sect. 2.2, previous work regarding disaster management and social media by focusing on the "people as sensors" approach is provided. Based on the literature review in Sect. 2.2, the author introduces the conceptual research framework of the study in Sect. 2.3.
Can math beat gamers in Quantum Moves
Year: 2018

In a recent work on quantum state preparation, S{\o}rensen and colleagues explore the possibility of using video games to help design quantum control protocols. The authors present a game called "Quantum Moves" in which gamers have to move an atom from A to B by means of optical tweezers. They report that, players succeed where purely numerical optimization fails  Moreover, by harnessing the player strategies they can outperform the most prominent established numerical methods  The aim of this manuscript is to analyze the problem in detail and show that those claims are untenable. In fact a simple stochastic local optimization method can easily find very good solutions to this problem in a few 1000 trials rather than the astronomical $7.4\times 10^{8} trials of the most successful optimization method reported in  Next, counter diabatic driving is used to generate protocols without resorting to numeric optimization; the protocols are shown to outperform virtually all players. The analysis moreover results in an accurate analytic estimate of the quantum speed limit which, apart from zero point motion, is shown to be entirely classical in nature. The latter might explain why gamers are remarkably good at the game.
End to End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving
Year: 2021

The complex decision making systems used for autonomous vehicles or advanced driver assistance systems (ADAS) are being replaced by end to end (e2e) architectures based on deep neural networks (DNN) DNNs can learn complex driving actions from datasets containing thousands of images and data obtained from the vehicle perception system. This work presents the classification, design and implementation of six e2e architectures capable of generating the driving actions of speed and steering wheel angle directly on the vehicle control elements. The work details the design stages and optimization process of the convolutional networks to develop six e2e architectures. In the metric analysis the architectures have been tested with different data sources from the vehicle, such as images, XYZ accelerations and XYZ angular speeds. The best results were obtained with a mixed data e2e architecture that used front images from the vehicle and angular speeds to predict the speed and steering wheel angle with a mean error of 1.06% An exhaustive optimization process of the convolutional blocks has demonstrated that it is possible to design lightweight e2e architectures with high performance more suitable for the final implementation in autonomous driving.
Disentangling shock diffusion on complex networks: identification through graph planarity
Venue: J. Complex Networks
Year: 2020

Large scale networks delineating collective dynamics often exhibit cascading failures across nodes leading to a system wide collapse. Prominent examples of such phenomena would include collapse on financial and economic networks. Intertwined nature of the dynamics of nodes in such network makes it difficult to disentangle the source and destination of a shock that percolates through the network, a property known as reflexivity. In this article, a novel methodology is proposed which combines vector autoregression model with an unique identification restrictions obtained from the topological structure of the network to uniquely characterize cascades. In particular, we show that planarity of the network allows us to statistically estimate a dynamical process consistent with the observed network and thereby uniquely identify a path for shock propagation from any chosen epicenter to all other nodes in the network. We analyze the distress propagation mechanism in closed loops giving rise to a detailed picture of the effect of feedback loops in transmitting shocks. We show usefulness and applications of the algorithm in two networks with dynamics at different time scales: worldwide GDP growth network and stock network. In both cases, we observe that the model predicts the impact of the shocks emanating from the US would be concentrated within the cluster of developed countries and the developing countries show very muted response, which is consistent with empirical observations over the past decade.
State governments as financiers of technology startups: Evidence from Michigan's R&D loan program
Year: 2020

State governments in the United States often fund and support technology startups within their borders. Yet little is known about the magnitude with which these place based policy interventions shift the performance trajectories of entrepreneurial firms. We provide new evidence based on 241 startups that compete for advanced research and technology commercialization loans between 2002 and 2008 through a Michigan based program. Among applicants with project scores near the threshold required for funding, we find that award recipients are 20% 30% more likely to remain in business four years after the competition relative to similar companies that seek but fail to receive funding. We also find that award receipt stimulates follow on venture capital (VC) investments in surviving companies. The VC stimulus effect is, however, disproportionately driven by subsets of firms that are very young, relatively inexperienced at external fundraising, or located outside the dominant hub of entrepreneurial activity within the state. This distinctive pattern of heterogeneous effects remains visible for follow on R&D financing from federal government sources, and for supplemental outcome measures that use news articles to track shifts in financing and business development activities. These findings are consistent with the view that public R&D programs are particularly beneficial when frictions in private resource markets are more severe.
Polymorphisms in Toll like receptor genes and risk of cancer
Venue: Oncogene
Year: 2008

Host genetic factors are emerging as key determinants of disease risk for many cancers. Identifying candidate genes is a major challenge that has to stem from a profound understanding of the pathophysiology of the disease. The Toll like receptors are important members of the host's innate immune response and their genes have been found to be polymorphic. This genetic variation allows for a more intricate repertoire that enables the host to withstand microbial challenges. While this may be advantageous on a population level, there may be less favourable outcomes for individuals that harbour certain genotypes associated with excessive immune activation and inflammatory drive. The damage is often collateral and is manifest in organs where this chronic inflammation alters normal physiology. A classic example of this paradigm is the Helicobacter pylori induced gastric cancer model. Another emerging model is prostate cancer where Toll like receptor polymorphisms have also been found to play a role. In this review, we discuss polymorphisms in Toll like receptors and give an insight into how they may influence risk of cancer.
Proposal of Lateral Force Disturbance Estimation Method for In Wheel Motored Electric Vehicles
Venue: IECON 2018 44th Annual Conference of the IEEE Industrial Electronics Society
Year: 2018

The longitudinal tire forces can be estimated with in wheel motored electric vehicles (IWM EVs) By exploiting this advantage, the authors have realized human friendly EV based on force control but only the longitudinal motion was considered. The aim of this paper is to achieve the next step: to propose a lateral external force estimation method. External force vectors can be estimated if each of tire forces can be detected. Longitudinal tire forces can be easily obtained with a disturbance observer (DOB) One easiest way to get lateral tire force is using lateral force sensors (LFS) In addition, steering DOB based method is also discussed here. The Extended Kalman Filter (EKF) is used to calculate a external force vector from each tire forces. A precise external force vector estimation is achieved in the simulations and the experiments. The results of this paper can be applied not only to disturbance compensation but also to hand operated parking control.
Answer Selection in Community Question Answering via Attentive Neural Networks
Venue: IEEE Signal Processing Letters
Year: 2017

Answer selection in community question answering (cQA) is a challenging task in natural language processing. The difficulty lies in that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. In this letter, we propose an attentive deep neural network architecture so as to learn the deterministic information for answer selection. The architecture can support various input formats through the organization of convolutional neural networks, attention based long short term memory, and conditional random fields. Experiments are carried out on the SemEval 2015 cQA dataset. We attain 58.35% on macroaveraged \mathrm{F_1} which outperforms the Top 1 system in the shared task by 1.16% and improves the state of the art deep neural network based method by 2.21%
GCN GAN: A Non linear Temporal Link Prediction Model for Weighted Dynamic Networks
Venue: IEEE INFOCOM 2019 IEEE Conference on Computer Communications
Year: 2019

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g. the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non linear characteristics and the informative link weights in the dynamic network, we introduce a novel non linear model GCN GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN) long short term memory (LSTM) as well as the generative adversarial network (GAN) Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide value range problem of edge weights in real life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state of the art competitors.
FRIED FOOD CONSUMPTION IS ASSOCIATED WITH A HIGHER RISK OF CARDIOVASCULAR MORTALITY IN THE PHYSICIANS' HEALTH STUDY
Year: 2016

Consumption of fried foods is highly prevalent worldwide. Despite reports of a positive association between fried food consumption (FFC) and risk of coronary heart disease (CHD) heart failure, hypertension, type 2 diabetes, and obesity, only limited data exist on its relation with mortality from
Running head HUMOR AND MORALITY 1 Why So Serious Experimental and Field Evidence that Morality and a Sense of Humor are Psychologically Incompatible
Year: 2014

Many of the jokes people enjoy carry a certain degree of moral violation. Since displaying humor often requires committing benign moral violations, we hypothesize that 1) a moral mindset stifles humor and 2) morally focused people are less humorous, and are therefore less liked by their workplace peers. Participants primed with a moral mindset were less likely to appreciate humor that involved benign moral violations (Study 1) and less likely to generate jokes others found funny (Study 2) compared to participants in the control condition. Additional field studies demonstrated that morally focused employees are seen as lacking a sense of humor by their coworkers and are therefore less liked and less socially popular (Studies 3 and 4) We further demonstrate that this mediational effect is stronger for targets who strongly endorse the purity/sanctity moral foundation (Study 4) These results suggest that morality and humor are to some degree psychologically incompatible, helping to explain why morally focused individuals are often socially marginalized in organizations.
PENGARUH KOMPETENSI PELATIHAN DAN PROMOSI JABATAN TERHADAP KINERJA BPR MAJESTY GOLDEN RAYA BATAM
Year: 2019

ABSTRAK Tujuan dari penelitian ini adalah untuk mengetahui pengaruh variabel Kompetensi, Pendidikan, dan Pengalaman Kerja secara parsial dan simultan terhadap kinerja pegawai di PT. BPR MAJESTY GOLDEN RAYA. Populasi dalam penelitian ini adalah karyawan PT. BPR MAJESTY GOLDEN RAYA Kota Batam yang berjumlah 45 orang dengan anggota populasi dijadikan sampel secara keseluruhan dengan metode kuesioner. Hasil penelitian ini dengan analisis regresi linear berganda. Berdasarkan hasil terdapat pengaruh signifikan kompetensi terhadap kinerja karyawan 5 (nilai alpha) Terdapat pengaruh signifikan pelatihan terhadap kinerja karyawan PT. BPR, dinyatakan diterima. Hal ini dibuktikan dengan nilai signifikan sebesar 0,000 0,05 (nilai alpha) Terdapat pengaruh signifikan promosi jabatan terhadap kinerja karyawan PT. BPR MAJESTY GOLDEN RAYA, dinyatakan diterima. Hal ini dibuktikan dengan nilai signifikan sebesar 0,046 0,05 (nilai alpha) Terdapat pengaruh signifikan Kompetensi, Pelatihan dan Promosi Jabatan secara bersama sama atau simultan terhadap kinerja karyawan PT. BPR MAJESTY GOLDEN RAYA, dinyatakan diterima. Hal ini dibuktikan dengan nilai signifikan uji F sebesar 0,000 0,05 (nilai alpha) Kata kunci Kompetensi, Pelatihan, Promosi Jabata n Kinerja pegawai
Real time detection of imaging errors in the Knife Edge Scanning Microscope through change detection
Venue: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Year: 2015

Advances in high resolution, high throughput 3D microscopy techniques are enabling subcellular investigation of whole small animal organs such as the mouse brain. Knife Edge Scanning Microscopy (KESM) is one such technique based on physical (or serial) sectioning to overcome diffraction limited imaging in optical sectioning approaches. However, due to the physical sectioning process depending on a mechanical process, vibration (chatter) and obstruction of the light path by floating debris can cause a varying degrees of image error. In this paper, we present a change detection based error detection method to minimize such errors. Change detection is done in three steps: preprocessing (subsampling and illumination equalization) change detection, and postprocessing (morphology based operation) Based on the change detection results, a finite state machine is used to alert a human operator or stop the machine. The method has been tested on three KESM data sets, demonstrating its effectiveness. The approach is expected to be widely applicable to other physical sectioning microscopy techniques.