nils-herrmann/clean_scirepeval huggingface dataset:
Minimization of Current Ripple and Switching Losses in Double Inverter Fed Wound Rotor Induction Machine Drive Using PWM Techniques
Venue: IEEE Transactions on Industrial Electronics
Year: 2020

In the topology of double inverter fed wound rotor induction machine (DI WRIM) two voltage source inverters (VSIs) are connected one each on stator and rotor terminals. This leads to ripple in both stator and rotor currents. In this paper, these current ripples are analyzed in terms of the stator and rotor flux ripples, which are estimated from their respective terminals. To reduce the current ripples, the VSIs of the stator and rotor terminals are modulated with different pulsewidth modulation (PWM) sequences. The switching loss in the DI WRIM is also analyzed for the proposed stator rotor PWM sequence combinations, based on the flexibility of changing the power factor, by changing the flux component of the current fed from either side of a DI WRIM drive. The proposed PWM combinations with the proposed flux component in currents are found to reduce the stator and rotor current ripples and, the switching losses. The analytically estimated root mean square current ripples for the proposed PWM combinations are validated using the simulation and the experimental results obtained from a 3 kW DI WRIM drive. The analysis on switching losses is validated by measuring the temperature rise of heat sinks present in the individual stator and rotor side VSIs of the DI WRIM drive.
From Zero Shot Learning to Cold Start Recommendation
Venue: AAAI
Year: 2019

Zero shot learning (ZSL) and cold start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto Encoder (LLAE) which challenges three cruxes, i.e. domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win win formulation, i.e. not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state of the art methods, but the consideration of CSR can benefit ZSL as well.
DAY LENGTH DEPENDENT DELAYED GREENING1, the Arabidopsis Homolog of the Cyanobacterial H+ Extrusion Protein, is Essential for Chloroplast pH Regulation and Optimization of Non Photochemical Quenching.
Venue: Plant cell physiology
Year: 2019

Plants convert solar energy into chemical energy through photosynthesis, which supports almost all life activities on earth. Because the intensity and quality of sunlight can change dramatically throughout the day, various regulatory mechanisms help plants adjust their photosynthetic output accordingly, including the regulation of light energy accumulation to prevent the generation of damaging reactive oxygen species. Non photochemical quenching (NPQ) is a regulatory mechanism that dissipates excess light energy, but how it is regulated is not fully elucidated. Herein, we report a new NPQ regulatory protein named Day Length dependent Delayed Greening1 (DLDG1) The Arabidopsis DLDG1 associates with the chloroplast envelope membrane, and the dldg1 mutant had a large NPQ value compared with wild type. The mutant also had a pale green phenotype in developing leaves but only under continuous light; this phenotype was not observed when dldg1 was cultured in the dark for =8 h per day. DLDG1 is a homolog of the plasma membrane localizing cyanobacterial proton extrusion protein A that is required for light induced H+ extrusion, and also shows similarity in its amino acid sequence to that of Ycf10 encoded in the plastid genome. Arabidopsis DLDG1 enhances the growth retardation phenotype of the Escherichia coli K+/H+ antiporter mutant, and the everted membrane vesicles of the E. coli expressing DLDG1 show the K+/H+ antiport activity. Our findings suggest that DLDG1 functionally interacts with Ycf10 to control H+ homeostasis in chloroplasts, which is important for the light acclimation response, by optimizing the extent of NPQ.
The effect of anonymity on the WTA WTP gap in endowment effect games
Year: 2012

The Effect of Anonymity on the WTA WTP Gap in Endowment Effect Games. (May 2012) Gregory Cohen Department of Economics Texas A&M University Research Advisor: Dr. Alex Brown Department of Economics I conducted experiments to determine whether anonymity affects the gap between the willingness to accept (WTA) and willingness to pay (WTP) in endowment effect games. Earlier research theorizes complete anonymity is necessary to eliminate the contamination of "signaling" within the endowment effect game. The "signaling" results when buyers over value their items and sellers under value the items they are trading for to signal they are good bargainers, which leads to suboptimal decisions. I find that the difference between an anonymous treatment and a non anonymous treatment yield WTA WTP gaps that are not statistically significant. I utilize a dictator game to ensure my anonymity procedure is sufficient.
Analysis of agriculture data using data mining techniques: application of big data
Venue: Journal of Big Data
Year: 2017

In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using data mining techniques like PAM, CLARA, DBSCAN and Multiple Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non experimental data optimizes the production and makes agriculture more resilient to climatic change.
To see ourselves as we need to see us: Ethnography's primative turn in the early cold war years
Year: 2009

Against a backdrop of calls for a more layered understanding of the dominant societal concerns which influence anthropologists' thinking, and thus the need to address the philosophies and assumptions that for so long misrepresented those characterized as 'primitive' along with others marked out as culturally or biologically inferior, I reflect on the existential crisis that engulfed Euroamerica in the early Cold War years. This was a threat anthropology was well placed to relieve; it did so in part by framing a natural 'primitive man' in opposition to 'civilized' humanity to restore the 'family of man' to psychic security. An image of 'Bushmen' etched by ethnographers rapidly emerged as a centerpiece of anthropological practice. I show how that image is indistinguishable from the fictional version popularized by Laurens van der Post and that both forms of it derive ultimately from the work of Jung. I argue that the image feeds readily into racialist discourse; thus, the time to render it obsolete has long passed.
E Government in developing countries: Framework of challenges and opportunities
Venue: 2012 International Conference for Internet Technology and Secured Transactions
Year: 2012

Responsibility of success the Electronic Government (E Government) is the responsibility of all citizens. E Government is a massive automated project that uses Information and Communication Technologies (ICTs) to promote more efficient and effective of government, facilitate more accessible government services, and make government more accountable to citizens. Through the implementation of E Government, there are a lot of challenges and difficulties, at the same time there are a lot of opportunities that will help us to accelerate the application of E Government. This paper will be introduced these challenges and opportunities for developing a successful E Government in developing countries, so we will discuss in detail the situation in Pakistan, India, Malaysia, Jordan, and others as examples. The aim of this study is find a general framework for the challenges and opportunities that facing the developing countries. Important issues will be discussing in this paper e.g. IT infrastructure, digital cultural, managerial issues, legislation issues, and budgeting.
Budgeting automation in construction companies
Year: 2016

The article focuses on corporate information systems intended for management accounting and budgeting (MAB) in project oriented construction companies. The author argues that the successful implementation of such systems requires a comprehensive approach involving the development or the adaptation of MAB philosophy taking into account the software functionality and related business processes. The author demonstrates that ERP software is the most effective technological platform for corporate MAB systems of project oriented companies. This is explained by the fact that the ERP systems have both sufficient functionality for project management automation and appropriate tools for management accounting and budgeting. Relying on this analysis, a comprehensive approach and appropriate solutions for developing corporate MAB systems in project oriented construction companies are proposed. The methodological solutions proposed in the field of MAB design take into account the specific nature of a contractor who carries out construction projects for external customers. In particular, the following methodological solutions are presented: establishing temporary project based profit centers in the construction company's financial structure; using administrative quasi projects and the "direct costing" method for fixed overheads costing in certain departments; establishing temporary profit centers related to production departments (internal subcontractors) This paper also shows how such a financial structure may be used in the SAP ERP system relying on the end to end "project" attribute. A budgeting structure with two versions of the corporate master budget is proposed. The first version of the structure is based on the contracting plans and the second on the last approved project budget versions. The paper shows that monitoring implementation is necessary for both master budget versions. The main assertions and results of the paper are applied in the implementation of a corporate MAB information system based on SAP ERP in a construction company. The results ofthe system's implementation and operation have shown significant improvement in the key financial indicators of the company, including _ profits, rate of return and net cash flow.
Genome wide association analysis for arabinoxylan content in common wheat (T. Aestivum L. flour
Year: 2021

are the major components of dietary fibers (DF) in wheat grain and their consumption has been associated with multiple health benefits. Selection of wheat varieties with higher AX content could be an efficient approach to improve the daily consumption of DF. Here, the genetic variability of the total (TOT AX) and water extractable arabinoxylans (WE AX) present in refined flour was investigated using a panel of 175 common wheat lines. Wide variation in both TOT AX (10.79 16.54 mg/g) and WE AX (3.19 7.63 mg/g) was found across the panel, with the genotype explaining a greater percentage of AX variation compared to the environment. Genomic regions associated with AX were investigated through genome wide association study (GWAS) which revealed the association of five SNPs on chromosomes 1BL and 5BS with TOT AX, and of 13 SNPs on chromosomes 1BL, 2BS, 6BS, 7A and 7BL with WE AX. Markers on chromosome 1BL were coincident for both the AX fractions and explained the greatest percentage of phenotypic variation (13.29 17.22% for TOT AX; 11.56 19.37% for WE AX) In silico analysis of the genomic region delimited by the most significant 1BL markers ~8 Mb) identified a predicted gene encoding for a glycosyl transferase (GT) of the GT61 family which is likely a candidate gene associated with the observed AX variation. Lastly, the four most significant 1BL GBS markers identified through the GWAS analysis were translated into Kompetitive allele specific PCR (KASP) markers. Results of this study constitute an important contribution for the improvement of grain DF in wheat breeding programs.
Automated gaze based mind wandering detection during computerized learning in classrooms
Venue: User Modeling and User Adapted Interaction
Year: 2019

AbstractWe investigate the use of commercial off the shelf (COTS) eye trackers to automatically detect mind wandering a phenomenon involving a shift in attention from task related to task unrelated thoughts during computerized learning. Study 1 (N 135 high school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student independent detectors of mind wandering, obtaining accuracies (mind wandering F1 0.59) substantially better than chance (F1 0.24) Study 3 investigated context generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze and video based mind wandering detection, finding that gaze based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F1 0.40) was considerably above chance (F1 0.24) albeit lower than offline detection accuracy from Study 1 (F1 0.59) a finding attributable to handling of missing data. We discuss our next steps towards developing gaze based attention aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts.
ANTIBACTERIAL ACTIVITY IN DIFFERENT EXTRACTS OF LANTANA CAMARA AGAINST ENTEROPATHOGENS
Year: 2015

Medicinal use of plants is the oldest form of healthcare known to mankind. India has a rich profusion of medicinal plants and 75% of its folk population is still using herbal preparations. Present study reports about antimicrobial potentiality of Lantana camara as its leaf extracts exhibit good antimicrobial, fungicidal, insecticidal and nematicidal properties and the plant might be a novel source of antimicrobial drug. Two enteropathogens as (A) B. subtilis (positive, obligate aerobic) and (B) E. coli (Gram negative, facultative anaerobic) were used for the present study. Four solvent phases viz, methanol, ethanol, acetone and aqueous were used for extraction of antimicrobial agent. The screening of antimicrobial property was done by well diffusion method. Leaves of Lantana camara showed excellent antibacterial activity in all the solvent phases used against both E. coli as well as B. subtilis Lantana is effective against both the bacteria The aqueous extract showed minimum ineffective antimicrobial activity against E. coli and B. subtilis, where as acetone phase showed maximum activity against B. subtilis and ethanol against E. coli as shown in the terms of maximum zone of inhibition.
Competitive Pricing Strategies in Social Networks
Year: 2018

We study pricing strategies of competing firms that sell heterogeneous products to consumers in a social network. Goods are substitutes and there are network externalities between neighboring consumers. We show that there exists a unique subgame perfect equilibrium where, in the first stage, firms choose prices while, in the second stage, individuals consume differentiated goods. In equilibrium, firms price discriminate based on the network positions and charge lower prices to more central consumers. We also show that, under some conditions, firms' equilibrium profits decrease when either the network becomes denser or network effects increase. In contrast, consumers always benefit from being more connected to each other because this increases their utilities and, at the same time, this lowers the prices due to intensified competition between firms. We determine the optimal network structure both for firms and consumers, and compare uniform pricing and discriminatory pricing from the perspectives of firms and consumers.
Image Clustering via Deep Embedded Dimensionality Reduction and Probability Based Triplet Loss
Venue: IEEE Transactions on Image Processing
Year: 2020

Image clustering is more challenging than image classification. Without supervised information, current deep learning methods are difficult to be directly applied to image clustering problems. Image clustering needs to deal with three main problems: 1) the curse of dimensionality caused by high dimensional image data; 2) extracting the effective image features; 3) combining feature extraction, dimensionality reduction and clustering. In this paper, we propose a new clustering framework called Deep Embedded Dimensionality Reduction Clustering (DERC) via Probability Based Triplet Loss, which effectively solves the above issues. To the best of our knowledge, the DERC is the first framework that effectively combines image embedding, dimensionality reduction, and clustering into the image clustering process. We also propose to incorporate a novel probability based triplet loss measure to retrain the DERC network as a unified framework. By integrating the reconstruction loss and the probability based triplet loss, we can improve the image clustering accuracy. Extensive experiments show that our proposed methods outperform state of the art methods on many commonly used datasets.
Simultaneous coded aperture and dictionary optimization in compressive spectral imaging via coherence minimization.
Venue: Optics express
Year: 2020

Coded aperture snapshot spectral imaging (CASSI) reconstructs a hyperspectral image from several two dimensional (2D) projections via compressive sensing. The reconstruction quality and the sampling efficiency of CASSI can be effectively improved by decreasing the coherence of the underlying sensing matrix. Efforts have been made to minimize the coherence with individual optimization on coded aperture or sparse basis. In this paper, a simultaneous optimization on the system projection and the over complete dictionary is introduced to minimize the Frobenius norm coherence. The dual disperser structure and the RGB image sensor are adopted for the lowest coherence in terms of system configuration. The coded aperture and the dictionary are optimized with genetic algorithm and gradient descent respectively, and simultaneous optimization is conducted iteratively. Low coherence of sensing matrix is acquired after the simultaneous optimization, with both reconstruction quality and sampling efficiency significantly improved. Compared to the non optimized system and state of the art systems with individually optimized coded aperture or dictionary, the simultaneous optimization promotes the peak signal to noise ratio by more than 5dB. The coherence minimization via simultaneous optimization on the system matrix and the sparse representation basis may open opportunities for further development of other compressive sensing based computational imaging systems.
Rejuvenated galaxies with very old bulges at the origin of the bending of the main sequence and of the 'green valley'
Venue: Monthly Notices of the Royal Astronomical Society
Year: 2019

We investigate the nature of star forming galaxies with reduced specific star formation rate (sSFR) and high stellar masses, those 'green valley' objects that seemingly cause a reported bending, or flattening, of the star forming main sequence. The fact that such objects host large bulges recently led some to suggest that the internal formation of bulges was a late event that induced the sSFRs of massive galaxies to drop in a slow downfall, and thus the main sequence to bend. We have studied in detail a sample of 10 galaxies at 0.45 z 1 with secure SFR from Herschel, deep Keck optical spectroscopy, and HST imaging from CANDELS allowing us to perform multiwavelength bulge to disc decomposition, and to derive star formation histories for the separated bulge and disc components. We find that the bulges hosted in these systems below main sequence are virtually all maximally old, with ages approaching the age of the Universe at the time of observation, while discs are young T50> 1.5 Gyr) We conclude that, at least based on our sample, the bending of the main sequence is, for a major part, due to rejuvenation, and we disfavour mechanisms that postulate the internal formation of bulges at late times. The very old stellar ages of our bulges suggest a number density of early type galaxies at z 1 3 higher than actually observed. If confirmed, this might represent one of the first direct validations of hierarchical assembly of bulges at high redshifts.
Measuring transient phase amplitude coupling using local mutual information
Venue: NeuroImage
Year: 2019

&NA; Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges interact and transiently couple. A particular form of CFC, phase amplitude coupling (PAC) has raised interest given the growing amount of evidence of its possible role in healthy and pathological brain information processing. Although several methods have been proposed for PAC estimation, only a few have addressed the estimation of the temporal evolution of PAC, and these typically require a large number of experimental trials to return a reliable estimate. Here we explore the use of mutual information to estimate a PAC measure (MIPAC) in both continuous and event related multi trial data. To validate these two applications of the proposed method, we first apply it to a set of simulated phase amplitude modulated signals and show that MIPAC can successfully recover the temporal dynamics of the simulated coupling in either continuous or multi trial data. Finally, to explore the use of MIPAC to analyze data from human event related paradigms, we apply it to an actual event related human electrocorticographic (ECoG) data set that exhibits strong PAC, demonstrating that the MIPAC estimator can be used to successfully characterize amplitude modulation dynamics in electrophysiological data. HighlightsWe propose a new information theory based approach (MIPAC) to estimate dynamic changes in phase amplitude coupling of electrophysiological signals.MIPAC is model free and can capture nonlinear interactions between phase and amplitude of a signal.Results from simulations and applications to real data suggest that MIPAC may be suitable for application in pathologies such as epilepsy, where the timevarying nature of PAC is crucial.