We also show that our proposed sparse attention method achieves comparable or better results than the previous sparse attention method , but significantly reduces training and testing time . For example , the inference speed is twice that of sparsemax in Transformer model . Code will be available at \url{this https URL } A 4-path architecture is realized in 65 nm CMOS . An off-chip transformer acts as a balun , improves filter-Q and realizes impedance matching . The differential architecture reduces clock-leakage and suppresses selectivity around even harmonics of the clock . At 900 MHz and 2 V supply voltage , the mixer dissipates 4 mA , provides a 9.3 dB power gain , 7.88 dB noise figure ( NF ) and a 3 dBm input intercept point ( IIP3 ) . The same design is implemented using an off chip transformer . Thus , strategies to adapt the PT to the variation of the output load are being pursued . In this article , a novel tunable piezoelectric transformer ( TPT ) for ac – dc and dc – dc high-power converters is presented . This TPT is designed to operate in the radial mode and is fabricated using cofired multilayer process . The high-frequency magnetic linked solid-state transformer ( SST ) has the tremendous potential to solve the issues related to interfacing the grid with renewable energy sources and the non-linear loads as well as to provide the additional control functionalities . Several H-bridges are usually connected in series for the medium voltage grid integration of the SST . Over the last two years there have been design studies to investigate the impact of improvements in fast energy storage systems on the design of simulator upgrades ( Double-EAGLE and Decade Quad ) and on larger future simulators ( 40-MA to 60-MA PRS machine ) . The fast energy storage systems investigated in these design studies included Fast Marx Generators ( FMG with √LC = 200 ns and √LC = 300 ns ) and Linear Transformer Drivers ( LTD ) . A design sketch of a compact 20-MA PRS driver and a potential upgrade of Double-EAGLE using FMG technology will be presented . In view of the status that field agricultural transformer are frequently stolen , which have seriously influenced people 's daily life and agricultural production .on the basis of analyzing existing agricultural transformer antitheft measures , in accordance with the advantage of GPRS(General Packet Radio Service ) , develop remote field agricultural transformer real-time monitoring system . Discovers the cracksman by the human body infrared induction module and gains the scene picture by the serial port camera , adopt FPGA technology for image compression and achieve the timely delivery of information with the GPRS network , this system can not only be used to guard theft in the field of agricultural transformer , but can also be used in the on-site monitoring of other electrical equipment . Summary form only as given . A Le-Blanc transformer is a special winding connection transformer for the single-phase power supply of electrified railway and it has been installed in the electrified railway of Taiwan ( Tairail ) for more than twenty years . It is well-known that the main electrical protection of large capacity transformers is differential protection . In case the customer is failed to maintain the system voltage well within the limit , then unnecessary penalty is charged by the power supply board . As per the demand and service , it is necessary to place tap-change transformer or regulating transformer to maintain the acceptable voltage limit . For that , first various literature reviews were collected on role of tap-changing transformer in power distribution study . The Wolfe linear search method is used to provide the direction of optimization when the consequence is dominated by filter . Furthermore , the linearization strategy of transformer ratio and the dynamic adjustment strategy of step size are proposed , which simplifies the formulation and improves the convergence of the algorithm . Case studies based on Ward-Hale 6-bus system demonstrate the efficacy of the new method in solving the OPF problems . Abstract— This paper proposes a new three-port , T-type multi-level converter ( TPTMC ) that simultaneously outputs two sinusoidal AC voltages with different frequencies : 50 Hz and 60 Hz . The proposed TPTMC is composed of a DC voltage source , two T-type power electronic legs , a filter inductor set , a frequency separated transformer ( FST ) , a 60 Hz filter capacitor , a 50-Hz filter capacitor , and a DSP controller . The most salient feature is that the proposed TPTMC outputs two sinusoidal AC voltages with different frequencies using only a power converter . While the proposed architecture achieves state - of - the - art results on several machine translation tasks , it requires a large number of parameters and training iterations to converge . We propose Weighted Transformer , a Transformer with modified attention layers , that not only outperforms the baseline network in BLEU score but also converges faster . Specifically , we replace the multi - head attention by multiple self - attention branches that the model learns to combine during the training process . ahmed2017branchconnect analyze learned branching , through gates , in the context of computer vision while in gastaldi2017shake , the author analyzes a two - branch model with randomly sampled weights in the context of image classification . subsection : Transformer Network The original Transformer network uses an encoder - decoder architecture with each layer consisting of a novel attention mechanism , which the authors call multi - head attention , followed by a feed - forward network . See Table [ reference ] . While the Transformer gets close but does not exceed the state of the art on the SST dataset , it exceeds both the mLSTM and ELMo baseline as well as both Watson and Google Sentiment APIs on the company tweets . This is despite optimally calibrating the API results on the test set . subsection : Machine Translation On the WMT 2014 English - to - German translation task , the big transformer model ( Transformer ( big ) in Table [ reference ] ) outperforms the best previously reported models ( including ensembles ) by more than BLEU , establishing a new state - of - the - art BLEU score of . The configuration of this model is listed in the bottom line of Table [ reference ] . We adopt similar factorizations of the joint pixel distribution as previous work . Following recent work on modeling text , however , we propose eschewing recurrent and convolutional networks in favor of the Image Transformer , a model based entirely on a self - attention mechanism . The specific , locally restricted form of multi - head self - attention we propose can be interpreted as a sparsely parameterized form of gated convolution . We compare our architecture with the previous results in Table [ reference ] . Under the model size constraint , the 12 - layer Transformer - XL achieves a new SoTA result , outperforming the 12 - layer vanilla Transformer from by 0.05 , while both Transformer variants have a large margin over conventional RNN - based models . Notably , our 12 - layer architecture achieves the same result as the 64 - layer network from , using only 17 % of the parameter budget . The second type of generation we consider in this paper is deformation . In this case , we propose Adversarial Spatial Transformer Network ( ASTN ) which learns how to rotate “ parts ” of the objects and make them hard to recognize by the detector . By competing against these networks and overcoming the obstacles , the FRCN learns to handle object occlusions and deformations in a robust manner . paragraph : Transformer Encoder ( ) : We use a multi - layer bidirectional Transformer encoder vaswani2017attention to map the input representation vectors ( ) into a sequence of contextual embedding vectors . This is the shared representation across different tasks . Without the constraint of an autoregressive factorization of the output distribution , we no longer need to prevent earlier decoding steps from accessing information from later steps . Thus we can avoid the causal mask used in the self - attention module of the conventional Transformer ’s decoder . Instead , we mask out each query position only from attending to itself , which we found to improve decoder performance relative to unmasked self - attention . Our re - ranking strategy selects a headline that contains source - side words the most . Table [ reference ] shows that Transformer + + with this re - ranking ( + Re - ranking ) achieved better scores than the state - of - the - art . subsection : Analysis of Output Length Gaussian process tomography ( GPT ) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak , given some model for the underlying physical processes involved . GPT can also be used , thanks to Bayesian formalism , to perform model selection -- i.e. , comparing different models and choosing the one with maximum evidence . Summary Geophysical survey datasets used for resource exploration and detection are large in volume , dense in time and space , and have many dimensions . We present the Geophysics Processing Toolkit ( GPT ) , an application for processing geophysical survey data prior to interpretation and inversion . Initially developed as a processing toolkit for airborne electromagnetic ( AEM ) data , our application can be extended to beyond electromagnetics processing and inversion to incorporate multiple geophysical datasets such as gravity and magnetics . Generalized perturbation theory ( GPT ) has been widely applied to sensitivity analysis of engineering systems models for decades . It is proven to be an efficient tool to calculate sensitivities in various applications such as uncertainty quantification , adaptive simulation and design optimization [ 1 - 3 ] . This new construct allows us understand the i ° generalityi± nature of GPTs in a more comprehensive way . Moreover , we suggest that since firms are not only the i ° usersi± but i ° innovatorsi± of specific technologies , the traditional GPT classification , which is primarily based on patent citations is problematic . We then created a new mechanism to classify the GPTs . GPT public key cryptosystem was proposed by Gabidulin , Paramonov and Tretjakov in 1991 . This cryptosystem is based on rank error correcting codes . This study explores group package tour ( GPT ) itineraries based on comparative risk methodology using the fuzzy Preference Ranking Organization Method for Enrichment Evaluation ( PROMETHEE ) . It combines the concepts of fuzzy sets to represent the uncertain information in intrinsic risks with PROMETHEE , a subgroup of Multi-Criteria Decision Making methods . The proposed Deconstruct and Preserve for all ( DaP∀ ) aims at mitigating the risk involved in exposing any data to these procedures and ensures that third parties get an exact match ; the process works on SSDs , GPT formatted devices , and other traditional formats , e.g. HDD . The results show a GPT TRIM enabled SSD imaged multiple times produces verification of matched hashes . With these results , it is proposed that DaP∀ should be considered as a Standard Operating Procedure ( SOP ) when completing data acquisition . The General Particle Tracer ( GPT ) code has been extended with a multi-dimensional optimizer and solver to automate the final stages of a design process . The new solver can be used for all GPT simulations , including 3D space-charge and particle-wave interaction . The internal algorithms and two examples are presented in this paper . The tests were carried out based on complete test procedures with test bases located on the premises of AGH University and city meadow in Krakow . The following instruments were tested : Trimble S6 , Leica TCRA 1101 and Topcon GPT 7500 . With a trained BP neural network modular , the optimized GPT can be automatically generated according to the specifications of a planning problem . Compared with previous methods of manual and empirical tuning of GPT for individual planning problems , the proposed approach is self-adaptive . Numerical experiments verified the performance of the proposed approach and furthermore showed that planning with BP-generated GPTs is more stable . A recent survey is included in gao2018neural . Some of the most prominent examples are ELMo elmo2018 , GPT gpt2018 and BERT bert2018 . These are neural network language models trained on text data using unsupervised objectives . For the self - attention layer , we adopt the multi - head attention mechanism defined in VaswaniSPUJGKP17 . The feed - forward network consists of two linear transformations with a GELU HendrycksG16 activation in between , following OpenAI GPT RadfordNSS18 . Each sub - layer is placed inside a residual block HeZRS16 . document : Passage Re - ranking with BERT Recently , neural models pretrained on a language modeling task , such as ELMo peters2017semi , OpenAI GPT radford2018improving , and BERT devlin2018bert , have achieved impressive results on various natural language processing tasks such as question - answering and natural language inference . In this paper , we describe a simple re - implementation of BERT for query - based passage re - ranking . This has been the subject of substantial interest recently , where multiple approximations were proposed to reduce the quadratic memory requirement using sparse attention matrices . In this work , we propose to augment sparse Transformer blocks with a dense attention-based $ \textit{global memory}$ of length $ M$ ( $ \ll L$ ) which provides an aggregate global view of the entire input sequence to each position . Our augmentation has a manageable $ O(M\cdot(L+M))$ memory overhead , and can be seamlessly integrated with prior sparse solutions . Self-attention is able to model long-term dependencies , but it may suffer from the extraction of irrelevant information in the context . To tackle the problem , we propose a novel model called \textbf{Explicit Sparse Transformer}. Explicit Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments . Extensive experimental results on a series of natural language processing and computer vision tasks , including neural machine translation , image captioning , and language modeling , all demonstrate the advantages of Explicit Sparse Transformer in model performance . In order to solve these two issues , we first propose convolutional self attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism . Then , we propose LogSparse Transformer with only $ O(L(\log L)^{2})$ memory cost , improving the time series forecasting in finer granularity under constrained memory budget . Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art . Abstract Power transformers are designed to supply linear loads at nominal frequency ; however , the development of power electronic devices and controllable loads that is generally called non-linear loads is promotion . Non-linear loads lead to temperature rise of transformers significantly and causes decrease their lifespan dramatically . This paper gives an overview of the process of designing partial core transformers using a reverse modelling technique . Modifications are made to full-core equivalent circuit components to accommodate partial core transformers . In this work , a variable voltage over fixed cable and MCCB switch contact resistance test method is used to verify ratings . This method uses two variable transformers driven by two separate stepper motors controlled by a microcontroller with a final step-down toroid transformer . To measure the circuit breaker , trip current level an accurate Hall-Effect current sensor was used and the trip time is determined by a microcontroller-based peripheral timer . The invention relates to a high-power waveform generator , which comprises two oil-immersed isolation transformers . Secondary windings of the oil-immersed isolation transformers are respectively connected to a dry-typed transformer ; each dry-typed transformer has 26 secondary windings which are respectively connected to 26 power modules ; 4 dry-typed transformers can be connected with 104 power modules respectively . The 104 power modules are connected with each other from head to tail and are output in series to obtain the highest output value of 145kV/100A . How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network ? We propose Equivariant Transformers ( ETs ) , a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups . Through the use of specially-derived canonical coordinate systems , ETs incorporate functions that are equivariant by construction with respect to these transformations . Electrical transformers spanning the integument ( skin or mucosa ) have long been attractive means of circumventing this risk . Yet all existing skin surface transformers leak substantial magnetic flux , with an intrinsic risk of battery draining cross-coupling by any nearby conductor , requiring strict control of surroundings . In a progression of designs , we have used the walls of a small pouch , surgically formed from ileum , rather than skin , as a barrier . By making the spiral of the primary winding wider and that of the secondary narrower , the ohmic loss of the primary winding and the spiral-substrate loss of the secondary winding of the transformer are minimized simultaneously . The silicon consumption of the step-up transformer impedance transformation network is also minimized by employing the stack configuration of transformers with the secondary winding implemented using an upper metal layer and the primary winding implemented using a lower metal layer . Two RF power harvesters , one with the proposed step-up transformer impedance transformation network and a modified Nakamoto rectifier and the other with a resonating LC impedance transformation network and the same rectifier , are designed in [ email protected ] 1.8V CMOS technology . This paper describes a general technique for reducing oscillator phase noise in < italic > LC</italic > voltage-controlled oscillators ( VCOs ) . Phase noise is reduced using a circuit topology that decouples the < italic > LC</italic > tank from the active devices using capacitive transformers , thereby achieving a higher amplitude of oscillation not limited by the transistor breakdown voltage . Moreover , the proposed VCO topology reduces active device noise injection into the tank by further improving phase noise . The proposed methodology and its performance is demonstrated on two test systems including ( a ) nonlinear R-L electric circuit , and ( b ) power electronic circuit ( switching system ) . The methodology is very useful for systems with power electronics and nonlinear devices such as saturable transformers/reactors and surge arresters .
We also show that our proposed sparse attention method achieves comparable or better results than the previous sparse attention method , but significantly reduces training and testing time . For example , the inference speed is twice that of sparsemax in Transformer model . Code will be available at \url{this https URL }
A 4-path architecture is realized in 65 nm CMOS . An off-chip transformer acts as a balun , improves filter-Q and realizes impedance matching . The differential architecture reduces clock-leakage and suppresses selectivity around even harmonics of the clock .
At 900 MHz and 2 V supply voltage , the mixer dissipates 4 mA , provides a 9.3 dB power gain , 7.88 dB noise figure ( NF ) and a 3 dBm input intercept point ( IIP3 ) . The same design is implemented using an off chip transformer .
Thus , strategies to adapt the PT to the variation of the output load are being pursued . In this article , a novel tunable piezoelectric transformer ( TPT ) for ac – dc and dc – dc high-power converters is presented . This TPT is designed to operate in the radial mode and is fabricated using cofired multilayer process .
The high-frequency magnetic linked solid-state transformer ( SST ) has the tremendous potential to solve the issues related to interfacing the grid with renewable energy sources and the non-linear loads as well as to provide the additional control functionalities . Several H-bridges are usually connected in series for the medium voltage grid integration of the SST .
Over the last two years there have been design studies to investigate the impact of improvements in fast energy storage systems on the design of simulator upgrades ( Double-EAGLE and Decade Quad ) and on larger future simulators ( 40-MA to 60-MA PRS machine ) . The fast energy storage systems investigated in these design studies included Fast Marx Generators ( FMG with √LC = 200 ns and √LC = 300 ns ) and Linear Transformer Drivers ( LTD ) . A design sketch of a compact 20-MA PRS driver and a potential upgrade of Double-EAGLE using FMG technology will be presented .
In view of the status that field agricultural transformer are frequently stolen , which have seriously influenced people 's daily life and agricultural production .on the basis of analyzing existing agricultural transformer antitheft measures , in accordance with the advantage of GPRS(General Packet Radio Service ) , develop remote field agricultural transformer real-time monitoring system . Discovers the cracksman by the human body infrared induction module and gains the scene picture by the serial port camera , adopt FPGA technology for image compression and achieve the timely delivery of information with the GPRS network , this system can not only be used to guard theft in the field of agricultural transformer , but can also be used in the on-site monitoring of other electrical equipment .
Summary form only as given . A Le-Blanc transformer is a special winding connection transformer for the single-phase power supply of electrified railway and it has been installed in the electrified railway of Taiwan ( Tairail ) for more than twenty years . It is well-known that the main electrical protection of large capacity transformers is differential protection .
In case the customer is failed to maintain the system voltage well within the limit , then unnecessary penalty is charged by the power supply board . As per the demand and service , it is necessary to place tap-change transformer or regulating transformer to maintain the acceptable voltage limit . For that , first various literature reviews were collected on role of tap-changing transformer in power distribution study .
The Wolfe linear search method is used to provide the direction of optimization when the consequence is dominated by filter . Furthermore , the linearization strategy of transformer ratio and the dynamic adjustment strategy of step size are proposed , which simplifies the formulation and improves the convergence of the algorithm . Case studies based on Ward-Hale 6-bus system demonstrate the efficacy of the new method in solving the OPF problems .
Abstract— This paper proposes a new three-port , T-type multi-level converter ( TPTMC ) that simultaneously outputs two sinusoidal AC voltages with different frequencies : 50 Hz and 60 Hz . The proposed TPTMC is composed of a DC voltage source , two T-type power electronic legs , a filter inductor set , a frequency separated transformer ( FST ) , a 60 Hz filter capacitor , a 50-Hz filter capacitor , and a DSP controller . The most salient feature is that the proposed TPTMC outputs two sinusoidal AC voltages with different frequencies using only a power converter .
While the proposed architecture achieves state - of - the - art results on several machine translation tasks , it requires a large number of parameters and training iterations to converge . We propose Weighted Transformer , a Transformer with modified attention layers , that not only outperforms the baseline network in BLEU score but also converges faster . Specifically , we replace the multi - head attention by multiple self - attention branches that the model learns to combine during the training process .
ahmed2017branchconnect analyze learned branching , through gates , in the context of computer vision while in gastaldi2017shake , the author analyzes a two - branch model with randomly sampled weights in the context of image classification . subsection : Transformer Network The original Transformer network uses an encoder - decoder architecture with each layer consisting of a novel attention mechanism , which the authors call multi - head attention , followed by a feed - forward network .
See Table [ reference ] . While the Transformer gets close but does not exceed the state of the art on the SST dataset , it exceeds both the mLSTM and ELMo baseline as well as both Watson and Google Sentiment APIs on the company tweets . This is despite optimally calibrating the API results on the test set .
subsection : Machine Translation On the WMT 2014 English - to - German translation task , the big transformer model ( Transformer ( big ) in Table [ reference ] ) outperforms the best previously reported models ( including ensembles ) by more than BLEU , establishing a new state - of - the - art BLEU score of . The configuration of this model is listed in the bottom line of Table [ reference ] .
We adopt similar factorizations of the joint pixel distribution as previous work . Following recent work on modeling text , however , we propose eschewing recurrent and convolutional networks in favor of the Image Transformer , a model based entirely on a self - attention mechanism . The specific , locally restricted form of multi - head self - attention we propose can be interpreted as a sparsely parameterized form of gated convolution .
We compare our architecture with the previous results in Table [ reference ] . Under the model size constraint , the 12 - layer Transformer - XL achieves a new SoTA result , outperforming the 12 - layer vanilla Transformer from by 0.05 , while both Transformer variants have a large margin over conventional RNN - based models . Notably , our 12 - layer architecture achieves the same result as the 64 - layer network from , using only 17 % of the parameter budget .
The second type of generation we consider in this paper is deformation . In this case , we propose Adversarial Spatial Transformer Network ( ASTN ) which learns how to rotate “ parts ” of the objects and make them hard to recognize by the detector . By competing against these networks and overcoming the obstacles , the FRCN learns to handle object occlusions and deformations in a robust manner .
paragraph : Transformer Encoder ( ) : We use a multi - layer bidirectional Transformer encoder vaswani2017attention to map the input representation vectors ( ) into a sequence of contextual embedding vectors . This is the shared representation across different tasks .
Without the constraint of an autoregressive factorization of the output distribution , we no longer need to prevent earlier decoding steps from accessing information from later steps . Thus we can avoid the causal mask used in the self - attention module of the conventional Transformer ’s decoder . Instead , we mask out each query position only from attending to itself , which we found to improve decoder performance relative to unmasked self - attention .
Our re - ranking strategy selects a headline that contains source - side words the most . Table [ reference ] shows that Transformer + + with this re - ranking ( + Re - ranking ) achieved better scores than the state - of - the - art . subsection : Analysis of Output Length
Gaussian process tomography ( GPT ) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak , given some model for the underlying physical processes involved . GPT can also be used , thanks to Bayesian formalism , to perform model selection -- i.e. , comparing different models and choosing the one with maximum evidence .
Summary Geophysical survey datasets used for resource exploration and detection are large in volume , dense in time and space , and have many dimensions . We present the Geophysics Processing Toolkit ( GPT ) , an application for processing geophysical survey data prior to interpretation and inversion . Initially developed as a processing toolkit for airborne electromagnetic ( AEM ) data , our application can be extended to beyond electromagnetics processing and inversion to incorporate multiple geophysical datasets such as gravity and magnetics .
Generalized perturbation theory ( GPT ) has been widely applied to sensitivity analysis of engineering systems models for decades . It is proven to be an efficient tool to calculate sensitivities in various applications such as uncertainty quantification , adaptive simulation and design optimization [ 1 - 3 ] .
This new construct allows us understand the i ° generalityi± nature of GPTs in a more comprehensive way . Moreover , we suggest that since firms are not only the i ° usersi± but i ° innovatorsi± of specific technologies , the traditional GPT classification , which is primarily based on patent citations is problematic . We then created a new mechanism to classify the GPTs .
GPT public key cryptosystem was proposed by Gabidulin , Paramonov and Tretjakov in 1991 . This cryptosystem is based on rank error correcting codes .
This study explores group package tour ( GPT ) itineraries based on comparative risk methodology using the fuzzy Preference Ranking Organization Method for Enrichment Evaluation ( PROMETHEE ) . It combines the concepts of fuzzy sets to represent the uncertain information in intrinsic risks with PROMETHEE , a subgroup of Multi-Criteria Decision Making methods .
The proposed Deconstruct and Preserve for all ( DaP∀ ) aims at mitigating the risk involved in exposing any data to these procedures and ensures that third parties get an exact match ; the process works on SSDs , GPT formatted devices , and other traditional formats , e.g. HDD . The results show a GPT TRIM enabled SSD imaged multiple times produces verification of matched hashes . With these results , it is proposed that DaP∀ should be considered as a Standard Operating Procedure ( SOP ) when completing data acquisition .
The General Particle Tracer ( GPT ) code has been extended with a multi-dimensional optimizer and solver to automate the final stages of a design process . The new solver can be used for all GPT simulations , including 3D space-charge and particle-wave interaction . The internal algorithms and two examples are presented in this paper .
The tests were carried out based on complete test procedures with test bases located on the premises of AGH University and city meadow in Krakow . The following instruments were tested : Trimble S6 , Leica TCRA 1101 and Topcon GPT 7500 .
With a trained BP neural network modular , the optimized GPT can be automatically generated according to the specifications of a planning problem . Compared with previous methods of manual and empirical tuning of GPT for individual planning problems , the proposed approach is self-adaptive . Numerical experiments verified the performance of the proposed approach and furthermore showed that planning with BP-generated GPTs is more stable .
A recent survey is included in gao2018neural . Some of the most prominent examples are ELMo elmo2018 , GPT gpt2018 and BERT bert2018 . These are neural network language models trained on text data using unsupervised objectives .
For the self - attention layer , we adopt the multi - head attention mechanism defined in VaswaniSPUJGKP17 . The feed - forward network consists of two linear transformations with a GELU HendrycksG16 activation in between , following OpenAI GPT RadfordNSS18 . Each sub - layer is placed inside a residual block HeZRS16 .
document : Passage Re - ranking with BERT Recently , neural models pretrained on a language modeling task , such as ELMo peters2017semi , OpenAI GPT radford2018improving , and BERT devlin2018bert , have achieved impressive results on various natural language processing tasks such as question - answering and natural language inference . In this paper , we describe a simple re - implementation of BERT for query - based passage re - ranking .
This has been the subject of substantial interest recently , where multiple approximations were proposed to reduce the quadratic memory requirement using sparse attention matrices . In this work , we propose to augment sparse Transformer blocks with a dense attention-based $ \textit{global memory}$ of length $ M$ ( $ \ll L$ ) which provides an aggregate global view of the entire input sequence to each position . Our augmentation has a manageable $ O(M\cdot(L+M))$ memory overhead , and can be seamlessly integrated with prior sparse solutions .
Self-attention is able to model long-term dependencies , but it may suffer from the extraction of irrelevant information in the context . To tackle the problem , we propose a novel model called \textbf{Explicit Sparse Transformer}. Explicit Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments . Extensive experimental results on a series of natural language processing and computer vision tasks , including neural machine translation , image captioning , and language modeling , all demonstrate the advantages of Explicit Sparse Transformer in model performance .
In order to solve these two issues , we first propose convolutional self attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism . Then , we propose LogSparse Transformer with only $ O(L(\log L)^{2})$ memory cost , improving the time series forecasting in finer granularity under constrained memory budget . Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art .
Abstract Power transformers are designed to supply linear loads at nominal frequency ; however , the development of power electronic devices and controllable loads that is generally called non-linear loads is promotion . Non-linear loads lead to temperature rise of transformers significantly and causes decrease their lifespan dramatically .
This paper gives an overview of the process of designing partial core transformers using a reverse modelling technique . Modifications are made to full-core equivalent circuit components to accommodate partial core transformers .
In this work , a variable voltage over fixed cable and MCCB switch contact resistance test method is used to verify ratings . This method uses two variable transformers driven by two separate stepper motors controlled by a microcontroller with a final step-down toroid transformer . To measure the circuit breaker , trip current level an accurate Hall-Effect current sensor was used and the trip time is determined by a microcontroller-based peripheral timer .
The invention relates to a high-power waveform generator , which comprises two oil-immersed isolation transformers . Secondary windings of the oil-immersed isolation transformers are respectively connected to a dry-typed transformer ; each dry-typed transformer has 26 secondary windings which are respectively connected to 26 power modules ; 4 dry-typed transformers can be connected with 104 power modules respectively . The 104 power modules are connected with each other from head to tail and are output in series to obtain the highest output value of 145kV/100A .
How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network ? We propose Equivariant Transformers ( ETs ) , a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups . Through the use of specially-derived canonical coordinate systems , ETs incorporate functions that are equivariant by construction with respect to these transformations .
Electrical transformers spanning the integument ( skin or mucosa ) have long been attractive means of circumventing this risk . Yet all existing skin surface transformers leak substantial magnetic flux , with an intrinsic risk of battery draining cross-coupling by any nearby conductor , requiring strict control of surroundings . In a progression of designs , we have used the walls of a small pouch , surgically formed from ileum , rather than skin , as a barrier .
By making the spiral of the primary winding wider and that of the secondary narrower , the ohmic loss of the primary winding and the spiral-substrate loss of the secondary winding of the transformer are minimized simultaneously . The silicon consumption of the step-up transformer impedance transformation network is also minimized by employing the stack configuration of transformers with the secondary winding implemented using an upper metal layer and the primary winding implemented using a lower metal layer . Two RF power harvesters , one with the proposed step-up transformer impedance transformation network and a modified Nakamoto rectifier and the other with a resonating LC impedance transformation network and the same rectifier , are designed in [ email protected ] 1.8V CMOS technology .
This paper describes a general technique for reducing oscillator phase noise in < italic > LC</italic > voltage-controlled oscillators ( VCOs ) . Phase noise is reduced using a circuit topology that decouples the < italic > LC</italic > tank from the active devices using capacitive transformers , thereby achieving a higher amplitude of oscillation not limited by the transistor breakdown voltage . Moreover , the proposed VCO topology reduces active device noise injection into the tank by further improving phase noise .
The proposed methodology and its performance is demonstrated on two test systems including ( a ) nonlinear R-L electric circuit , and ( b ) power electronic circuit ( switching system ) . The methodology is very useful for systems with power electronics and nonlinear devices such as saturable transformers/reactors and surge arresters .
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