nils-herrmann/clean_scirepeval huggingface dataset:
In situ X ray diffraction and X ray absorption spectroscopy of electrocatalysts for energy conversion reactions
Year: 2020

In the face of climate change and rising energy consumption, electrocatalysis for the oxygen evolution reaction (OER) hydrogen evolution reaction (HER) and CO2 reduction reaction (CO2RR) have become promising catalytic processes to address these critical issues. By integrating OER and HER/CO2RR with renewable energy, it may potentially provide a promising way for a sustainable human society. Nonetheless, lack of understanding of catalysts under working conditions and reaction mechanisms restrict the development of efficient catalysts. In recent years, in situ techniques that can be used to investigate the structural transformation of catalysts during the reaction have attracted considerable research interest. Herein, recent developments in heterogeneous catalysts for OER, HER, and CO2RR explored by using in situ X ray diffraction and X ray absorption spectroscopy techniques have been comprehensively summarized. Finally, challenges and future perspectives in this field are also provided to pave the way for future developments.
ORB SLAM accelerated on heterogeneous parallel architectures
Year: 2021

SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end to end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB SLAM2. Through this, we attempt to implement the proposed optimization in FPGA based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high end GPU (NVIDIA GeForce 920MX) The implementation is achieved using high level synthesis based OpenCL for FPGA and CUDA for NVIDIA targeted boards.
Impact of Covid 19 on consumer behavior: Will the old habits return or die?
Venue: Journal of Business Research
Year: 2020

The COVID 19 pandemic and the lockdown and social distancing mandates have disrupted the consumer habits of buying as well as shopping. Consumers are learning to improvise and learn new habits. For example, consumers cannot go to the store, so the store comes to home. While consumers go back to old habits, it is likely that they will be modified by new regulations and procedures in the way consumers shop and buy products and services. New habits will also emerge by technology advances, changing demographics and innovative ways consumers have learned to cope with blurring the work, leisure, and education boundaries.
Research Methods in Linguistics
Year: 2010

Linguistics guides the reader through the key issues, principles, and contributions of core methods in linguistic research. It is an essential resource for researchers and research students looking for clear introductions to key concepts, accessible discussions of theory and practice through illustrative examples, and critical engagement with current debates. Topics include: developing research questions; combining methods; quantitative research designs (including questionnaires, chi square tests and t tests) corpus analysis; qualitative research methods (discourse analytic approaches, linguistic ethnography, interviews focus groups, multimodal analysis, and narrative analysis) With each chapter offering an overview of key issues, many examples from recent linguistic research, and suggestions for further reading, this book will be an invaluable resource for anyone working with linguistic data.
MEGA CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis
Venue: Bioinform.
Year: 2012

There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand alone executable (MEGA CC) along with an analysis prototyper (MEGA Proto) MEGA CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA CC and outline the steps for using MEGA CC in tandem with MEGA Proto for iterative and automated data analysis.
Current status and strategic possibilities on potential use of combinational drug therapy against COVID 19 caused by SARS CoV 2
Venue: Journal of biomolecular structure dynamics
Year: 2020

The spread of new coronavirus infection starting December 2019 as novel SARS CoV 2, identified as the causing agent of COVID 19, has affected all over the world and been declared as pandemic. Approximately, more than 8,807,398 confirmed cases of COVID 19 infection and 464,483 deaths have been reported globally till the end of 21 June 2020. Until now, there is no specific drug therapy or vaccine available for the treatment of COVID 19. However, some potential antimalarial drugs like hydroxychloroquine and azithromycin, antifilarial drug ivermectin and antiviral drugs have been tested by many research groups worldwide for their possible effect against the COVID 19. Hydroxychloroquine and ivermectin have been identified to act by creating the acidic condition in cells and inhibiting the importin (IMPa/b1) mediated viral import. There is a possibility that some other antimalarial drugs/antibiotics in combination with immunomodulators may help in combatting this pandemic disease. Therefore, this review focuses on the current use of various drugs as single agents (hydroxychloroquine, ivermectin, azithromycin, favipiravir, remdesivir, umifenovir, teicoplanin, nitazoxanide, doxycycline, and dexamethasone) or in combinations with immunomodulators additionally. Furthermore, possible mode of action, efficacy and current stage of clinical trials of various drug combinations against COVID 19 disease has also been discussed in detail. Communicated by Ramaswamy H. Sarma
Inflation Volatility and Forecast Accuracy
Year: 2011

This paper examines the statistical properties of inflation in a sample of inflation targeting and non inflation targeting countries. First, it analyses the time varying volatility of a measure of the persistent component of inflation. Based on this measure, inflation targeting countries (Australia, Canada, New Zealand, Sweden and the United Kingdom) have experienced a relatively more pronounced fall in the volatility of inflation than non inflation targeting countries (Austria, France, Germany, Japan and the United States) But it is hard to say whether inflation is more volatile in inflation targeting or non inflation targeting countries. Second, it analyses whether inflation became easier to forecast after the introduction of inflation targeting. It finds that inflation became easier to forecast in both inflation targeting and non inflation targeting countries; the improvement was greater for the former group but forecast errors remain smaller for the latter group.
The Effects of Trade Liberalization between Heterogeneous Countries on Endogenous Mergers
Year: 2008

This paper examines the effects of trade liberalization on merger behavior. We endogenize merger choice among owners in an oligopolistic industry in asymmetric countries to analyze the consequences of trade cost reductions on competitiveness and welfare. In this context, the non cooperative game supports asymmetric market structures. We also find that trade liberalization is not necessarily pro competitive in countries with the competitive advantage, even if trade costs are completely abolished. Moreover, the tariff jumping explanation of international mergers does not necessarily apply. The welfare analysis shows that merger behavior can significantly alter any gains from liberalization. Countries should consider enforcing competition in regional agreements. Specifically, to avoid a reduction in domestic welfare following trade liberalizing reductions in trade costs, a high cost country's optimal policy may be to ban international mergers.
A Study of Irrigation Requirements of Southwestern Landscape Trees
Year: 2017

Trees planted in urban landscapes need regular watering during establishment to develop a healthy root and shoot system. After establishment, tree species differ in how much supplemental irrigation they need to grow to their mature size and to remain healthy. Increasing the amount of irrigation water does not always result in more tree growth. Responses vary by how often and how much water a tree receives, the degree to which the soil dries between irrigations, and the amount of water a plant needs based on weather conditions, primarily solar radiation, temperature and wind. Recommendations for irrigation amounts and frequencies can be found in several publications (AMWUA, 2005; Costello and Jones, 2014) however, these values are generally based on expert agreements or anecdotal evidence and not on scientific experiments. Following are the results of a study conducted at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona to determine the irrigation needs of nine species of commonly planted landscape trees. After planting, trees were well watered for 1.5 years for establishment, allowing approximately 25% soil moisture depletion between irrigations during summer (June to August) and 35% depletion during the remainder of the year. In summer, this required weekly irrigation with 20 gallons of water per tree. After this establishment period, three irrigation frequency treatments were applied to trees. This study describes the subsequent four seasons of irrigation regimes after establishment and the irrigation frequency study. During the last year of the study, trees were grown without supplemental irrigation to simulate what happens when irrigation is suddenly turned off. Recommendations are given for irrigating the nine species of trees that were studied.
Modeling and Analysis of Distortion Caused by Markov Model Burst Packet Losses in Video Transmission
Venue: IEEE Transactions on Circuits and Systems for Video Technology
Year: 2009

This paper addresses the problem of distortion modeling for video transmission over burst loss channels characterized by a finite state Markov chain. Based on a detailed analysis of the error propagation and the bursty losses, a distortion trellis model is proposed, enabling us to estimate at the both the frame level and sequence level the expected mean square error (MSE) distortion caused by Markov model burst packet losses. The model takes into account the temporal dependencies induced by both the motion compensated coding scheme and the Markov model channel losses. The model is applicable to most block based motion compensated encoders, and most Markov model lossy channels as long as the loss pattern probabilities for that channel is computable. Based on the study of the decaying behavior of the error propagation, a sliding window algorithm is developed to perform the MSE estimation with low complexity. Simulation results show that the proposed models are accurate for all tested average loss rates and average burst lengths. Based on the experimental results, the proposed techniques are used to analyze the impact of factors such as average burst length on the average decoded video quality. The proposed model is further extended to a more general form, and the modeled distortion is compared with the data produced from realistic networks loss traces. The experiment results demonstrate that the proposed model is also accurate in estimating the expected distortion for video transmission in real networks.
CrossSense: Towards Cross Site and Large Scale WiFi Sensing
Venue: MobiCom
Year: 2018

We present CrossSense, a novel system for scaling up WiFi sensing to new environments and larger problems. To reduce the cost of sensing model training data collection, CrossSense employs machine learning to train, off line, a roaming model that generates from one set of measurements synthetic training samples for each target environment. To scale up to a larger problem size, CrossSense adopts a mixture of experts approach where multiple specialized sensing models, or experts, are used to capture the mapping from diverse WiFi inputs to the desired outputs. The experts are trained offline and at runtime the appropriate expert for a given input is automatically chosen. We evaluate CrossSense by applying it to two representative WiFi sensing applications, gait identification and gesture recognition, in controlled single link environments. We show that CrossSense boosts the accuracy of state of the art WiFi sensing techniques from 20% to over 80% and 90% for gait identification and gesture recognition respectively, delivering consistently good performance particularly when the problem size is significantly greater than that current approaches can effectively handle.
The Impact of Previous Schooling Experiences on a Quaker High School's Graduating Students' College Entrance Exam Scores, Parents' Expectations, and College Acceptance Outcomes
Year: 2010

THE IMPACT OF PREVIOUS SCHOOLING EXPERIENCES ON A QUAKER HIGH SCHOOL'S GRADUATING STUDENTS' COLLEGE ENTRANCE EXAM SCORES, PARENTS' EXPECTATIONS, AND COLLEGE ACCEPTANCE OUTCOMES Debbie K. Galusha University of Nebraska Advisor: Dr. John W. Hill The purpose of the study is to determine the impact of previous private, public, home, or international schooling experiences on a Quaker high school's graduating students' college entrance composite exam scores, parents' expectations, and college attendance outcomes. The study's results suggest that regardless of previous private, public, home, and international schooling experiences the research Quaker school equally prepared graduating high school students for postsecondary first year college academic success. Parent expectations for the research Quaker school graduating high school students indicated congruent preference for Quaker values for parents whose youth had previous private, public, home, and international schooling experiences. Furthermore, in this study high school students with previous private, public, home, and international schooling experiences were all predominately admitted to tier 1 and 2 postsecondary schools with a significant difference observed in the post secondary institution admission comparison of students with previous private verses public schooling experiences.
Searching overlapping communities for group query
Venue: World Wide Web
Year: 2015

In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community detection, which takes the whole network as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing a given node is a flexible and light weight solution, and also supports dynamic graphs very well. However, in some scenarios, it requires overlapping community search for group query, which means that the input is a set of nodes instead of one single node. To solve this problem, we propose an overlapping community search framework for group query, including both exact and heuristic solutions. The heuristic solution has four strategies, some of which are adjustable and self adaptive. We propose two parameters node degree and discovery power to trade off the efficiency and quality of the heuristic strategies, in order to make them satisfy different application requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of both exact and heuristic solutions.
Blockchain applications in supply chains, transport and logistics: a systematic review of the literature
Venue: Int. J. Prod. Res.
Year: 2020

This paper presents current academic and industrial frontiers on blockchain application in supply chain, logistics and transport management. We conduct a systematic review of the literature and find four main clusters in the co citation analysis, namely Technology, Trust, Trade, and Traceability/Transparency. For each cluster, and based on the pool of articles included in it, we apply an inductive method of reasoning and discuss the emerging themes and applications of blockchains for supply chains, logistics and transport. We conclude by discussing the main themes for future research on blockchain technology and its application in industry and services.
Benchmark Data Set and Method for Depth Estimation From Light Field Images
Venue: IEEE Transactions on Image Processing
Year: 2018

Convolutional neural networks (CNNs) have performed extremely well for many image analysis tasks. However, supervised training of deep CNN architectures requires huge amounts of labeled data, which is unavailable for light field images. In this paper, we leverage on synthetic light field images and propose a two stream CNN network that learns to estimate the disparities of multiple correlated neighborhood pixels from their epipolar plane images (EPIs) Since the EPIs are unrelated except at their intersection, a two stream network is proposed to learn convolution weights individually for the EPIs and then combine the outputs of the two streams for disparity estimation. The CNN estimated disparity map is then refined using the central RGB light field image as a prior in a variational technique. We also propose a new real world data set comprising light field images of 19 objects captured with the Lytro Illum camera in outdoor scenes and their corresponding 3D pointclouds, as ground truth, captured with the 3dMD scanner. This data set will be made public to allow more precise 3D pointcloud level comparison of algorithms in the future which is currently not possible. Experiments on the synthetic and real world data sets show that our algorithm outperforms existing state of the art for depth estimation from light field images.
Personalized travel route recommendation using collaborative filtering based on GPS trajectories
Venue: Int. J. Digit. Earth
Year: 2018

Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users' travel preferences. In this paper, we define users' travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+ Both methods consider users' personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users' travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user's travel behaviour is then generated based on the naive Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.
A Review of "Pedagogy of multiliteracies: rewriting Goldilocks"
Year: 2013

In the marketing blurbs printed on the back cover, Kamal K. Sridah says, 'I have waited for over 20 years for a volume such as this and Larry E. Smith says, 'I've been waiting for this book a long time Undoubtedly both of these were meant entirely positively, that the book is splendid and well worth the wait. Although it is certainly a valuable volume which discusses issues concerning intelligibility with admirable clarity and with plenty of interesting examples, the coverage of a few modern theories is rather thin and occasionally a bit dubious, the selection of some of the material is at times quirky, and overall it seems somewhat dated.
Information Technology Acceptance in South Africa: An Investigation of Perceived Usefulness, Perceived Ease of Use, and Actual System Use Constructs
Year: 2008

Information technology (IT) acceptance studies pay much attention to issues of significance in assessing the contributions of variables explaining IT usage for decision making in organizations. Davis' Technology Acceptance Model (TAM) states that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) are the two factors that govern the adoption and use of information technology. Actual System Use (behavior) consists of the number of times of systems use. The author conducted a survey of 31 organizations in KwaZulu/Natal, a region in South Africa, which implemented an Executive Information System (EIS) A validated survey instrument was administered to an EIS stakeholder in each organization surveyed. This paper reports on the PU, PEOU, and Actual System Use constructs for organizations surveyed in South Africa.
It's not just about protein turnover: the role of ribosomal biogenesis and satellite cells in the regulation of skeletal muscle hypertrophy
Venue: European journal of sport science
Year: 2019

Skeletal muscle has indispensable roles in regulating whole body health (e.g. glycemic control, energy consumption) and, in being central in locomotion, is crucial in maintaining quality of life. Therefore, understanding the regulation of muscle mass is of significant importance. Resistance exercise training (RET) combined with supportive nutrition is an effective strategy to achieve muscle hypertrophy by driving chronic elevations in muscle protein synthesis (MPS) The regulation of muscle protein synthesis is a coordinated process, in requiring ribosomes to translate mRNA and sufficient myonuclei density to provide the platform for ribosome and mRNA transcription; as such MPS is determined by both translational efficiency (ribosome activity) and translational capacity (ribosome number) Moreover, as the muscle protein pool expands during hypertrophy, translation capacity (i.e. ribosomes and myonuclei content) could theoretically become rate limiting such that an inability to expand these pools through ribosomal biogenesis and satellite cell (SC) mediated myonuclear addition could limit growth potential. Simple measures of RNA (ribosome content) and DNA (SC/Myonuclei number) concentrations reveal that these pools do increase with hypertrophy; yet whether these adaptations are a pre requisite or a limiting factor for hypertrophy is unresolved and highly debated. This is primarily due to methodological limitations and many assumptions being made on static measures or correlative associations. However recent advances within the field using stable isotope tracers shows promise in resolving these questions in muscle adaptation.