However , the temporal dynamics of cryptocurrencies is highly complex , and predictions are still challenging . In this study , we establish two prevailing machine learning models , fully-connected Artificial Neural Network ( ANN ) and the Long-Short-Term-Memory ( LSTM ) , to predictively model the price of several popular cryptocurrencies , including Bitcoin ( BTC ) , Ethereum ( ETH ) , Ripple ( XRP ) , Stellar Lumens ( XLM ) , Litecoin ( LTC ) , and Monero ( XMR ) . We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors . RoBERTa reduces WER by up to 30 % relative on an end-to-end LibriSpeech system and adds up to + 1.7 BLEU on state-of-the-art baselines for TED Talks low-resource pairs , with further gains from domain adaptation . In the multilingual setting , a single XLM can be used to rerank translation outputs in multiple languages . The numerical and qualitative properties of LPL scores suggest that LPLs capture sentence fluency better than autoregressive scores . Furthermore , common tools often restrict the number of metalevels that can be modeled and force modelers to use workarounds to express certain facts . To overcome these issues we present the Cross-Layer Modeler ( XLM ) , a modeling tool that supports multilevel modeling and allows co-evolution of metamodels and models . The XLM automatically performs co-evolution of constraints and gives instant feedback about model consistency . In this paper , data conversion technology based on relationship schema mapping is proposed , which has achieved data conversion between relationship databases . Exchanging and conversion of heterogeneous data which is the problem Data Sharing Platform facing to has been analyzed , practical technology of exchanging XML heterogeneous data has been studied , the specific implementation mechanism has been developed , the problems like message losing , relationship misjudging and field attribute changing during data exchanging has been solved , and the XLM heterogeneous data exchanging method during relationship databases is general and integral has finally proved by experiment results . In this paper , data conversion technology based on relationship schema mapping is proposed , which has achieved data conversion between relationship databases . Exchanging and conversion of heterogeneous data which is the problem Data Sharing Platform facing to has been analyzed , practical technology of exchanging XML heterogeneous data has been studied , the specific implementation mechanism has been developed , the problems like message losing , relationship misjudging and field attribute changing during data exchanging has been solved , and the XLM heterogeneous data exchanging method during relationship databases is general and integral has finally proved by experiment results . We participated on the word , sentence , and document-level tracks , encompassing 3 language pairs : nglish-German , English-Russian , and English-French . Our submissions build upon the recent OpenKiwi framework : we combine linear , neural , and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models . We compare systems individually and propose new ensemble techniques for word and sentence-level predictions . We participated on the word , sentence , and document-level tracks , encompassing 3 language pairs : English-German , English-Russian , and English-French . Our submissions build upon the recent OpenKiwi framework : we combine linear , neural , and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models . We compare systems individually and propose new ensemble techniques for word and sentence-level predictions . Our model , dubbed XLM-R , significantly outperforms multilingual BERT ( mBERT ) on a variety of cross-lingual benchmarks , including + 14.6 % average accuracy on XNLI , + 13 % average F1 score on MLQA , and + 2.4 % F1 score on NER . XLM-R performs particularly well on low-resource languages , improving 15.7 % in XNLI accuracy for Swahili and 11.4 % for Urdu over previous XLM models . We also present a detailed empirical analysis of the key factors that are required to achieve these gains , including the trade-offs between ( 1 ) positive transfer and capacity dilution and ( 2 ) the performance of high and low resource languages at scale . It has been commonly known that functions with epistasis can be efficiently optimized with crossovers which satisfy the inheritance of statistics . For efficient search of IBP , we propose extended crossover-like mutation ( extended XLM ) which generates a new instance around an instance with satisfying the inheritance of statistics . For overcoming multi-modality , we propose extended CCM for selection . This paper presents an approach based on constraint templates that tackles this evolution scenario by automatically updating constraints . We developed the Cross-Layer Modeler XLM approach which relies on incremental consistency-checking . As a case study , we performed evolutions of the UML-metamodel and 21 design models . That job , really , requires , besides rich knowledge and experience , also “ wasting ” a lot of time , nerves and renunciation of all kinds in order that journals that we edit , in their final form are rich with content , contains articles of superior/high scientific quality , results of research that can interest potential readers and users of scientific published content , but also linguistically , aestheticly and technically acceptable to attract and meet the needs and taste of potential readers . All above mentioned is sometimes very easily unsuccessful or the efforts of the publishers , which have the teams of qualified and capable associates involved in at least six of technological stages – from submission , editing , linguistic and technical preparations , printing , converting in the XLM format for on-line databases and painstaking process of communication with authors and co-authors that sometimes become unsuccessful and very frustrating . Unfortunately , this last mentioned can make life difficult for the editor and shake or diminish his/hers sense of love and pleasures for this beautiful and above all socially useful , but hard work in all scientific and academic community centers in any country and at any level of its development . Based on this framework , spatial Correlation-based Collaborative Medium Access Control ( CC-MAC ) protocol is described , which exploits the spatial correlation in the WSN in order to achieve efficient medium access . Furthermore , the cross-layer module ( XLM ) , which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes , is developed . The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN . Conventional approaches for identifying transcription factors , such as DNA sequence motif analysis , are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers . Here , we present binding analysis for regulation of transcription ( BART ) , a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile , based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse . This method demonstrates the advantage of utilizing publicly available data for functional genomics research . It seems more natural to consider how observable features of the objects in the ordered pair give rise to the truth of the relation , i.e. , to apply a discriminative approach . Abstract Bayesian Analogy with Relational Transformations ( BART ) is a discriminative model that can learn comparative relations from non-relational inputs ( Lu , Chen & Holyoak , 2012 ) . Here we show that BART can be extended to solve inference problems that require generation ( rather than classification ) of relation instances . This tutorial is aimed at providing such a resource . In addition to explaining the different components of BART using simple examples , we also discuss a framework , the General BART model that unifies some of the recent BART extensions , including semiparametric models , correlated outcomes , and statistical matching problems in surveys , and models with weaker distributional assumptions . By showing how these models fit into a single framework , we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework . BART combines the classic algorithm classification and regression trees ( C&RT ) and autoregressive models ARIMA . Using the BART model , we made a short-term forecast ( from 5 to 30 days ) for the 3 most capitalized cryptocurrencies : Bitcoin , Ethereum and Ripple . We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising ( falling ) and in the periods of transition dynamics ( change of trend ) . The problem of endogeneity that affects survey data in this area , is addressed by applying the Bayesian Additive Regression Trees ( BART ) methodology as in Cabras & Tena Horrillo ( 2016 ) . The BART methodology needs a prior and likelihood functions using the Markov Chain Monte Carlo ( MCMC ) algorithm to obtain the posterior distribution . Controlling for socio-economic , demographic and school factors , the predicted posterior distribution implies an increase , on average , of 16 points in the test scores . Originality : It is not usual the use of BART methodology for the analysis of credit scoring data . The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations . A key strength of BART is that default prior settings work reasonably well in a variety of problems . The challenge in extending BART is to choose the parameters of the DPM so that the strengths of the standard BART approach is not lost when the errors are close to normal , but the DPM has the ability to adapt to non-normal errors . For illustration , a summer daily rainfall example is utilized to show the feasibility of the BART ensemble model in improving the accuracy of torrential rainfall with linear regression and nonlinear regression model . Empirical results obtained reveal that the torrential rainfall prediction by using the BART ensemble model is generally better than those obtained using other models presented in this paper in terms of the same evaluation measurements . Our findings reveal that the BRAT ensemble model proposed here can be used as an alternative forecasting tool for a Severe Weather application in achieving greater forecasting accuracy and improving prediction quality further . The second section then describes a Behavioral Architecture for Robot Tasks ( BART ) being developed . A BART language is being built to provide a portable tool to support various robot programming and execution strategies , evidence accrual methods , and domain representations . This language is being used to develop behavioral programs that control tanks and tank platoons . The balloon analogue risk task ( BART ) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks . We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making . Voxel-wise GLM analysis was performed on 18human participants ( 10 males and 8females).In this work , we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT . This method employs multi-rate ( MR ) probe packet sequences with Kalman filtering . Our proposed method is an extension of the Bandwidth Available in Real Time ( BART ) method , so that the probe packet sequences are injected into the network path of interest . Using this technique , the probing rate within each probing sequence is varied . We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real . Going further , we adapt the BART procedure to incorporate informed prior information about variable importance . We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings . In this paper , we examine whether the improvements hold true when applied to a novel task , by replicating the BERT model from Munikar et al. , and swapping the embedding layer to the alternative models . Over the experiments , we found that AlBERT suffers significantly more accuracy loss than reported on other tasks , DistilBERT has accuracy loss similar to their reported loss on other tasks while being the fastest model to train , and RoBERTa reaches anew state-of-the-art accuracy for prediction on the SST-5 root level ( 60.2 % ) . In this work , we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips , with under 100 K parameters . Moreover , we perform acceleration experiments on CPUs , where we observe our multi-task model predicts intents and slots nearly 63x faster than even DistilBERT . Recently , pretraining methods for Natural Language Processing ( NLP ) , such as DistilBERT pre-trained model , have achieved state-of-the-art results on some NLP tasks such as text classification . In this work we focus on investigating the effect of applying DistilBERT pre-trained model and fine-tuning it for the important task of multi-label patent classification . Moreover , the large USPTO-3 M dataset ( 3,050,625 patents ) based on CPC subclass and subgroup level is used for the purpose of comparing previous deep learning related studies . First , we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino . Second , we pretrain better BERT and DistilBERT models for use within the Filipino setting . Third , we introduce a simple degradation test that benchmarks a model 's resistance to performance degradation as the number of training samples are reduced . This paper aims at learning representations for long sequences of continuous signals . Recently , the BERT model has demonstrated the effectiveness of stacked transformers for representing sequences of discrete signals ( i.e. word tokens ) . Inspired by its success , we adopt the stacked transformer architecture , but generalize its training objective to maximize the mutual information between the masked signals , and the bidirectional context , via contrastive loss . The reduction of distribution transformer no-load loss is particularly important as the ratio of no-load to load losses is nearly three . In this paper , the no-load operation of wound-core transformers under sinusoidal and distorted supply-voltage conditions is investigated . For that purpose , a 2-D nonlinear transient finite-element analysis taking into account hysteresis has been developed . The present invention relates to a transformer system and a direct current power supply for obtaining a dc current including a luminous body disposed on the primary side for converting a electric power into a light and a solar cell unit disposed on the secondary side , opposing the luminous body , for receiving light emitted from the luminous body and for converting the light into electric power , thereby constituting a transformer which is further provided with an input control circuit and an output control circuit so as to configure a dc power supply . According to this apparatus , a stable dc current can be obtained without generating the magnetic field and noise problems of typical wound core transformers . It is well-known that the main electrical protection of large capacity transformers is differential protection . As the special winding connection and current relationship of Le-Blanc , differential relays are not easy to apply directly without using the special turn ratio current transformers . Therefore , the purpose of this paper is to design a differential protective scheme that is built by normal turn ratio current transformers connected for Le-Blanc transformers . The concept of transmission line transformers ( TLTs , also known as equal delay transformers ) has been a distinct element of RF circuit design since at least 1944 , when Guanella disclosed an impedance transformer of novel design which consisted of a pair of interconnected transmission lines [ 1 , 2 ] . TLTs have been found to possess far wider bandwidth and much greater transmission efficiencies by arranging the windings of the TLT to have uniform transmission line properties . This paper describes the modeling of shell type distribution transformers with varying degrees of complexity . Results from several types of studies are reported and used to evaluate the effectiveness of FEA methods for resolving the forces exerted on the coils of a single-phase shell form transformer . An important component of the framework is an iterative correction procedure that feeds the accumulated discrepancies between the target and the actual design objective values back to the kriging surrogate to produce improved predictions . The efficacy of our methodology is demonstrated using two miniaturized impedance matching transformers with the optimized designs obtained at the cost of a few EM simulations of the respective circuits . The relevance of the iterative correction is corroborated through the comparative studies showing its superiority over rudimentary gradient-based refinement . Attention-Based Transformers vs. Recurrent Models . Automatically recognizing an existing semantic relation ( such as “ is a ” , “ part of ” , “ property of ” , “ opposite of ” etc . ) between two arbitrary words ( phrases , concepts , etc . ) is an important task affecting many information retrieval and artificial intelligence tasks including query expansion , common-sense reasoning , question answering , and database federation . The d-LNF is further developed by weighting the most relevant parameters to drive a simplified LNF . Both formulas have proven their reliability in calculating the load noise and therefore , they have been used in the daily designs to optimize for low noise transformers . However , the advantage of the LNF is that it can be easily exchanged between transformer manufacturers and utilities as it involves the main electrical design data without details about the designs of winding , shields , tank and insulations . Nevertheless , different methods of extracting harmonic currents that are used to control the active power filters , most of the time assume that the filter elements have little effect on the performance of the control system if it is well designed , algorithms . In the past , Leblanc and Scott connected transformers used to be a means to interconnect three-phase and two-phase systems . Today , three-phase systems have become so universal that it is seldom to require such connections . This paper develops an analysis of the parameters that most influence the dielectric design of distribution transformers . Through the simulation in Finite Elements were compared the influence levels of each parameter , thus determining which parameters that most influence in the equipment 's electrical stress .
However , the temporal dynamics of cryptocurrencies is highly complex , and predictions are still challenging . In this study , we establish two prevailing machine learning models , fully-connected Artificial Neural Network ( ANN ) and the Long-Short-Term-Memory ( LSTM ) , to predictively model the price of several popular cryptocurrencies , including Bitcoin ( BTC ) , Ethereum ( ETH ) , Ripple ( XRP ) , Stellar Lumens ( XLM ) , Litecoin ( LTC ) , and Monero ( XMR ) . We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors .
RoBERTa reduces WER by up to 30 % relative on an end-to-end LibriSpeech system and adds up to + 1.7 BLEU on state-of-the-art baselines for TED Talks low-resource pairs , with further gains from domain adaptation . In the multilingual setting , a single XLM can be used to rerank translation outputs in multiple languages . The numerical and qualitative properties of LPL scores suggest that LPLs capture sentence fluency better than autoregressive scores .
Furthermore , common tools often restrict the number of metalevels that can be modeled and force modelers to use workarounds to express certain facts . To overcome these issues we present the Cross-Layer Modeler ( XLM ) , a modeling tool that supports multilevel modeling and allows co-evolution of metamodels and models . The XLM automatically performs co-evolution of constraints and gives instant feedback about model consistency .
In this paper , data conversion technology based on relationship schema mapping is proposed , which has achieved data conversion between relationship databases . Exchanging and conversion of heterogeneous data which is the problem Data Sharing Platform facing to has been analyzed , practical technology of exchanging XML heterogeneous data has been studied , the specific implementation mechanism has been developed , the problems like message losing , relationship misjudging and field attribute changing during data exchanging has been solved , and the XLM heterogeneous data exchanging method during relationship databases is general and integral has finally proved by experiment results .
In this paper , data conversion technology based on relationship schema mapping is proposed , which has achieved data conversion between relationship databases . Exchanging and conversion of heterogeneous data which is the problem Data Sharing Platform facing to has been analyzed , practical technology of exchanging XML heterogeneous data has been studied , the specific implementation mechanism has been developed , the problems like message losing , relationship misjudging and field attribute changing during data exchanging has been solved , and the XLM heterogeneous data exchanging method during relationship databases is general and integral has finally proved by experiment results .
We participated on the word , sentence , and document-level tracks , encompassing 3 language pairs : nglish-German , English-Russian , and English-French . Our submissions build upon the recent OpenKiwi framework : we combine linear , neural , and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models . We compare systems individually and propose new ensemble techniques for word and sentence-level predictions .
We participated on the word , sentence , and document-level tracks , encompassing 3 language pairs : English-German , English-Russian , and English-French . Our submissions build upon the recent OpenKiwi framework : we combine linear , neural , and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models . We compare systems individually and propose new ensemble techniques for word and sentence-level predictions .
Our model , dubbed XLM-R , significantly outperforms multilingual BERT ( mBERT ) on a variety of cross-lingual benchmarks , including + 14.6 % average accuracy on XNLI , + 13 % average F1 score on MLQA , and + 2.4 % F1 score on NER . XLM-R performs particularly well on low-resource languages , improving 15.7 % in XNLI accuracy for Swahili and 11.4 % for Urdu over previous XLM models . We also present a detailed empirical analysis of the key factors that are required to achieve these gains , including the trade-offs between ( 1 ) positive transfer and capacity dilution and ( 2 ) the performance of high and low resource languages at scale .
It has been commonly known that functions with epistasis can be efficiently optimized with crossovers which satisfy the inheritance of statistics . For efficient search of IBP , we propose extended crossover-like mutation ( extended XLM ) which generates a new instance around an instance with satisfying the inheritance of statistics . For overcoming multi-modality , we propose extended CCM for selection .
This paper presents an approach based on constraint templates that tackles this evolution scenario by automatically updating constraints . We developed the Cross-Layer Modeler XLM approach which relies on incremental consistency-checking . As a case study , we performed evolutions of the UML-metamodel and 21 design models .
That job , really , requires , besides rich knowledge and experience , also “ wasting ” a lot of time , nerves and renunciation of all kinds in order that journals that we edit , in their final form are rich with content , contains articles of superior/high scientific quality , results of research that can interest potential readers and users of scientific published content , but also linguistically , aestheticly and technically acceptable to attract and meet the needs and taste of potential readers . All above mentioned is sometimes very easily unsuccessful or the efforts of the publishers , which have the teams of qualified and capable associates involved in at least six of technological stages – from submission , editing , linguistic and technical preparations , printing , converting in the XLM format for on-line databases and painstaking process of communication with authors and co-authors that sometimes become unsuccessful and very frustrating . Unfortunately , this last mentioned can make life difficult for the editor and shake or diminish his/hers sense of love and pleasures for this beautiful and above all socially useful , but hard work in all scientific and academic community centers in any country and at any level of its development .
Based on this framework , spatial Correlation-based Collaborative Medium Access Control ( CC-MAC ) protocol is described , which exploits the spatial correlation in the WSN in order to achieve efficient medium access . Furthermore , the cross-layer module ( XLM ) , which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes , is developed . The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN .
Conventional approaches for identifying transcription factors , such as DNA sequence motif analysis , are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers . Here , we present binding analysis for regulation of transcription ( BART ) , a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile , based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse . This method demonstrates the advantage of utilizing publicly available data for functional genomics research .
It seems more natural to consider how observable features of the objects in the ordered pair give rise to the truth of the relation , i.e. , to apply a discriminative approach . Abstract Bayesian Analogy with Relational Transformations ( BART ) is a discriminative model that can learn comparative relations from non-relational inputs ( Lu , Chen & Holyoak , 2012 ) . Here we show that BART can be extended to solve inference problems that require generation ( rather than classification ) of relation instances .
This tutorial is aimed at providing such a resource . In addition to explaining the different components of BART using simple examples , we also discuss a framework , the General BART model that unifies some of the recent BART extensions , including semiparametric models , correlated outcomes , and statistical matching problems in surveys , and models with weaker distributional assumptions . By showing how these models fit into a single framework , we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework .
BART combines the classic algorithm classification and regression trees ( C&RT ) and autoregressive models ARIMA . Using the BART model , we made a short-term forecast ( from 5 to 30 days ) for the 3 most capitalized cryptocurrencies : Bitcoin , Ethereum and Ripple . We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising ( falling ) and in the periods of transition dynamics ( change of trend ) .
The problem of endogeneity that affects survey data in this area , is addressed by applying the Bayesian Additive Regression Trees ( BART ) methodology as in Cabras & Tena Horrillo ( 2016 ) . The BART methodology needs a prior and likelihood functions using the Markov Chain Monte Carlo ( MCMC ) algorithm to obtain the posterior distribution . Controlling for socio-economic , demographic and school factors , the predicted posterior distribution implies an increase , on average , of 16 points in the test scores .
Originality : It is not usual the use of BART methodology for the analysis of credit scoring data . The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations .
A key strength of BART is that default prior settings work reasonably well in a variety of problems . The challenge in extending BART is to choose the parameters of the DPM so that the strengths of the standard BART approach is not lost when the errors are close to normal , but the DPM has the ability to adapt to non-normal errors .
For illustration , a summer daily rainfall example is utilized to show the feasibility of the BART ensemble model in improving the accuracy of torrential rainfall with linear regression and nonlinear regression model . Empirical results obtained reveal that the torrential rainfall prediction by using the BART ensemble model is generally better than those obtained using other models presented in this paper in terms of the same evaluation measurements . Our findings reveal that the BRAT ensemble model proposed here can be used as an alternative forecasting tool for a Severe Weather application in achieving greater forecasting accuracy and improving prediction quality further .
The second section then describes a Behavioral Architecture for Robot Tasks ( BART ) being developed . A BART language is being built to provide a portable tool to support various robot programming and execution strategies , evidence accrual methods , and domain representations . This language is being used to develop behavioral programs that control tanks and tank platoons .
The balloon analogue risk task ( BART ) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks . We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making . Voxel-wise GLM analysis was performed on 18human participants ( 10 males and 8females).In this work , we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT .
This method employs multi-rate ( MR ) probe packet sequences with Kalman filtering . Our proposed method is an extension of the Bandwidth Available in Real Time ( BART ) method , so that the probe packet sequences are injected into the network path of interest . Using this technique , the probing rate within each probing sequence is varied .
We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real . Going further , we adapt the BART procedure to incorporate informed prior information about variable importance . We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings .
In this paper , we examine whether the improvements hold true when applied to a novel task , by replicating the BERT model from Munikar et al. , and swapping the embedding layer to the alternative models . Over the experiments , we found that AlBERT suffers significantly more accuracy loss than reported on other tasks , DistilBERT has accuracy loss similar to their reported loss on other tasks while being the fastest model to train , and RoBERTa reaches anew state-of-the-art accuracy for prediction on the SST-5 root level ( 60.2 % ) .
In this work , we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips , with under 100 K parameters . Moreover , we perform acceleration experiments on CPUs , where we observe our multi-task model predicts intents and slots nearly 63x faster than even DistilBERT .
Recently , pretraining methods for Natural Language Processing ( NLP ) , such as DistilBERT pre-trained model , have achieved state-of-the-art results on some NLP tasks such as text classification . In this work we focus on investigating the effect of applying DistilBERT pre-trained model and fine-tuning it for the important task of multi-label patent classification . Moreover , the large USPTO-3 M dataset ( 3,050,625 patents ) based on CPC subclass and subgroup level is used for the purpose of comparing previous deep learning related studies .
First , we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino . Second , we pretrain better BERT and DistilBERT models for use within the Filipino setting . Third , we introduce a simple degradation test that benchmarks a model 's resistance to performance degradation as the number of training samples are reduced .
This paper aims at learning representations for long sequences of continuous signals . Recently , the BERT model has demonstrated the effectiveness of stacked transformers for representing sequences of discrete signals ( i.e. word tokens ) . Inspired by its success , we adopt the stacked transformer architecture , but generalize its training objective to maximize the mutual information between the masked signals , and the bidirectional context , via contrastive loss .
The reduction of distribution transformer no-load loss is particularly important as the ratio of no-load to load losses is nearly three . In this paper , the no-load operation of wound-core transformers under sinusoidal and distorted supply-voltage conditions is investigated . For that purpose , a 2-D nonlinear transient finite-element analysis taking into account hysteresis has been developed .
The present invention relates to a transformer system and a direct current power supply for obtaining a dc current including a luminous body disposed on the primary side for converting a electric power into a light and a solar cell unit disposed on the secondary side , opposing the luminous body , for receiving light emitted from the luminous body and for converting the light into electric power , thereby constituting a transformer which is further provided with an input control circuit and an output control circuit so as to configure a dc power supply . According to this apparatus , a stable dc current can be obtained without generating the magnetic field and noise problems of typical wound core transformers .
It is well-known that the main electrical protection of large capacity transformers is differential protection . As the special winding connection and current relationship of Le-Blanc , differential relays are not easy to apply directly without using the special turn ratio current transformers . Therefore , the purpose of this paper is to design a differential protective scheme that is built by normal turn ratio current transformers connected for Le-Blanc transformers .
The concept of transmission line transformers ( TLTs , also known as equal delay transformers ) has been a distinct element of RF circuit design since at least 1944 , when Guanella disclosed an impedance transformer of novel design which consisted of a pair of interconnected transmission lines [ 1 , 2 ] . TLTs have been found to possess far wider bandwidth and much greater transmission efficiencies by arranging the windings of the TLT to have uniform transmission line properties .
This paper describes the modeling of shell type distribution transformers with varying degrees of complexity . Results from several types of studies are reported and used to evaluate the effectiveness of FEA methods for resolving the forces exerted on the coils of a single-phase shell form transformer .
An important component of the framework is an iterative correction procedure that feeds the accumulated discrepancies between the target and the actual design objective values back to the kriging surrogate to produce improved predictions . The efficacy of our methodology is demonstrated using two miniaturized impedance matching transformers with the optimized designs obtained at the cost of a few EM simulations of the respective circuits . The relevance of the iterative correction is corroborated through the comparative studies showing its superiority over rudimentary gradient-based refinement .
Attention-Based Transformers vs. Recurrent Models . Automatically recognizing an existing semantic relation ( such as “ is a ” , “ part of ” , “ property of ” , “ opposite of ” etc . ) between two arbitrary words ( phrases , concepts , etc . ) is an important task affecting many information retrieval and artificial intelligence tasks including query expansion , common-sense reasoning , question answering , and database federation .
The d-LNF is further developed by weighting the most relevant parameters to drive a simplified LNF . Both formulas have proven their reliability in calculating the load noise and therefore , they have been used in the daily designs to optimize for low noise transformers . However , the advantage of the LNF is that it can be easily exchanged between transformer manufacturers and utilities as it involves the main electrical design data without details about the designs of winding , shields , tank and insulations .
Nevertheless , different methods of extracting harmonic currents that are used to control the active power filters , most of the time assume that the filter elements have little effect on the performance of the control system if it is well designed , algorithms . In the past , Leblanc and Scott connected transformers used to be a means to interconnect three-phase and two-phase systems . Today , three-phase systems have become so universal that it is seldom to require such connections .
This paper develops an analysis of the parameters that most influence the dielectric design of distribution transformers . Through the simulation in Finite Elements were compared the influence levels of each parameter , thus determining which parameters that most influence in the equipment 's electrical stress .
Prediction of Cryptocurrency Price Dynamics with Multiple Machine Learning Techniques Pseudolikelihood Reranking with Masked Language Models Cross-layer modeler: a tool for flexible multilevel modeling with consistency checking Database Conversion Based on Relationship Database Conversion Based on Relationship Schema Mapping Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task Unsupervised Cross-lingual Representation Learning at Scale Instance-based Policy Learning by Real-coded Genetic Algorithms and Its Application to Control of Nonholonomic Systems Supporting the Co-evolution of Metamodels and Constraints through Incremental Constraint Management A New Example of Unethical Behaviour in the Academic Journal “Medical Archives” Correlation-based cross-layer communication in wireless sensor networks BART: a transcription factor prediction tool with query gene sets or epigenomic profiles Generative Inferences Based on a Discriminative Bayesian Model of Relation Learning Bayesian additive regression trees and the General BART model. Forecasting cryptocurrency prices time series using machine learning approach Is information and communication technology satisfying educational needs at school? Application of bayesian additive regression trees in the development of credit scoring models in Brazil Fully Nonparametric Bayesian Additive Regression Trees A Novel Bayesian Additive Regression Trees Ensemble Model Based on Linear Regression and Nonlinear Regression for Torrential Rain Forecasting Specification and control of behavioral robot programs Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model MR-BART: multi-rate available bandwidth estimation in real-time Variable selection for BART: An application to gene regulation Language Representation Models for Fine-Grained Sentiment Classification Accelerating Natural Language Understanding in Task-Oriented Dialog Multi-label Patent Classification using Attention-Aware Deep Learning Model Establishing Baselines for Text Classification in Low-Resource Languages Contrastive Bidirectional Transformer for Temporal Representation Learning Harmonic Impact on Distribution Transformer No-Load Loss A Graph Transformation-based Approach to Task Allocation in Wireless Sensor Actor Networks A two-relay differential protective scheme for Le-Blanc transformers Transmission Line Transformers: Theory, Design and Applications — Part 1 Comparison of assumptions in computation of short circuit forces in transformers Rapid Optimization of Compact Microwave Passives Using Kriging Surrogates and Iterative Correction Recognizing Semantic Relations: Attention-Based Transformers vs. Recurrent Models Developing an Accurate Load Noise Formula for Power Transformers Single phase five levels inverter using Leblanc transformer for power quality improvement Analysis for the dielectric design optimization to distribution transformers by simulation in Finite Elements
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SIGSOFT FSE WMT MoDELS CogSci PM2HW2N BigComp SEKE ECIR (1)
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