nils-herrmann/clean_scirepeval huggingface dataset:
A review of multi criteria decision making approaches for evaluating energy storage systems for grid applications
Venue: Renewable and Sustainable Energy Reviews
Year: 2019

Energy storage systems (ESS) are seen as one of the main pillars for a renewable based energy system. Selecting the most suitable and sustainable ESS for a given project is a problem that involves multiple stakeholders with quite often diverging objectives that cannot all be fulfilled by a single technology. Several studies are available that tackle this problem applying multi criteria decision analysis (MCDA) However, these use very different Multi Attribute Decision Making (MADM) approaches, criteria and goals for decision support, why their results are difficult to compare or to reproduce. This work presents a review of existing MCDA literature using MADM as a tool for sustainability evaluation of grid tied ESS. Available studies are summarized, the goals, used MADM methods, and quantification of criteria are analyzed and discussed to provide tentative recommendations. The reviewed studies cover multiple technologies ranging from electrochemical, mechanical or electric ESS. Considered criteria are mainly structured around technology, economy, society, and environment, comprising a high number of individual sub criteria. The aggregation of these criteria is mainly realized through the Analytic Hierarchy Process (AHP) in combination with a wide set of other methods. The quantification of various criteria is often based on different literature sources wherein context free data for cost, and environmental impacts are used, leading in some cases to inconsistent comparisons in the assessments. Only in a few cases, assessments are linked to specific application requirements, which are decisive factors for the design of an ESS. A minority of the reviewed works include a representative set of decision makers in their approaches, wherein the number or type of participants is often not communicated transparently. Therefore, most of the studies are considered to have a limited orientation towards practical decision making, but they provide valuable information regarding MADM method development.
Comparisons between high resolution profiles of squared refractive index gradient M 2 measured by the Middle and Upper Atmosphere Radar and unmanned aerial vehicles (UAVs) during the Shigaraki UAV Radar Experiment 2015 campaign
Year: 2017

New comparisons between the square of the generalized potential refractive index gradient M2, estimated from the very high frequency (VHF) Middle and Upper Atmosphere (MU) Radar, located at Shigaraki, Japan, and unmanned aerial vehicle (UAV) measurements are presented. These comparisons were performed at unprecedented temporal and range resolutions (1 4 min and 20 m, respectively) in the altitude range 1.27 4.5 km from simultaneous and nearly collocated measurements made during the ShUREX (Shigaraki UAV Radar Experiment) 2015 campaign. Seven consecutive UAV flights made during daytime on 7 June 2015 were used for this purpose. The MU Radar was operated in range imaging mode for improving the range resolution at vertical incidence (typically a few tens of meters) The proportionality of the radar echo power to M2 is reported for the first time at such high time and range resolutions for stratified conditions for which Fresnel scatter or a reflection mechanism is expected. In more complex features obtained for a range of turbulent layers generated by shear instabilities or associated with convective cloud cells, M2 estimated from UAV data does not reproduce observed radar echo power profiles. Proposed interpretations of this discrepancy are presented.
Fekal mikrobiyota transplantasyonu ve hastaliklar uzerine etkileri
Year: 2020

Insan vucudunda mikrobiyota adi verilen cok sayida mikroorganizma bulunmaktadir. Bu mikroorganizmalar cesitli sistemleri etkilemek ile beraber en cok uzerinde durulan alan gastrointestinal sistemdir. Mikrobiyota uzerine ilk calismalar Clostridium difficile enfeksiyonuna karsi tedavi gelistirmek icin yapilmistir. Gunumuzde mikrobiyotanin daha iyi taninmasi ile gastrointestinal sistem disinda bircok mekanizmayi etkiledigi belirlenmistir. Mikrobiyotadaki olumsuzluklarin cesitli hastaliklara neden oldugu belirlenmis ve yeni tedavi statejileri gelistirilmeye calisilmaktadir. Bunlar arasinda son donemlerde fekal mikrobiyota transplantasyonuna (FMT) dikkat cekmektedir. FMT, cesitli ulkelerde populer olarak uygulanmakta ve umut vaat etmektedir. Ancak uygulama sonrasi uzun donem takibin olmamasi FMT konusunda elestirilere yol acmistir ve uygulamanin guvenilirligi konusunda daha cok calismaya ihtiyac duyulmaktadir.
A2J: Anchor to Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019

For 3D hand and body pose estimation task in depth image, a novel anchor based approach termed Anchor to Joint regression network (A2J) with the end to end learning ability is proposed. Within A2J, anchor points able to capture global local spatial context information are densely set on depth image as local regressors for the joints. They contribute to predict the positions of the joints in ensemble way to enhance generalization ability. The proposed 3D articulated pose estimation paradigm is different from the state of the art encoder decoder based FCN, 3D CNN and point set based manners. To discover informative anchor points towards certain joint, anchor proposal procedure is also proposed for A2J. Meanwhile 2D CNN (i.e. ResNet 50) is used as backbone network to drive A2J, without using time consuming 3D convolutional or deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets verify A2J's superiority. Meanwhile, A2J is of high running speed around 100 FPS on single NVIDIA 1080Ti GPU.
Release of sphingosine 1 phosphate from human platelets is dependent on thromboxane formation
Venue: Journal of thrombosis and haemostasis JTH
Year: 2011

Platelets release the immune modulating lipid sphingosine 1 phosphate (S1P) However, the mechanisms of platelet S1P secretion are not fully understood.  The present study investigates the function of thromboxane (TX) for platelet S1P secretion during platelet activation and the consequences for monocyte chemotaxis.  S1P was detected using thin layer chromatography in [3H]sphingosine labeled platelets and by mass spectrometry. Monocyte migration was measured in modified Boyden chamber chemotaxis assays.  Release of S1P from platelets was stimulated with protease activated receptor 1 activating peptide (PAR 1 AP, 100 mm) Acetylsalicylic acid (ASA) and two structurally unrelated reversible cyclooxygenase inhibitors diclofenac and ibuprofen suppressed S1P release. Oral ASA (500 mg single dose or 100 mg over 3 days) attenuated S1P release from platelets in healthy human volunteers ex vivo. This was paralleled by inhibition of TX formation. S1P release was increased by the TX receptor (TP) agonist U 46619, and inhibited by the TP antagonist ramatroban and by inhibitors of ABC transport. Furthermore, thrombin induced release of S1P was attenuated in platelets from TP deficient mice. Supernatants from PAR 1 AP stimulated human platelets increased the chemotactic capacity of human peripheral monocytes in a S1P dependent manner via S1P receptors 1 and 3. These effects were inhibited by ASA pretreatment of platelets.  TX synthesis and TP activation mediate S1P release after thrombin receptor activation. Inhibition of this pathway may contribute to the anti inflammatory actions of ASA, for example by affecting activity of monocytes at sites of vascular injury.
Learning ambidextrous robot grasping policies
Venue: Science Robotics
Year: 2019

An ambidextrous grasping policy trained on synthetic datasets consistently clears bins of up to 25 novel objects with a physical robot. Universal picking (UP) or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores "ambidextrous" robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex Net) 4.0, a substantial extension to previous versions of Dex Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel jaw and a vacuum based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three dimensional objects. On a physical robot with two grippers, the Dex Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour.
Comparison of chest CT images between confirmed and suspected cases of COVID 19/ Zhong Hua Fang She Xue Za Zhi
Year: 2020

To explore the value of chest CT features and clinical indexes in the differential diagnosis between suspected COVID 19 with two or more negative nucleic acid tests and confirmed COVID 19.  The clinical data and chest CT images of 105 cases withconfirmedCOVID 19 (55 males and 50 females, aged from 2 month to 88 years) and 97 cases with suspected COVID 19(59 males and 38 females, aged from 1 month to 93 years) were analyzed retrospectively in Shiyan Taihe Hospital from January 21 to February 10, 2020. kh2test and two independent sample t test were used to analyze the clinical data and CT signs of the two cases, with P<0.05 for the difference statistically significant.  Compared with the suspected patients, the average age of diagnosis of covid 19 was higher (t 2.460, P 0.01) The main pathological changes were pure ground glass (68 cases) and mixed ground glass density (53 cases) (kh2 50.016, P< 0.01) Interstitial thickening (83 cases) (kh2 55.395, P< 0.01) vascular thickening (73 cases) (kh2 57.527, P< 0.01) air bronchoscopic sign or bronchiectasis Zhang (67 cases) (kh2 17.899, P< 0.01) cord focus (54 cases) (kh2 5.500, P 0.02) easily distributed under the pleura and the long axis of the lesion was parallel to the pleura (89 cases) (kh2 23.597, P< 0.01) most of them had no pleural effusion (kh2 7.017, P< 0.01) both lesions were mainly distributed in patches (89 cases were confirmed, 87 suspected) (kh2 19.573, P< 0.01) In addition, the lesions of patients with confirmed covid 19 showed progress in short term (72 87, 82.76% and those with suspected covid 19 showed remission in short term (63 89, 70.78% The difference was statistically significant (kh2 51.114, P< 0.01) There was no significant difference in gender and distribution of pulmonary lobes (kh2 1.462, P= 0.23; kh2 7.381, P= 0.19) The number of white blood cells (kh2 17.891, P< 0.01) and the percentage of lymphocytes (kh2 11.151,P< 0.01) of covid 19 were mostly normal or decreased, creatine kinase (kh2 9.589, P< 0.01) were mostly normal, and erythrocyte sedimentation rate was mostly normal or increased (kh2 4.240, P= 0.04)  The imaging features and biochemical indexes of diagnosed COVID 19 are different from those of suspected COVID 19. The comparative analysis of imaging features, clinical indexes and reexamination are helpful for the differential diagnosis of COVID 19 and suspected COVID 19.  COVID 19; Tomography, X ray computed
Noisy BiLSTM Based Models for Disfluency Detection
Venue: INTERSPEECH
Year: 2019

This paper describes BiLSTM based models to disfluency detection in speech transcripts using residual BiLSTM blocks, self attention, and noisy training approach. Our best model not only surpasses BERT in 4 non Switchboard test sets, but also is 20 times smaller than the BERT based model  Thus, we demonstrate that strong performance can be achieved without extensively use of very large training data. In addition, we show that it is possible to be robust across data sets with noisy training approach in which we found insertion is the most useful noise for augmenting training data.
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Venue: GIS 2008
Year: 2008

These proceedings contain the papers selected for publication and presentation at the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS) in Irvine, California, USA, from November 5th to 7th, 2008. ACM GIS 2008 is the sixteenth event of an annual series of symposia and workshops with the mission to bring together researchers, developers, users, and practitioners carrying out research and development in novel systems based on geospatial data and knowledge. The symposium fosters interdisciplinary discussions and research in all aspects of Geographic Information Systems and Science (GIS) and provides a forum for original research contributions covering all conceptual, design, and implementation aspects of GIS and ranging from applications, user interface considerations, and visualization down to storage management and indexing issues. This is the first time that the conference is being held under the auspices of the new ACM Special Interest Group on Spatial Information (SIGSPATIAL) This year, in addition to the regular research paper track, we introduced two new conference tracks, namely a demonstration track and a Ph.D. showcase track. A program committee of 93 members reviewed the submissions. The call for papers led to 232 paper submissions over all three tracks. The research paper track attracted 193 research paper submissions, of which 38 were accepted as full papers and another 37 papers were accepted as poster papers. The Ph.D. Showcase track attracted 19 Ph.D. showcase submissions, of which 8 were accepted, while the demonstrations track attracted 20 demonstration paper submissions, of which 13 were accepted. These numbers indicate the continued health, interest, and growth of the research field of geographic information systems, and the need to bring its researchers, students, and industrial practitioners together.
Development of an Integrated Risk Assessment Method to Quantify the Life Safety Risk in Buildings in Case of Fire
Year: 2019

An integrated probabilistic risk assessment methodology is developed for the purpose of quantifying the life safety level of people present in buildings in the context of fire safety design. Multiple risk based concepts and tools have been developed in previous research to objectify performance based design methods for simple building types and layouts. However, these available models lack an integrated approach for challenging building designs and moreover they are not adequately coupled, most often resulting in a significant computational effort. Hence, there is a need for a practical and efficient framework for dealing with complicated building layouts and different occupancy types. Therefore, a computationally efficient quantitative risk assessment method is developed that provides a framework by combining deterministic sub models and probabilistic techniques to quantify the fire safety level by means of failure probabilities, individual and societal risk. The deterministic framework is supported by analytical and numerical models. The probabilistic framework is supported by response surface modelling, sampling techniques and limit state design. Following the theoretical description of the model, a case study of a five storey commercial shopping mall of 25,000 m2 is elaborated and discussed as proof of concept. Multiple fire, building and occupant variables are implemented in the model. Three different fire safety designs are compared, resulting in quantified risks between 10 6 and 10 8. The case study proves the validity of the newly developed integrated methodology for this type of buildings and its benefits in fire safety engineering.
CFR RL: Traffic Engineering With Reinforcement Learning in SDN
Venue: IEEE Journal on Selected Areas in Communications
Year: 2020

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal Cost Multi Path (ECMP) and selectively rerouting a few critical flows using Software Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR RL (Critical Flow Rerouting Reinforcement Learning) a Reinforcement Learning based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR RL achieves near optimal performance by rerouting only 10% 21.3% of total traffic.
A Case of Dengue Fever With Hemorrhagic Manifestations
Venue: Cureus
Year: 2020

Dengue fever is an arboviral infection spread by the Aedes mosquito with a wide spectrum of presentations encompassing simple flu like illness to hemorrhagic manifestations. Hemorrhagic complications range from simple petechiae and purpura to gastrointestinal bleeding, hematuria, and severe central nervous system (CNS) bleeds. Herein we present a case of a 38 year old male with dengue fever along with its hemorrhagic manifestations. Additionally, we conducted an extensive review of the literature to elucidate pathophysiology, diagnosis, and management of hemorrhagic manifestations in dengue fever.
Human Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach
Venue: IEEE Transactions on Intelligent Transportation Systems
Year: 2021

Considering that human driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision making module. Finally, two typical testing scenarios of lane change, i.e. merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision making framework considering different human like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%
Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis
Venue: Brain a journal of neurology
Year: 2018

Neuro axonal injury is a key factor in the development of permanent disability in multiple sclerosis. Neurofilament light chain in peripheral blood has recently emerged as a biofluid marker reflecting neuro axonal damage in this disease. We aimed at comparing serum neurofilament light chain levels in multiple sclerosis and healthy controls, to determine their association with measures of disease activity and their ability to predict future clinical worsening as well as brain and spinal cord volume loss. Neurofilament light chain was measured by single molecule array assay in 2183 serum samples collected as part of an ongoing cohort study from 259 patients with multiple sclerosis (189 relapsing and 70 progressive) and 259 healthy control subjects. Clinical assessment, serum sampling and MRI were done annually; median follow up time was 6.5 years. Brain volumes were quantified by structural image evaluation using normalization of atrophy, and structural image evaluation using normalization of atrophy, cross sectional, cervical spinal cord volumes using spinal cord image analyser (cordial) Results were analysed using ordinary linear regression models and generalized estimating equation modelling. Serum neurofilament light chain was higher in patients with a clinically isolated syndrome or relapsing remitting multiple sclerosis as well as in patients with secondary or primary progressive multiple sclerosis than in healthy controls (age adjusted P 0.001 for both) Serum neurofilament light chain above the 90th percentile of healthy controls values was an independent predictor of Expanded Disability Status Scale worsening in the subsequent year (P 0.001) The probability of Expanded Disability Status Scale worsening gradually increased by higher serum neurofilament light chain percentile category. Contrast enhancing and new/enlarging lesions were independently associated with increased serum neurofilament light chain (17.8% and 4.9% increase per lesion respectively; P 0.001) The higher the serum neurofilament light chain percentile level, the more pronounced was future brain and cervical spinal volume loss: serum neurofilament light chain above the 97.5th percentile was associated with an additional average loss in brain volume of 1.5% (P 0.001) and spinal cord volume of 2.5% over 5 years (P 0.009) Serum neurofilament light chain correlated with concurrent and future clinical and MRI measures of disease activity and severity. High serum neurofilament light chain levels were associated with both brain and spinal cord volume loss. Neurofilament light chain levels are a real time, easy to measure marker of neuro axonal injury that is conceptually more comprehensive than brain MRI.
Pengaruh corporate social responsibility disclosure,Kepemilikan manajerial, kepemilikan institusi,Ukuran dewan, dan dualitas ceo terhadapNilai perusahaan otomotifDi asia tenggara
Year: 2018

The purpose of this study is to determine the effect of corporate social responsibility disclousure, managerial ownership, institutional ownership, board size, and CEO duality on firm value automotive in Southeast Asia. This study used a sample of forty three automotive companies listed on the Stock Exchange of Southeast Asia which publishes data from 2014 to 2016. Sampling technique is nonprobability sampling using saturated sampling (census) The test results show that corporate social responsibility disclosure variable does not affect the value of the company. Awareness of automotive companies in Southeast Asian countries still tend not to do corporate social responsibility disclosure because it is based on ISO 26000 which is still voluntary. The next result, managerial ownership affect the value of the company, the ownership of the institution affect the company value, the size of the board affect the value of the company, and the duality of the CEO affect the value of automotive companies in Southeast Asia. Keyword firm value, corporate social responsibility disclousure, managerial ownership, institutional ownership, board size, and CEO duality